hexagon: MUL_MAT, MUL_MAT_ID, FLASH_ATTN and GDN cleanup and optimizations for latest models (#23989 )

* hex-mm: initial support for F32 * F32 -> F32 matmuls * hex-rms-norm: fix src1 stride use in fused rms_norm_mul * hex-ops: clear spad pointers in the ops that clober it This fixes an odd case where fused rms-norm-mul was failing but only in qwen3.5-2B and only at searth op-bath sizes. * hmx-mm: add support for F32 * F32 -> F32 matmul_2d on HMX Decided to use Q4_0 * F32 -> F32 matmul for this. Q4_0 gets dequantized and tiled into F16, and here we quantize and tile F32 into F16. Super simple and pretty efficient. * hmx-mm: route f16 2D matmuls through the same kernel used for all other types * hmx-mm: re-introduce pipelined vs non-pipelined mode that we used to have but is much more generic way This update futher improves matmul performance and at the same time removes most of the redudant logic we had in different paths. * hmx-fa: slighlty improved pipeline simimar to matmul updates * hmx-mm: initial version of MAT_MUL_ID support for HMX * hmx-mm: fixed mxfp4 handling for MUL_MAT_ID * hex-gdn: optimize GATED_DELTA_NET DMA prefetch/double-buff, vectorize everything with HVX, in other words -- the usual :) * hmx-mm: missed one more case where we can use fastmod * hexagon: update DCVS settings for a slight perf bump * hmx-fa: use fastdiv in hmx-flash-attn * hmx-fa: precompute slope values to avoid disrupting the inner loop * hvx-utils/fa: new HVX helpers for powf and logf and using those to speed up FA alibi * hex-ops: fixed a bug in fusion logic that was messing up the order of the src tensors when some srcs are empty * hex-fa: correctly fallback to HVX if we have sinks or the dims are not quite right
hexagon: add gelu_quick (#24007 )
2026-11-02 17:07:39 -06:00 · 2026-06-01 23:40:08 -07:00 · 2026-06-01 23:19:07 -07:00 · 2026-06-02 07:26:20 +02:00 · 2026-06-02 10:38:37 +08:00 · 2026-06-01 19:15:09 -07:00
256 changed files with 17970 additions and 4955 deletions
@@ -3,6 +3,7 @@
  glibc,
  config,
  stdenv,
+  stdenvNoCC,
  runCommand,
  cmake,
  ninja,
@@ -19,6 +20,8 @@
  openssl,
  shaderc,
  spirv-headers,
+  nodejs,
+  importNpmLock,
  useBlas ?
    builtins.all (x: !x) [
      useCuda
@@ -130,7 +133,31 @@ effectiveStdenv.mkDerivation (finalAttrs: {
    src = lib.cleanSource ../../.;
  };

-  postPatch = ''
+  # Builds the webui locally, taking care not to require updating any sha256 hash.
+  webui = stdenvNoCC.mkDerivation {
+    pname = "webui";
+    version = llamaVersion;
+    src = lib.cleanSource ../../tools/ui;
+
+    nativeBuildInputs = [
+      nodejs
+      importNpmLock.linkNodeModulesHook
+    ];
+
+    # no sha256 required when using buildNodeModules
+    npmDeps = importNpmLock.buildNodeModules {
+      npmRoot = ../../tools/ui;
+      inherit nodejs;
+    };
+
+    installPhase = ''
+      LLAMA_UI_OUT_DIR=$out npm run build --offline
+    '';
+  };
+
+  postPatch = lib.optionalString useWebUi ''
+    cp -r ${finalAttrs.webui} tools/ui/dist
+    chmod -R u+w tools/ui/dist
  '';

  # With PR#6015 https://github.com/ggml-org/llama.cpp/pull/6015,
@@ -0,0 +1,101 @@
+ARG UBUNTU_VERSION=24.04
+ARG BUILD_DATE=N/A
+ARG APP_VERSION=N/A
+ARG APP_REVISION=N/A
+
+FROM ubuntu:$UBUNTU_VERSION AS build
+
+RUN apt-get update && \
+    apt-get install -y gcc-13 g++-13 build-essential git cmake libssl-dev libomp-dev libnuma-dev python3 ca-certificates
+
+ENV CC=gcc-13 CXX=g++-13
+
+WORKDIR /app
+
+COPY . .
+
+RUN cmake -S . -B build -DCMAKE_BUILD_TYPE=Release -DGGML_NATIVE=OFF -DLLAMA_BUILD_TESTS=OFF -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON -DGGML_ZENDNN=ON && \
+    cmake --build build -j $(nproc)
+
+RUN mkdir -p /app/lib && \
+    find build -name "*.so*" -exec cp -P {} /app/lib \;
+
+RUN mkdir -p /app/full \
+    && cp build/bin/* /app/full \
+    && cp *.py /app/full \
+    && cp -r conversion /app/full \
+    && cp -r gguf-py /app/full \
+    && cp -r requirements /app/full \
+    && cp requirements.txt /app/full \
+    && cp .devops/tools.sh /app/full/tools.sh
+
+## Base image
+FROM ubuntu:$UBUNTU_VERSION AS base
+
+ARG BUILD_DATE=N/A
+ARG APP_VERSION=N/A
+ARG APP_REVISION=N/A
+ARG IMAGE_URL=https://github.com/ggml-org/llama.cpp
+ARG IMAGE_SOURCE=https://github.com/ggml-org/llama.cpp
+LABEL org.opencontainers.image.created=$BUILD_DATE \
+      org.opencontainers.image.version=$APP_VERSION \
+      org.opencontainers.image.revision=$APP_REVISION \
+      org.opencontainers.image.title="llama.cpp" \
+      org.opencontainers.image.description="LLM inference in C/C++" \
+      org.opencontainers.image.url=$IMAGE_URL \
+      org.opencontainers.image.source=$IMAGE_SOURCE
+
+RUN apt-get update \
+    && apt-get install -y libgomp1 libnuma1 curl \
+    && apt autoremove -y \
+    && apt clean -y \
+    && rm -rf /tmp/* /var/tmp/* \
+    && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \
+    && find /var/cache -type f -delete
+
+COPY --from=build /app/lib/ /app
+
+### Full
+FROM base AS full
+
+COPY --from=build /app/full /app
+
+WORKDIR /app
+
+RUN apt-get update \
+    && apt-get install -y \
+    git \
+    python3 \
+    python3-pip \
+    python3-wheel \
+    && pip install --break-system-packages --upgrade setuptools \
+    && pip install --break-system-packages -r requirements.txt \
+    && apt autoremove -y \
+    && apt clean -y \
+    && rm -rf /tmp/* /var/tmp/* \
+    && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \
+    && find /var/cache -type f -delete
+
+ENTRYPOINT ["/app/tools.sh"]
+
+### Light, CLI only
+FROM base AS light
+
+COPY --from=build /app/full/llama-cli /app/full/llama-completion /app
+
+WORKDIR /app
+
+ENTRYPOINT [ "/app/llama-cli" ]
+
+### Server, Server only
+FROM base AS server
+
+ENV LLAMA_ARG_HOST=0.0.0.0
+
+COPY --from=build /app/full/llama-server /app
+
+WORKDIR /app
+
+HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ]
+
+ENTRYPOINT [ "/app/llama-server" ]
@@ -0,0 +1,22 @@
+name: "ccache-clear"
+description: "Delete all GitHub Actions caches matching a key prefix"
+inputs:
+  key:
+    description: "Cache key prefix to match and delete"
+    required: true
+
+runs:
+  using: "composite"
+  steps:
+    - name: Clear caches
+      shell: bash
+      run: |
+        CACHES=$(gh cache list --key "ccache-${{ inputs.key }}" --json id,key --jq '.[] | "\(.id) \(.key)"' 2>/dev/null)
+        if [ -z "$CACHES" ]; then
+          echo "No caches found with key prefix: ${{ inputs.key }}"
+          exit 0
+        fi
+        while read -r id key; do
+          echo "Deleting cache: $id ($key)"
+          gh cache delete "$id"
+        done <<< "$CACHES"
@@ -32,7 +32,7 @@ env:
  LLAMA_ARG_LOG_TIMESTAMPS: 1

 jobs:
-  android:
+  default:
    runs-on: ubuntu-latest

    steps:
@@ -58,7 +58,7 @@ jobs:
          cd examples/llama.android
          ./gradlew build --no-daemon

-  android-ndk:
+  ndk:
    runs-on: ubuntu-latest
    container:
      image: 'ghcr.io/snapdragon-toolchain/arm64-android:v0.3'
@@ -92,7 +92,7 @@ jobs:
          name: llama-cpp-android-arm64-cpu
          path: pkg-adb/llama.cpp

-  android-arm64:
+  arm64:
    runs-on: ubuntu-latest

    env:
@@ -103,12 +103,18 @@ jobs:
        id: checkout
        uses: actions/checkout@v6

-      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.21
-        with:
-          key: android-arm64
-          evict-old-files: 1d
-          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
+      # note : disabled to spare some cache space (https://github.com/ggml-org/llama.cpp/pull/23789)
+      #        for some reason, the ccache does not improve the build time in this case
+      # example:
+      #   cache off: https://github.com/ggerganov/tmp2/actions/runs/26534713799/job/78160400831
+      #   cache on:  https://github.com/ggerganov/tmp2/actions/runs/26534713799/job/78224189394
+      #
+      #- name: ccache
+      #  uses: ggml-org/ccache-action@v1.2.21
+      #  with:
+      #    key: android-ubuntu-arm64
+      #    evict-old-files: 1d
+      #    save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}

      - name: Set up JDK
        uses: actions/setup-java@v5
@@ -48,7 +48,7 @@ jobs:
      - name: ccache
        uses: ggml-org/ccache-action@v1.2.21
        with:
-          key: macos-latest-arm64
+          key: apple-arm64
          evict-old-files: 1d
          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}

@@ -84,7 +84,7 @@ jobs:
      - name: ccache
        uses: ggml-org/ccache-action@v1.2.21
        with:
-          key: macos-latest-x64
+          key: apple-x64
          evict-old-files: 1d
          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}

@@ -109,39 +109,6 @@ jobs:
          cd build
          ctest -L main --verbose --timeout 900

-  macos-latest-ios:
-    runs-on: macos-latest
-
-    steps:
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v6
-
-      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.21
-        with:
-          key: macos-latest-ios
-          evict-old-files: 1d
-          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
-
-      - name: Build
-        id: cmake_build
-        run: |
-          sysctl -a
-          cmake -B build -G Xcode \
-            -DGGML_METAL_USE_BF16=ON \
-            -DGGML_METAL_EMBED_LIBRARY=ON \
-            -DLLAMA_BUILD_APP=OFF \
-            -DLLAMA_BUILD_COMMON=OFF \
-            -DLLAMA_BUILD_EXAMPLES=OFF \
-            -DLLAMA_BUILD_TOOLS=OFF \
-            -DLLAMA_BUILD_TESTS=OFF \
-            -DLLAMA_BUILD_SERVER=OFF \
-            -DCMAKE_SYSTEM_NAME=iOS \
-            -DCMAKE_OSX_DEPLOYMENT_TARGET=14.0 \
-            -DCMAKE_XCODE_ATTRIBUTE_DEVELOPMENT_TEAM=ggml
-          cmake --build build --config Release -j $(sysctl -n hw.logicalcpu) -- CODE_SIGNING_ALLOWED=NO
-
  macos-latest-ios-xcode:
    runs-on: macos-latest

@@ -197,10 +164,11 @@ jobs:
        id: checkout
        uses: actions/checkout@v6

+      # TODO: this likely does not do anything - if yes, remove it
      - name: ccache
        uses: ggml-org/ccache-action@v1.2.21
        with:
-          key: macos-latest-tvos
+          key: apple-tvos
          evict-old-files: 1d
          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}

@@ -230,6 +198,14 @@ jobs:
        id: checkout
        uses: actions/checkout@v6

+      # TODO: this likely does not do anything - if yes, remove it
+      - name: ccache
+        uses: ggml-org/ccache-action@v1.2.21
+        with:
+          key: apple-visionos
+          evict-old-files: 1d
+          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
+
      - name: Build
        id: cmake_build
        run: |
@@ -261,10 +237,11 @@ jobs:
        id: checkout
        uses: actions/checkout@v6

+      # TODO: this likely does not do anything - if yes, remove it
      - name: ccache
        uses: ggml-org/ccache-action@v1.2.21
        with:
-          key: macos-latest-swift
+          key: apple-swift
          evict-old-files: 1d
          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}

@@ -1,4 +1,4 @@
-name: CI
+name: CI (cpu)

 on:
  workflow_dispatch: # allows manual triggering
@@ -6,7 +6,7 @@ on:
    branches:
      - master
    paths: [
-      '.github/workflows/build.yml',
+      '.github/workflows/build-cpu.yml',
      '.github/workflows/build-cmake-pkg.yml',
      '**/CMakeLists.txt',
      '**/.cmake',
@@ -14,35 +14,19 @@ on:
      '**/*.hpp',
      '**/*.c',
      '**/*.cpp',
-      '**/*.cu',
-      '**/*.cuh',
-      '**/*.swift',
-      '**/*.m',
-      '**/*.metal',
-      '**/*.comp',
-      '**/*.glsl',
-      '**/*.wgsl'
    ]

  pull_request:
    types: [opened, synchronize, reopened]
    paths: [
-      '.github/workflows/build.yml',
+      '.github/workflows/build-cpu.yml',
      '.github/workflows/build-cmake-pkg.yml',
      '**/CMakeLists.txt',
      '**/.cmake',
      '**/*.h',
      '**/*.hpp',
      '**/*.c',
-      '**/*.cpp',
-      '**/*.cu',
-      '**/*.cuh',
-      '**/*.swift',
-      '**/*.m',
-      '**/*.metal',
-      '**/*.comp',
-      '**/*.glsl',
-      '**/*.wgsl'
+      '**/*.cpp'
    ]

 concurrency:
@@ -60,7 +44,7 @@ jobs:
  build-cmake-pkg:
    uses: ./.github/workflows/build-cmake-pkg.yml

-  ubuntu-cpu:
+  ubuntu:
    strategy:
      matrix:
        include:
@@ -79,7 +63,7 @@ jobs:
      - name: ccache
        uses: ggml-org/ccache-action@v1.2.21
        with:
-          key: ubuntu-cpu-${{ matrix.build }}
+          key: cpu-${{ matrix.os }}
          evict-old-files: 1d
          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}

@@ -131,46 +115,7 @@ jobs:
          ./bin/llama-convert-llama2c-to-ggml --copy-vocab-from-model ./tok512.bin --llama2c-model stories260K.bin --llama2c-output-model stories260K.gguf
          ./bin/llama-completion -m stories260K.gguf -p "One day, Lily met a Shoggoth" -n 500 -c 256

-  ubuntu-24-vulkan:
-    strategy:
-      matrix:
-        include:
-          - build: 'x64'
-            os: ubuntu-24.04
-          - build: 'arm64'
-            os: ubuntu-24.04-arm
-
-    runs-on: ${{ matrix.os }}
-
-    steps:
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v6
-
-      - name: Dependencies
-        id: depends
-        run: |
-          sudo apt-get update
-          sudo apt-get install -y gcc-14 g++-14 build-essential glslc libvulkan-dev spirv-headers libssl-dev ninja-build
-          echo "CC=gcc-14" >> "$GITHUB_ENV"
-          echo "CXX=g++-14" >> "$GITHUB_ENV"
-
-      - name: Configure
-        id: cmake_configure
-        run: |
-          cmake -B build \
-            -G "Ninja" \
-            -DCMAKE_BUILD_TYPE=RelWithDebInfo \
-            -DGGML_BACKEND_DL=ON \
-            -DGGML_CPU_ALL_VARIANTS=ON \
-            -DGGML_VULKAN=ON
-
-      - name: Build
-        id: cmake_build
-        run: |
-          time cmake --build build -j $(nproc)
-
-  windows-latest:
+  windows:
    runs-on: windows-2025

    env:
@@ -202,7 +147,7 @@ jobs:
      - name: ccache
        uses: ggml-org/ccache-action@v1.2.21
        with:
-          key: windows-latest-${{ matrix.build }}
+          key: cpu-windows-2025-${{ matrix.build }}
          variant: ccache
          evict-old-files: 1d
          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
@@ -268,88 +213,3 @@ jobs:
      #     cd build
      #     $env:LLAMA_SKIP_TESTS_SLOW_ON_EMULATOR = 1
      #     & $sde -future -- ctest -L main -C Release --verbose --timeout 900
-
-  ubuntu-latest-cuda:
-    runs-on: ubuntu-latest
-    container: nvidia/cuda:12.6.2-devel-ubuntu24.04
-
-    steps:
-        - name: Clone
-          id: checkout
-          uses: actions/checkout@v6
-
-        - name: Install dependencies
-          env:
-            DEBIAN_FRONTEND: noninteractive
-          run: |
-              apt update
-              apt install -y cmake build-essential ninja-build libgomp1 git libssl-dev
-
-        - name: ccache
-          uses: ggml-org/ccache-action@v1.2.21
-          with:
-            key: ubuntu-latest-cuda
-            evict-old-files: 1d
-            save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
-
-        - name: Build with CMake
-          # TODO: Remove GGML_CUDA_CUB_3DOT2 flag once CCCL 3.2 is bundled within CTK and that CTK version is used in this project
-          run: |
-            cmake -S . -B build -G Ninja \
-              -DLLAMA_FATAL_WARNINGS=ON \
-              -DCMAKE_BUILD_TYPE=Release \
-              -DCMAKE_CUDA_ARCHITECTURES=89-real \
-              -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined \
-              -DGGML_NATIVE=OFF \
-              -DGGML_CUDA=ON \
-              -DGGML_CUDA_CUB_3DOT2=ON
-            cmake --build build
-
-  windows-2022-cuda:
-    runs-on: windows-2022
-
-    strategy:
-      matrix:
-        cuda: ['12.4']
-
-    steps:
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v6
-
-      - name: Install ccache
-        uses: ggml-org/ccache-action@v1.2.21
-        with:
-          key: windows-cuda-${{ matrix.cuda }}
-          variant: ccache
-          evict-old-files: 1d
-          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
-
-      - name: Install Cuda Toolkit
-        uses: ./.github/actions/windows-setup-cuda
-        with:
-          cuda_version: ${{ matrix.cuda }}
-
-      - name: Install Ninja
-        id: install_ninja
-        run: |
-          choco install ninja
-
-      - name: Build
-        id: cmake_build
-        shell: cmd
-        # TODO: Remove GGML_CUDA_CUB_3DOT2 flag once CCCL 3.2 is bundled within CTK and that CTK version is used in this project
-        run: |
-          call "C:\Program Files\Microsoft Visual Studio\2022\Enterprise\VC\Auxiliary\Build\vcvarsall.bat" x64
-          cmake -S . -B build -G "Ninja Multi-Config" ^
-            -DLLAMA_BUILD_SERVER=ON ^
-            -DLLAMA_BUILD_BORINGSSL=ON ^
-            -DGGML_NATIVE=OFF ^
-            -DGGML_BACKEND_DL=ON ^
-            -DGGML_CPU_ALL_VARIANTS=ON ^
-            -DGGML_CUDA=ON ^
-            -DGGML_RPC=ON ^
-            -DGGML_CUDA_CUB_3DOT2=ON
-          set /A NINJA_JOBS=%NUMBER_OF_PROCESSORS%-1
-          cmake --build build --config Release -j %NINJA_JOBS% -t ggml
-          cmake --build build --config Release
@@ -1,4 +1,4 @@
-name: CI (hip)
+name: CI (CUDA, ubuntu)

 on:
  workflow_dispatch: # allows manual triggering
@@ -6,7 +6,7 @@ on:
    branches:
      - master
    paths: [
-      '.github/workflows/build-hip.yml',
+      '.github/workflows/build-cuda-ubuntu.yml',
      '**/CMakeLists.txt',
      '**/.cmake',
      '**/*.h',
@@ -20,7 +20,7 @@ on:
  pull_request:
    types: [opened, synchronize, reopened]
    paths: [
-      '.github/workflows/build-hip.yml',
+      '.github/workflows/build-cuda-ubuntu.yml',
      'ggml/src/ggml-cuda/**'
    ]

@@ -36,8 +36,43 @@ env:
  LLAMA_ARG_LOG_TIMESTAMPS: 1

 jobs:
+  cuda:
+    runs-on: ubuntu-24.04
+    container: nvidia/cuda:12.6.2-devel-ubuntu24.04

-  ubuntu-22-hip:
+    steps:
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v6
+
+      - name: Install dependencies
+        env:
+          DEBIAN_FRONTEND: noninteractive
+        run: |
+          apt update
+          apt install -y cmake build-essential ninja-build libgomp1 git libssl-dev
+
+      - name: ccache
+        uses: ggml-org/ccache-action@v1.2.21
+        with:
+          key: cuda-ubuntu-24.04-cuda
+          evict-old-files: 1d
+          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
+
+      - name: Build with CMake
+        # TODO: Remove GGML_CUDA_CUB_3DOT2 flag once CCCL 3.2 is bundled within CTK and that CTK version is used in this project
+        run: |
+          cmake -S . -B build -G Ninja \
+            -DLLAMA_FATAL_WARNINGS=ON \
+            -DCMAKE_BUILD_TYPE=Release \
+            -DCMAKE_CUDA_ARCHITECTURES=89-real \
+            -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined \
+            -DGGML_NATIVE=OFF \
+            -DGGML_CUDA=ON \
+            -DGGML_CUDA_CUB_3DOT2=ON
+          cmake --build build
+
+  hip:
    runs-on: ubuntu-22.04
    container: rocm/dev-ubuntu-22.04:6.1.2

@@ -55,7 +90,7 @@ jobs:
      - name: ccache
        uses: ggml-org/ccache-action@v1.2.21
        with:
-          key: ubuntu-22-hip
+          key: cuda-ubuntu-22.04-hip
          evict-old-files: 1d
          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}

@@ -69,75 +104,7 @@ jobs:
            -DGGML_HIP=ON
          cmake --build build --config Release -j $(nproc)

-  windows-latest-hip:
-    runs-on: windows-2022
-
-    env:
-      # Make sure this is in sync with build-cache.yml
-      HIPSDK_INSTALLER_VERSION: "26.Q1"
-
-    steps:
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v6
-
-      - name: Grab rocWMMA package
-        id: grab_rocwmma
-        run: |
-          curl -o rocwmma.deb "https://repo.radeon.com/rocm/apt/7.2.1/pool/main/r/rocwmma-dev/rocwmma-dev_2.2.0.70201-81~24.04_amd64.deb"
-          7z x rocwmma.deb
-          7z x data.tar
-
-      - name: Use ROCm Installation Cache
-        uses: actions/cache@v5
-        id: cache-rocm
-        with:
-          path: C:\Program Files\AMD\ROCm
-          key: cache-gha-rocm-${{ env.HIPSDK_INSTALLER_VERSION }}-${{ runner.os }}
-
-      - name: Setup ROCm
-        if: steps.cache-rocm.outputs.cache-hit != 'true'
-        uses: ./.github/actions/windows-setup-rocm
-        with:
-          version: ${{ env.HIPSDK_INSTALLER_VERSION }}
-
-      - name: Verify ROCm
-        id: verify
-        run: |
-          # Find and test ROCm installation
-          $clangPath = Get-ChildItem 'C:\Program Files\AMD\ROCm\*\bin\clang.exe' | Select-Object -First 1
-          if (-not $clangPath) {
-            Write-Error "ROCm installation not found"
-            exit 1
-          }
-          & $clangPath.FullName --version
-
-      - name: Install ccache
-        uses: ggml-org/ccache-action@v1.2.21
-        with:
-          key: ${{ github.job }}
-          evict-old-files: 1d
-          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
-
-      - name: Build
-        id: cmake_build
-        run: |
-          $env:HIP_PATH=$(Resolve-Path 'C:\Program Files\AMD\ROCm\*\bin\clang.exe' | split-path | split-path)
-          $env:CMAKE_PREFIX_PATH="${env:HIP_PATH}"
-          cmake -G "Unix Makefiles" -B build -S . `
-            -DCMAKE_C_COMPILER="${env:HIP_PATH}\bin\clang.exe" `
-            -DCMAKE_CXX_COMPILER="${env:HIP_PATH}\bin\clang++.exe" `
-            -DCMAKE_CXX_FLAGS="-I$($PWD.Path.Replace('\', '/'))/opt/rocm-7.2.1/include/" `
-            -DCMAKE_BUILD_TYPE=Release `
-            -DLLAMA_BUILD_BORINGSSL=ON `
-            -DROCM_DIR="${env:HIP_PATH}" `
-            -DGGML_HIP=ON `
-            -DGGML_HIP_ROCWMMA_FATTN=ON `
-            -DGPU_TARGETS="gfx1100"  `
-            -DGGML_RPC=ON
-          cmake --build build -j ${env:NUMBER_OF_PROCESSORS}
-
-  ubuntu-22-musa:
+  musa:
    runs-on: ubuntu-22.04
    container: mthreads/musa:rc4.3.0-devel-ubuntu22.04-amd64

@@ -155,7 +122,7 @@ jobs:
      - name: ccache
        uses: ggml-org/ccache-action@v1.2.21
        with:
-          key: ubuntu-22-musa
+          key: cuda-ubuntu-22.04-musa
          evict-old-files: 1d
          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}

@@ -0,0 +1,162 @@
+name: CI (CUDA, windows)
+
+# TODO: this workflow is only triggered manually because it is very heavy on the CI
+#       when we provision dedicated windows runners, we can enable it for pushes too
+# note: running this workflow manually will populate the ccache for the release builds
+#       this can be used before merging a PR to speed up the release workflow
+on:
+  workflow_dispatch: # allows manual triggering
+
+# note: this will run in queue with the release workflow
+concurrency:
+  group: release
+  queue: max
+
+env:
+  GH_TOKEN: ${{ github.token }}
+  GGML_NLOOP: 3
+  GGML_N_THREADS: 1
+  LLAMA_ARG_LOG_COLORS: 1
+  LLAMA_ARG_LOG_PREFIX: 1
+  LLAMA_ARG_LOG_TIMESTAMPS: 1
+
+jobs:
+  cuda:
+    runs-on: windows-2022
+
+    permissions:
+      actions: write
+
+    strategy:
+      matrix:
+        cuda: ['12.4', '13.3']
+
+    steps:
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v6
+
+      - name: ccache
+        uses: ggml-org/ccache-action@v1.2.21
+        with:
+          key: release-windows-2022-x64-cuda-${{ matrix.cuda }}
+
+      - name: Install Cuda Toolkit
+        uses: ./.github/actions/windows-setup-cuda
+        with:
+          cuda_version: ${{ matrix.cuda }}
+
+      - name: Install Ninja
+        id: install_ninja
+        run: |
+          choco install ninja
+
+      - name: Build
+        id: cmake_build
+        shell: cmd
+        # TODO: Remove GGML_CUDA_CUB_3DOT2 flag once CCCL 3.2 is bundled within CTK and that CTK version is used in this project
+        run: |
+          call "C:\Program Files\Microsoft Visual Studio\2022\Enterprise\VC\Auxiliary\Build\vcvarsall.bat" x64
+          cmake -S . -B build -G "Ninja Multi-Config" ^
+            -DLLAMA_BUILD_SERVER=ON ^
+            -DLLAMA_BUILD_BORINGSSL=ON ^
+            -DGGML_NATIVE=OFF ^
+            -DGGML_BACKEND_DL=ON ^
+            -DGGML_CPU_ALL_VARIANTS=ON ^
+            -DGGML_CUDA=ON ^
+            -DGGML_RPC=ON ^
+            -DGGML_CUDA_CUB_3DOT2=ON
+          set /A NINJA_JOBS=%NUMBER_OF_PROCESSORS%-1
+          cmake --build build --config Release -j %NINJA_JOBS% -t ggml
+          cmake --build build --config Release
+
+      - name: ccache-clear
+        uses: ./.github/actions/ccache-clear
+        with:
+          key: release-windows-2022-x64-cuda-${{ matrix.cuda }}
+
+  hip:
+    runs-on: windows-2022
+
+    permissions:
+      actions: write
+
+    env:
+      # Make sure this is in sync with build-cache.yml
+      HIPSDK_INSTALLER_VERSION: "26.Q1"
+
+    strategy:
+      matrix:
+        include:
+          # sync with release.yml
+          - name: "radeon"
+            gpu_targets: "gfx1150;gfx1151;gfx1200;gfx1201;gfx1100;gfx1101;gfx1102;gfx1030;gfx1031;gfx1032"
+
+    steps:
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v6
+
+      - name: Grab rocWMMA package
+        id: grab_rocwmma
+        run: |
+          curl -o rocwmma.deb "https://repo.radeon.com/rocm/apt/7.2.1/pool/main/r/rocwmma-dev/rocwmma-dev_2.2.0.70201-81~24.04_amd64.deb"
+          7z x rocwmma.deb
+          7z x data.tar
+
+      - name: Use ROCm Installation Cache
+        uses: actions/cache@v5
+        id: cache-rocm
+        with:
+          path: C:\Program Files\AMD\ROCm
+          key: cache-gha-rocm-${{ env.HIPSDK_INSTALLER_VERSION }}-${{ runner.os }}
+
+      - name: Setup ROCm
+        if: steps.cache-rocm.outputs.cache-hit != 'true'
+        uses: ./.github/actions/windows-setup-rocm
+        with:
+          version: ${{ env.HIPSDK_INSTALLER_VERSION }}
+
+      - name: Verify ROCm
+        id: verify
+        run: |
+          # Find and test ROCm installation
+          $clangPath = Get-ChildItem 'C:\Program Files\AMD\ROCm\*\bin\clang.exe' | Select-Object -First 1
+          if (-not $clangPath) {
+            Write-Error "ROCm installation not found"
+            exit 1
+          }
+          & $clangPath.FullName --version
+
+      - name: ccache
+        uses: ggml-org/ccache-action@v1.2.21
+        with:
+          # TODO: this build does not match the build in release.yml, so we use a different cache key
+          #       ideally, the builds should match, similar to the CUDA build above so that we would be able
+          #       to populate the ccache for the release with manual runs of this workflow
+          #key: release-windows-2022-x64-hip-${{ env.HIPSDK_INSTALLER_VERSION }}-${{ matrix.name }}
+          key: cuda-windows-2022-x64-hip-${{ env.HIPSDK_INSTALLER_VERSION }}-${{ matrix.name }}
+
+      - name: Build
+        id: cmake_build
+        run: |
+          $env:HIP_PATH=$(Resolve-Path 'C:\Program Files\AMD\ROCm\*\bin\clang.exe' | split-path | split-path)
+          $env:CMAKE_PREFIX_PATH="${env:HIP_PATH}"
+          cmake -G "Unix Makefiles" -B build -S . `
+            -DCMAKE_C_COMPILER="${env:HIP_PATH}\bin\clang.exe" `
+            -DCMAKE_CXX_COMPILER="${env:HIP_PATH}\bin\clang++.exe" `
+            -DCMAKE_CXX_FLAGS="-I$($PWD.Path.Replace('\', '/'))/opt/rocm-7.2.1/include/" `
+            -DCMAKE_BUILD_TYPE=Release `
+            -DLLAMA_BUILD_BORINGSSL=ON `
+            -DROCM_DIR="${env:HIP_PATH}" `
+            -DGGML_HIP=ON `
+            -DGGML_HIP_ROCWMMA_FATTN=ON `
+            -DGPU_TARGETS="gfx1100"  `
+            -DGGML_RPC=ON
+          cmake --build build -j ${env:NUMBER_OF_PROCESSORS}
+
+      - name: ccache-clear
+        uses: ./.github/actions/ccache-clear
+        with:
+          #key: release-windows-2022-x64-hip-${{ env.HIPSDK_INSTALLER_VERSION }}-${{ matrix.name }}
+          key: cuda-windows-2022-x64-hip-${{ env.HIPSDK_INSTALLER_VERSION }}-${{ matrix.name }}
@@ -37,7 +37,7 @@ jobs:
      #- name: ccache
      #  uses: ggml-org/ccache-action@v1.2.16
      #  with:
-      #    key: windows-msys2
+      #    key: msys-windows-2025-x64
      #    variant: ccache
      #    evict-old-files: 1d
      #    save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
@@ -35,8 +35,7 @@ env:
  LLAMA_ARG_LOG_TIMESTAMPS: 1

 jobs:
-
-  windows-latest-opencl-adreno:
+  windows-2025-opencl-adreno:
    runs-on: windows-2025

    steps:
@@ -47,7 +46,7 @@ jobs:
      - name: ccache
        uses: ggml-org/ccache-action@v1.2.21
        with:
-          key: windows-latest-llvm-arm64-opencl-adreno
+          key: opencl-windows-2025-x64
          variant: ccache
          evict-old-files: 1d
          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
@@ -35,24 +35,12 @@ env:

 jobs:
  ubuntu-24-openvino:
-    name: ubuntu-24-openvino-${{ matrix.openvino_device }}
+    runs-on: [self-hosted, Linux, Intel, OpenVINO]

    concurrency:
-      group: openvino-${{ matrix.variant }}-${{ github.head_ref || github.ref }}
+      group: openvino-gpu-${{ github.head_ref || github.ref }}
      cancel-in-progress: false

-    strategy:
-      matrix:
-        include:
-          - variant: cpu
-            runner: '"ubuntu-24.04"'
-            openvino_device: "CPU"
-          - variant: gpu
-            runner: '["self-hosted","Linux","Intel","OpenVINO"]'
-            openvino_device: "GPU"
-
-    runs-on: ${{ fromJSON(matrix.runner) }}
-
    env:
      # Sync versions in build-openvino.yml, build-self-hosted.yml, release.yml, build-cache.yml, .devops/openvino.Dockerfile
      OPENVINO_VERSION_MAJOR: "2026.0"
@@ -63,14 +51,6 @@ jobs:
        id: checkout
        uses: actions/checkout@v6

-      - name: ccache
-        if: runner.environment == 'github-hosted'
-        uses: ggml-org/ccache-action@v1.2.21
-        with:
-          key: ubuntu-24-openvino-${{ matrix.variant }}-no-preset-v1
-          evict-old-files: 1d
-          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
-
      - name: Dependencies
        id: depends
        run: |
@@ -78,16 +58,7 @@ jobs:
          sudo apt-get install -y build-essential libssl-dev libtbb12 cmake ninja-build python3-pip
          sudo apt-get install -y ocl-icd-opencl-dev opencl-headers opencl-clhpp-headers intel-opencl-icd

-      - name: Use OpenVINO Toolkit Cache
-        if: runner.environment == 'github-hosted'
-        uses: actions/cache@v5
-        id: cache-openvino
-        with:
-          path: ./openvino_toolkit
-          key: cache-gha-openvino-toolkit-v${{ env.OPENVINO_VERSION_FULL }}-${{ runner.os }}
-
      - name: Setup OpenVINO Toolkit
-        if: steps.cache-openvino.outputs.cache-hit != 'true'
        uses: ./.github/actions/linux-setup-openvino
        with:
          path: ./openvino_toolkit
@@ -109,12 +80,17 @@ jobs:
            -DGGML_OPENVINO=ON
          time cmake --build build/ReleaseOV --config Release -j $(nproc)

-      - name: Test
-        id: cmake_test
+      - name: Test (CPU)
+        id: cmake_test_cpu
        # TODO: fix and re-enable the `test-llama-archs` test below
        run: |
          cd ${{ github.workspace }}
-          if [ "${{ matrix.openvino_device }}" = "GPU" ]; then
-            export GGML_OPENVINO_DEVICE=GPU
-          fi
+          ctest --test-dir build/ReleaseOV -L main -E "test-llama-archs" --verbose --timeout 2000
+
+      - name: Test (GPU)
+        id: cmake_test_gpu
+        # TODO: fix and re-enable the `test-llama-archs` test below
+        run: |
+          cd ${{ github.workspace }}
+          export GGML_OPENVINO_DEVICE=GPU
          ctest --test-dir build/ReleaseOV -L main -E "test-llama-archs" --verbose --timeout 2000
@@ -69,7 +69,7 @@ jobs:
      #- name: ccache
      #  uses: ggml-org/ccache-action@afde29e5b5422e5da23cb1f639e8baecadeadfc3 # https://github.com/ggml-org/ccache-action/pull/1
      #  with:
-      #    key: ubuntu-cpu-riscv64-native
+      #    key: riscv-ubuntu-native
      #    evict-old-files: 1d
      #    save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}

@@ -139,7 +139,7 @@ jobs:
      #- name: ccache
      #  uses: ggml-org/ccache-action@afde29e5b5422e5da23cb1f639e8baecadeadfc3 # https://github.com/ggml-org/ccache-action/pull/1
      #  with:
-      #    key: ubuntu-riscv64-native-sanitizer-${{ matrix.sanitizer }}-${{ matrix.build_type }}
+      #    key: riscv-ubuntu-native-sanitizer-${{ matrix.sanitizer }}-${{ matrix.build_type }}
      #    evict-old-files: 1d
      #    save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}

@@ -34,9 +34,8 @@ env:
  LLAMA_ARG_LOG_TIMESTAMPS: 1

 jobs:
-
-  ubuntu-latest-rpc:
-    runs-on: ubuntu-latest
+  ubuntu-24-rpc:
+    runs-on: ${{ 'ubuntu-24.04-arm' || 'ubuntu-24.04' }}

    continue-on-error: true

@@ -41,19 +41,6 @@ jobs:
        id: checkout
        uses: actions/checkout@v6

-      #- name: ccache
-      #  uses: ggml-org/ccache-action@v1.2.21
-      #  with:
-      #    key: ubuntu-latest-sanitizer-${{ matrix.sanitizer }}
-      #    evict-old-files: 1d
-      #    save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
-
-      #- name: Dependencies
-      #  id: depends
-      #  run: |
-      #    sudo apt-get update
-      #    sudo apt-get install build-essential libssl-dev
-
      # with UNDEFINED sanitizer, we have to build in Debug to avoid GCC 13 false-positive warnings
      - name: Build (undefined)
        id: cmake_build_undefined
@@ -210,7 +210,7 @@ jobs:
          GG_BUILD_WEBGPU=1 GG_BUILD_WEBGPU_DAWN_PREFIX="$GITHUB_WORKSPACE/dawn" \
            bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp

-  gpu-vulkan:
+  gpu-vulkan-apple:
    runs-on: [self-hosted, macOS, ARM64]

    steps:
@@ -261,7 +261,7 @@ jobs:
          # a valid python environment for testing
          LLAMA_FATAL_WARNINGS=OFF GG_BUILD_NINJA=1 GG_BUILD_VULKAN=1 GG_BUILD_LOW_PERF=1 ./ci/run.sh ./results/llama.cpp ./mnt/llama.cpp

-  cpu-openvino-low-perf:
+  gpu-openvino-low-perf:
    runs-on: [self-hosted, Linux, Intel, OpenVINO]

    concurrency:
@@ -297,8 +297,8 @@ jobs:
          source ./openvino_toolkit/setupvars.sh
          GG_BUILD_OPENVINO=1 GGML_OPENVINO_DEVICE=GPU GG_BUILD_LOW_PERF=1 bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp

-  cpu-any-low-perf:
-    runs-on: [self-hosted, CPU]
+  cpu-x64-high-perf:
+    runs-on: [self-hosted, Linux, X64]

    steps:
      - name: Clone
@@ -308,22 +308,9 @@ jobs:
      - name: Test
        id: ggml-ci
        run: |
-          LLAMA_ARG_THREADS=$(nproc) GG_BUILD_LOW_PERF=1 bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp
+          LLAMA_ARG_THREADS=$(nproc) GG_BUILD_HIGH_PERF=1 GG_BUILD_EXTRA_TESTS_0=1 bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp

-  cpu-any-high-perf:
-    runs-on: [self-hosted, CPU]
-
-    steps:
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v6
-
-      - name: Test
-        id: ggml-ci
-        run: |
-          LLAMA_ARG_THREADS=$(nproc) GG_BUILD_HIGH_PERF=1 GG_BUILD_NO_SVE=1 GG_BUILD_NO_BF16=1 GG_BUILD_EXTRA_TESTS_0=1 bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp
-
-  cpu-arm64-graviton4:
+  cpu-arm64-high-perf-graviton4:
    runs-on: ah-ubuntu_22_04-c8g_8x

    steps:
@@ -360,7 +347,7 @@ jobs:
      - name: Test
        id: ggml-ci
        run: |
-          LLAMA_ARG_THREADS=$(nproc) GG_BUILD_NO_BF16=1 GG_BUILD_EXTRA_TESTS_0=1 bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp
+          LLAMA_ARG_THREADS=$(nproc) GG_BUILD_HIGH_PERF=1 GG_BUILD_NO_BF16=1 GG_BUILD_EXTRA_TESTS_0=1 bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp

  cpu-arm64-graviton4-kleidiai:
    runs-on: ah-ubuntu_22_04-c8g_8x
@@ -396,14 +383,6 @@ jobs:
          sudo apt-get update
          sudo apt-get install -y cmake

-      # note: sparing some ccache since these jobs run on dedicated runners that are not part of the organitzation
-      #- name: ccache
-      #  uses: ggml-org/ccache-action@v1.2.21
-      #  with:
-      #    key: arm64-cpu-kleidiai-graviton4
-      #    evict-old-files: 1d
-      #    save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
-
      - name: Test
        id: ggml-ci
        run: |
@@ -88,7 +88,7 @@ jobs:
 #      - name: ccache
 #        uses: ggml-org/ccache-action@v1.2.21
 #        with:
-#          key: ubuntu-24-sycl-${{ matrix.build }}
+#          key: sycl-ubuntu-24-${{ matrix.build }}
 #          evict-old-files: 1d
 #          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
 #
@@ -150,7 +150,7 @@ jobs:
 #      - name: ccache
 #        uses: ggml-org/ccache-action@v1.2.21
 #        with:
-#          key: windows-latest-sycl
+#          key: sycl-windows-latest
 #          variant: ccache
 #          evict-old-files: 1d
 #          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
@@ -36,21 +36,51 @@ env:
  LLAMA_ARG_LOG_TIMESTAMPS: 1

 jobs:
-  ubuntu-24-vulkan-llvmpipe:
-    runs-on: ubuntu-24.04
+  ubuntu-arm64:
+    runs-on: ubuntu-24.04-arm

    steps:
      - name: Clone
        id: checkout
        uses: actions/checkout@v6

+      - name: Dependencies
+        id: depends
+        run: |
+          sudo apt-get update
+          sudo apt-get install -y gcc-14 g++-14 build-essential glslc libvulkan-dev spirv-headers libssl-dev ninja-build
+          echo "CC=gcc-14" >> "$GITHUB_ENV"
+          echo "CXX=g++-14" >> "$GITHUB_ENV"
+
      - name: ccache
        uses: ggml-org/ccache-action@v1.2.21
        with:
-          key: ubuntu-24-vulkan-llvmpipe
+          key: vulkan-ubuntu-24.04-arm-new
+          variant: ccache
          evict-old-files: 1d
          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}

+      - name: Configure
+        id: cmake_configure
+        run: |
+          cmake -B build \
+            -G "Ninja" \
+            -DCMAKE_BUILD_TYPE=Release \
+            -DGGML_VULKAN=ON
+
+      - name: Build
+        id: cmake_build
+        run: |
+          time cmake --build build -j $(nproc)
+
+  ubuntu-llvmpipe:
+    runs-on: ubuntu-24.04
+
+    steps:
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v6
+
      - name: Dependencies
        id: depends
        run: |
@@ -77,6 +107,13 @@ jobs:
          path: ./vulkan_sdk
          version: ${{ env.VULKAN_SDK_VERSION }}

+      - name: ccache
+        uses: ggml-org/ccache-action@v1.2.21
+        with:
+          key: vulkan-ubuntu-24.04-llvmpipe
+          evict-old-files: 1d
+          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
+
      - name: Build
        id: cmake_build
        run: |
@@ -35,7 +35,7 @@ env:
  LLAMA_ARG_LOG_TIMESTAMPS: 1

 jobs:
-  macos-latest-webgpu:
+  macos:
    runs-on: macos-latest

    steps:
@@ -46,7 +46,7 @@ jobs:
      - name: ccache
        uses: ggml-org/ccache-action@v1.2.21
        with:
-          key: macos-latest-webgpu
+          key: webgpu-macos-latest
          evict-old-files: 1d
          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}

@@ -76,7 +76,7 @@ jobs:
          cd build
          ctest -L main --verbose --timeout 900

-  ubuntu-24-webgpu:
+  ubuntu:
    runs-on: ubuntu-24.04

    steps:
@@ -87,7 +87,7 @@ jobs:
      - name: ccache
        uses: ggml-org/ccache-action@v1.2.21
        with:
-          key: ubuntu-24-webgpu
+          key: webgpu-ubuntu-24.04
          evict-old-files: 1d
          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}

@@ -129,8 +129,8 @@ jobs:
          # test-backend-ops is too slow on llvmpipe, skip it
          ctest -L main -E test-backend-ops --verbose --timeout 900

-  ubuntu-24-webgpu-wasm:
-    runs-on: ${{ 'ubuntu-24.04-arm' || 'ubuntu-24.04' }}
+  ubuntu-wasm:
+    runs-on: ubuntu-24.04-arm

    steps:
      - name: Clone
@@ -140,7 +140,7 @@ jobs:
      - name: ccache
        uses: ggml-org/ccache-action@v1.2.21
        with:
-          key: ubuntu-24-webgpu-wasm
+          key: webgpu-ubuntu-24.04-arm-wasm
          evict-old-files: 1d
          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}

@@ -50,7 +50,7 @@ jobs:
      - name: ccache
        uses: ggml-org/ccache-action@v1.2.21
        with:
-          key: ubuntu-22-hip-quality-check
+          key: hip-quality-check-ubuntu-22.04
          evict-old-files: 1d
          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}

@@ -27,18 +27,19 @@ on:
      '**/*.glsl'
    ]

-concurrency:
-  group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
-  cancel-in-progress: true
-
 env:
+  GH_TOKEN: ${{ github.token }}
  BRANCH_NAME: ${{ github.head_ref || github.ref_name }}
  CMAKE_ARGS: "-DLLAMA_BUILD_EXAMPLES=OFF -DLLAMA_BUILD_TESTS=OFF -DLLAMA_BUILD_TOOLS=ON -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON"

-jobs:
+# note: run this workflow one at a time for better cache reuse
+concurrency:
+  group: release
+  queue: max

-  check_release:
-    runs-on: [self-hosted, fast]
+jobs:
+  check-release:
+    runs-on: ubuntu-slim

    outputs:
      should_release: ${{ steps.check.outputs.should_release }}
@@ -59,14 +60,14 @@ jobs:
          fi

  macos-cpu:
-    needs: [check_release]
-    if: ${{ needs.check_release.outputs.should_release == 'true' }}
+    needs: [check-release]
+    if: ${{ needs.check-release.outputs.should_release == 'true' }}
    strategy:
      matrix:
        include:
          - build: 'arm64'
            arch: 'arm64'
-            os: macos-14
+            os: macos-26
            defines: "-DGGML_METAL_USE_BF16=ON -DGGML_METAL_EMBED_LIBRARY=ON"
          # TODO: this build is disabled to save Github Actions resources (https://github.com/ggml-org/llama.cpp/pull/23780)
          #       in order to enable it again, we have to provision dedicated runners  to run it
@@ -83,6 +84,9 @@ jobs:

    runs-on: ${{ matrix.os }}

+    permissions:
+      actions: write
+
    steps:
      - name: Clone
        id: checkout
@@ -100,8 +104,7 @@ jobs:
      - name: ccache
        uses: ggml-org/ccache-action@v1.2.21
        with:
-          key: macos-latest-${{ matrix.arch }}
-          evict-old-files: 1d
+          key: release-${{ matrix.os }}-${{ matrix.arch }}

      - name: Build
        id: cmake_build
@@ -116,6 +119,11 @@ jobs:
            ${{ env.CMAKE_ARGS }}
          cmake --build build --config Release -j $(sysctl -n hw.logicalcpu)

+      - name: ccache-clear
+        uses: ./.github/actions/ccache-clear
+        with:
+          key: release-${{ matrix.os }}-${{ matrix.arch }}
+
      - name: Determine tag name
        id: tag
        uses: ./.github/actions/get-tag-name
@@ -133,8 +141,8 @@ jobs:
          name: llama-bin-macos-${{ matrix.build }}.tar.gz

  ubuntu-cpu:
-    needs: [check_release]
-    if: ${{ needs.check_release.outputs.should_release == 'true' }}
+    needs: [check-release]
+    if: ${{ needs.check-release.outputs.should_release == 'true' }}
    strategy:
      matrix:
        include:
@@ -147,6 +155,9 @@ jobs:

    runs-on: ${{ matrix.os }}

+    permissions:
+      actions: write
+
    steps:
      - name: Clone
        id: checkout
@@ -161,13 +172,6 @@ jobs:
          cache: "npm"
          cache-dependency-path: "tools/ui/package-lock.json"

-      - name: ccache
-        if: ${{ matrix.build != 's390x' }}
-        uses: ggml-org/ccache-action@v1.2.21
-        with:
-          key: ubuntu-cpu-${{ matrix.build }}
-          evict-old-files: 1d
-
      - name: Dependencies
        id: depends
        run: |
@@ -181,6 +185,12 @@ jobs:
          echo "CC=gcc-14" >> "$GITHUB_ENV"
          echo "CXX=g++-14" >> "$GITHUB_ENV"

+      - name: ccache
+        if: ${{ matrix.build != 's390x' }}
+        uses: ggml-org/ccache-action@v1.2.21
+        with:
+          key: release-${{ matrix.os }}-cpu
+
      - name: Build
        id: cmake_build
        run: |
@@ -194,6 +204,12 @@ jobs:
            ${{ env.CMAKE_ARGS }}
          cmake --build build --config Release -j $(nproc)

+      - name: ccache-clear
+        if: ${{ matrix.build != 's390x' }}
+        uses: ./.github/actions/ccache-clear
+        with:
+          key: release-${{ matrix.os }}-cpu
+
      - name: Determine tag name
        id: tag
        uses: ./.github/actions/get-tag-name
@@ -211,8 +227,8 @@ jobs:
          name: llama-bin-ubuntu-${{ matrix.build }}.tar.gz

  ubuntu-vulkan:
-    needs: [check_release]
-    if: ${{ needs.check_release.outputs.should_release == 'true' }}
+    needs: [check-release]
+    if: ${{ needs.check-release.outputs.should_release == 'true' }}

    strategy:
      matrix:
@@ -224,6 +240,9 @@ jobs:

    runs-on: ${{ matrix.os }}

+    permissions:
+      actions: write
+
    steps:
      - name: Clone
        id: checkout
@@ -238,12 +257,6 @@ jobs:
          cache: "npm"
          cache-dependency-path: "tools/ui/package-lock.json"

-      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.21
-        with:
-          key: ubuntu-vulkan-${{ matrix.build }}
-          evict-old-files: 1d
-
      - name: Dependencies
        id: depends
        run: |
@@ -259,6 +272,11 @@ jobs:
            echo "CXX=g++-14" >> "$GITHUB_ENV"
          fi

+      - name: ccache
+        uses: ggml-org/ccache-action@v1.2.21
+        with:
+          key: release-${{ matrix.os }}-vulkan
+
      - name: Build
        id: cmake_build
        run: |
@@ -272,6 +290,11 @@ jobs:
            ${{ env.CMAKE_ARGS }}
          cmake --build build --config Release -j $(nproc)

+      - name: ccache-clear
+        uses: ./.github/actions/ccache-clear
+        with:
+          key: release-${{ matrix.os }}-vulkan
+
      - name: Determine tag name
        id: tag
        uses: ./.github/actions/get-tag-name
@@ -289,11 +312,14 @@ jobs:
          name: llama-bin-ubuntu-vulkan-${{ matrix.build }}.tar.gz

  android-arm64:
-    needs: [check_release]
-    if: ${{ needs.check_release.outputs.should_release == 'true' }}
+    needs: [check-release]
+    if: ${{ needs.check-release.outputs.should_release == 'true' }}

    runs-on: ubuntu-latest

+    #permissions:
+    #  actions: write
+
    env:
      NDK_VERSION: "29.0.14206865"

@@ -311,12 +337,6 @@ jobs:
          cache: "npm"
          cache-dependency-path: "tools/ui/package-lock.json"

-      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.21
-        with:
-          key: android-arm64
-          evict-old-files: 1d
-
      - name: Set up JDK
        uses: actions/setup-java@v5
        with:
@@ -333,6 +353,17 @@ jobs:
          sdkmanager "ndk;${{ env.NDK_VERSION }}"
          echo "ANDROID_NDK=${ANDROID_SDK_ROOT}/ndk/${{ env.NDK_VERSION }}" >> $GITHUB_ENV

+      # note : disabled to spare some cache space (https://github.com/ggml-org/llama.cpp/pull/23789)
+      #        for some reason, the ccache does not improve the build time in this case
+      # example:
+      #   cache off: https://github.com/ggerganov/tmp2/actions/runs/26534713799/job/78160400831
+      #   cache on:  https://github.com/ggerganov/tmp2/actions/runs/26534713799/job/78224189394
+      #
+      #- name: ccache
+      #  uses: ggml-org/ccache-action@v1.2.21
+      #  with:
+      #    key: release-android-arm64
+
      - name: Build
        id: cmake_build
        run: |
@@ -351,6 +382,11 @@ jobs:
            ${{ env.CMAKE_ARGS }}
          cmake --build build --config Release -j $(nproc)

+      #- name: ccache-clear
+      #  uses: ./.github/actions/ccache-clear
+      #  with:
+      #    key: release-android-arm64
+
      - name: Determine tag name
        id: tag
        uses: ./.github/actions/get-tag-name
@@ -368,11 +404,14 @@ jobs:
          name: llama-bin-android-arm64.tar.gz

  ubuntu-24-openvino:
-    needs: [check_release]
-    if: ${{ needs.check_release.outputs.should_release == 'true' }}
+    needs: [check-release]
+    if: ${{ needs.check-release.outputs.should_release == 'true' }}

    runs-on: ubuntu-24.04

+    permissions:
+      actions: write
+
    outputs:
      openvino_version: ${{ steps.openvino_version.outputs.value }}

@@ -402,8 +441,7 @@ jobs:
      - name: ccache
        uses: ggml-org/ccache-action@v1.2.21
        with:
-          key: ubuntu-24-openvino-release-no-preset-v1
-          evict-old-files: 1d
+          key: release-ubuntu-24.04-openvino-release-no-preset-v1

      - name: Dependencies
        run: |
@@ -441,6 +479,11 @@ jobs:
            -DGGML_OPENVINO=ON
          cmake --build build/ReleaseOV --config Release -j $(nproc)

+      - name: ccache-clear
+        uses: ./.github/actions/ccache-clear
+        with:
+          key: release-ubuntu-24.04-openvino-release-no-preset-v1
+
      - name: Determine tag name
        id: tag
        uses: ./.github/actions/get-tag-name
@@ -458,11 +501,14 @@ jobs:
          name: llama-bin-ubuntu-openvino-${{ env.OPENVINO_VERSION_MAJOR }}-x64.tar.gz

  windows-cpu:
-    needs: [check_release]
-    if: ${{ needs.check_release.outputs.should_release == 'true' }}
+    needs: [check-release]
+    if: ${{ needs.check-release.outputs.should_release == 'true' }}

    runs-on: windows-2025

+    permissions:
+      actions: write
+
    strategy:
      matrix:
        include:
@@ -482,17 +528,15 @@ jobs:
          cache: "npm"
          cache-dependency-path: "tools/ui/package-lock.json"

-      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.21
-        with:
-          key: windows-latest-cpu-${{ matrix.arch }}
-          variant: ccache
-          evict-old-files: 1d
-
      - name: Install Ninja
        run: |
          choco install ninja

+      - name: ccache
+        uses: ggml-org/ccache-action@v1.2.21
+        with:
+          key: release-windows-2025-${{ matrix.arch }}-cpu
+
      - name: Build
        shell: cmd
        run: |
@@ -507,6 +551,11 @@ jobs:
            ${{ env.CMAKE_ARGS }}
          cmake --build build --config Release

+      - name: ccache-clear
+        uses: ./.github/actions/ccache-clear
+        with:
+          key: release-windows-2025-${{ matrix.arch }}-cpu
+
      - name: Pack artifacts
        id: pack_artifacts
        run: |
@@ -520,11 +569,14 @@ jobs:
          name: llama-bin-win-cpu-${{ matrix.arch }}.zip

  windows:
-    needs: [check_release]
-    if: ${{ needs.check_release.outputs.should_release == 'true' }}
+    needs: [check-release]
+    if: ${{ needs.check-release.outputs.should_release == 'true' }}

    runs-on: windows-2025

+    permissions:
+      actions: write
+
    env:
      OPENBLAS_VERSION: 0.3.23
      VULKAN_VERSION: 1.4.313.2
@@ -553,13 +605,6 @@ jobs:
          cache: "npm"
          cache-dependency-path: "tools/ui/package-lock.json"

-      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.21
-        with:
-          key: windows-latest-${{ matrix.backend }}-${{ matrix.arch }}
-          variant: ccache
-          evict-old-files: 1d
-
      - name: Install Vulkan SDK
        id: get_vulkan
        if: ${{ matrix.backend == 'vulkan' }}
@@ -574,6 +619,11 @@ jobs:
        run: |
          choco install ninja

+      - name: ccache
+        uses: ggml-org/ccache-action@v1.2.21
+        with:
+          key: release-windows-2025-${{ matrix.arch }}-${{ matrix.backend }}
+
      - name: Install OpenCL Headers and Libs
        id: install_opencl
        if: ${{ matrix.backend == 'opencl-adreno' && matrix.arch == 'arm64' }}
@@ -600,6 +650,11 @@ jobs:
          cmake -S . -B build ${{ matrix.defines }} -DGGML_NATIVE=OFF -DGGML_CPU=OFF -DGGML_BACKEND_DL=ON -DLLAMA_BUILD_BORINGSSL=ON
          cmake --build build --config Release --target ${{ matrix.target }}

+      - name: ccache-clear
+        uses: ./.github/actions/ccache-clear
+        with:
+          key: release-windows-2025-${{ matrix.arch }}-${{ matrix.backend }}
+
      - name: Pack artifacts
        id: pack_artifacts
        run: |
@@ -612,11 +667,14 @@ jobs:
          name: llama-bin-win-${{ matrix.backend }}-${{ matrix.arch }}.zip

  windows-cuda:
-    needs: [check_release]
-    if: ${{ needs.check_release.outputs.should_release == 'true' }}
+    needs: [check-release]
+    if: ${{ needs.check-release.outputs.should_release == 'true' }}

    runs-on: windows-2022

+    permissions:
+      actions: write
+
    strategy:
      matrix:
        cuda: ['12.4', '13.3']
@@ -633,13 +691,6 @@ jobs:
          cache: "npm"
          cache-dependency-path: "tools/ui/package-lock.json"

-      - name: Install ccache
-        uses: ggml-org/ccache-action@v1.2.21
-        with:
-          key: windows-cuda-${{ matrix.cuda }}
-          variant: ccache
-          evict-old-files: 1d
-
      - name: Install Cuda Toolkit
        uses: ./.github/actions/windows-setup-cuda
        with:
@@ -650,6 +701,11 @@ jobs:
        run: |
          choco install ninja

+      - name: ccache
+        uses: ggml-org/ccache-action@v1.2.21
+        with:
+          key: release-windows-2022-x64-cuda-${{ matrix.cuda }}
+
      - name: Build
        id: cmake_build
        shell: cmd
@@ -666,6 +722,11 @@ jobs:
          set /A NINJA_JOBS=%NUMBER_OF_PROCESSORS%-1
          cmake --build build --config Release -j %NINJA_JOBS% --target ggml-cuda

+      - name: ccache-clear
+        uses: ./.github/actions/ccache-clear
+        with:
+          key: release-windows-2022-x64-cuda-${{ matrix.cuda }}
+
      - name: Pack artifacts
        id: pack_artifacts
        run: |
@@ -744,9 +805,7 @@ jobs:
 #      - name: ccache
 #        uses: ggml-org/ccache-action@v1.2.21
 #        with:
-#          key: windows-latest-sycl
-#          variant: ccache
-#          evict-old-files: 1d
+#          key: release-windows-2022-x64-sycl
 #
 #      - name: Build
 #        id: cmake_build
@@ -866,9 +925,7 @@ jobs:
 #      - name: ccache
 #        uses: ggml-org/ccache-action@v1.2.21
 #        with:
-#          key: ubuntu-24-sycl-${{ matrix.build }}
-#          evict-old-files: 1d
-#          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
+#          key: release-ubuntu-24.04-sycl
 #
 #      - name: Build
 #        id: cmake_build
@@ -902,11 +959,14 @@ jobs:
 #          name: llama-bin-ubuntu-sycl-${{ matrix.build }}-x64.tar.gz

  ubuntu-22-rocm:
-    needs: [check_release]
-    if: ${{ needs.check_release.outputs.should_release == 'true' }}
+    needs: [check-release]
+    if: ${{ needs.check-release.outputs.should_release == 'true' }}

    runs-on: ubuntu-22.04

+    permissions:
+      actions: write
+
    strategy:
      matrix:
        include:
@@ -936,8 +996,7 @@ jobs:
      - name: ccache
        uses: ggml-org/ccache-action@v1.2.21
        with:
-          key: ubuntu-rocm-${{ matrix.ROCM_VERSION }}-${{ matrix.build }}
-          evict-old-files: 1d
+          key: release-ubuntu-22.04-rocm-${{ matrix.ROCM_VERSION }}

      - name: Dependencies
        id: depends
@@ -995,6 +1054,11 @@ jobs:
            ${{ env.CMAKE_ARGS }}
          cmake --build build --config Release -j $(nproc)

+      - name: ccache-clear
+        uses: ./.github/actions/ccache-clear
+        with:
+          key: release-ubuntu-22.04-rocm-${{ matrix.ROCM_VERSION }}
+
      - name: Determine tag name
        id: tag
        uses: ./.github/actions/get-tag-name
@@ -1015,11 +1079,14 @@ jobs:
          name: llama-bin-ubuntu-rocm-${{ env.ROCM_VERSION_SHORT }}-${{ matrix.build }}.tar.gz

  windows-hip:
-    needs: [check_release]
-    if: ${{ needs.check_release.outputs.should_release == 'true' }}
+    needs: [check-release]
+    if: ${{ needs.check-release.outputs.should_release == 'true' }}

    runs-on: windows-2022

+    permissions:
+      actions: write
+
    env:
      HIPSDK_INSTALLER_VERSION: "26.Q1"

@@ -1058,8 +1125,7 @@ jobs:
      - name: ccache
        uses: ggml-org/ccache-action@v1.2.21
        with:
-          key: windows-latest-hip-${{ env.HIPSDK_INSTALLER_VERSION }}-${{ matrix.name }}-x64
-          evict-old-files: 1d
+          key: release-windows-2022-x64-hip-${{ env.HIPSDK_INSTALLER_VERSION }}-${{ matrix.name }}

      - name: Install ROCm
        if: steps.cache-rocm.outputs.cache-hit != 'true'
@@ -1119,6 +1185,11 @@ jobs:
          cp "${env:HIP_PATH}\bin\rocblas\library\*" "build\bin\rocblas\library\"
          cp "${env:HIP_PATH}\bin\hipblaslt\library\*" "build\bin\hipblaslt\library\"

+      - name: ccache-clear
+        uses: ./.github/actions/ccache-clear
+        with:
+          key: release-windows-2022-x64-hip-${{ env.HIPSDK_INSTALLER_VERSION }}-${{ matrix.name }}
+
      - name: Pack artifacts
        id: pack_artifacts
        run: |
@@ -1130,10 +1201,10 @@ jobs:
          path: llama-bin-win-hip-${{ matrix.name }}-x64.zip
          name: llama-bin-win-hip-${{ matrix.name }}-x64.zip

-  ios-xcode-build:
-    needs: [check_release]
-    if: ${{ needs.check_release.outputs.should_release == 'true' }}
-    runs-on: macos-15
+  ios-xcode:
+    needs: [check-release]
+    if: ${{ needs.check-release.outputs.should_release == 'true' }}
+    runs-on: macos-26

    steps:
      - name: Checkout code
@@ -1143,7 +1214,7 @@ jobs:

      - name: Setup Xcode
        run: |
-          sudo xcode-select -s /Applications/Xcode_16.4.app
+          sudo xcode-select -s /Applications/Xcode_26.4.app

      - name: Build
        id: cmake_build
@@ -1159,7 +1230,7 @@ jobs:
            -DLLAMA_BUILD_TESTS=OFF \
            -DLLAMA_BUILD_SERVER=OFF \
            -DCMAKE_SYSTEM_NAME=iOS \
-            -DCMAKE_OSX_DEPLOYMENT_TARGET=14.0 \
+            -DCMAKE_OSX_DEPLOYMENT_TARGET=16.0 \
            -DCMAKE_XCODE_ATTRIBUTE_DEVELOPMENT_TEAM=ggml
          cmake --build build --config Release -j $(sysctl -n hw.logicalcpu) -- CODE_SIGNING_ALLOWED=NO

@@ -1280,9 +1351,9 @@ jobs:
 #          path: llama-${{ steps.tag.outputs.name }}-bin-${{ matrix.chip_type }}-openEuler-${{ matrix.arch }}${{ matrix.use_acl_graph == 'on' && '-aclgraph' || '' }}.tar.gz
 #          name: llama-bin-${{ matrix.chip_type }}-openEuler-${{ matrix.arch }}${{ matrix.use_acl_graph == 'on' && '-aclgraph' || '' }}.tar.gz

-  ui-build:
-    needs: [check_release]
-    if: ${{ needs.check_release.outputs.should_release == 'true' }}
+  ui:
+    needs: [check-release]
+    if: ${{ needs.check-release.outputs.should_release == 'true' }}
    uses: ./.github/workflows/ui-build.yml

  release:
@@ -1308,9 +1379,9 @@ jobs:
      #- ubuntu-24-sycl
      - android-arm64
      - macos-cpu
-      - ios-xcode-build
+      - ios-xcode
      #- openEuler-cann
-      - ui-build
+      - ui

    outputs:
      tag_name: ${{ steps.tag.outputs.name }}
@@ -55,21 +55,7 @@ concurrency:

 jobs:
  ubuntu:
-    runs-on: ubuntu-latest
-
-    name: ubuntu (${{ matrix.wf_name }})
-    strategy:
-      matrix:
-        build_type: [Release]
-        wf_name: ["default"]
-        include:
-          - build_type: Release
-            extra_args: ""
-            wf_name:    "default"
-          - build_type: Release
-            extra_args: "LLAMA_ARG_BACKEND_SAMPLING=1"
-            wf_name:    "backend-sampling"
-      fail-fast: false
+    runs-on: ubuntu-24.04-arm

    steps:
      - name: Dependencies
@@ -96,7 +82,7 @@ jobs:
      - name: ccache
        uses: ggml-org/ccache-action@v1.2.21
        with:
-          key: server-ubuntu-default
+          key: server-ubuntu-24.04-arm
          evict-old-files: 1d
          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}

@@ -105,7 +91,7 @@ jobs:
        run: |
          cmake -B build \
            -DGGML_SCHED_NO_REALLOC=ON
-          cmake --build build --config ${{ matrix.build_type }} -j $(nproc) --target llama-server
+          cmake --build build --config Release -j $(nproc) --target llama-server

      - name: Python setup
        id: setup_python
@@ -116,18 +102,30 @@ jobs:

      - name: Tests
        id: server_integration_tests
-        if: ${{ (!matrix.disabled_on_pr || !github.event.pull_request) }}
        run: |
          cd tools/server/tests
-          export ${{ matrix.extra_args }}
          pytest -v -x -m "not slow"

      - name: Slow tests
        id: server_integration_tests_slow
-        if: ${{ (github.event.schedule || github.event.inputs.slow_tests == 'true') && matrix.build_type == 'Release' }}
+        if: ${{ github.event.schedule || github.event.inputs.slow_tests == 'true' }}
        run: |
          cd tools/server/tests
-          export ${{ matrix.extra_args }}
+          SLOW_TESTS=1 pytest -v -x
+
+      - name: Tests (Backend sampling)
+        id: server_integration_tests_backend_sampling
+        run: |
+          cd tools/server/tests
+          export LLAMA_ARG_BACKEND_SAMPLING=1
+          pytest -v -x -m "not slow"
+
+      - name: Slow tests (Backend sampling)
+        id: server_integration_tests_slow_backend_sampling
+        if: ${{ github.event.schedule || github.event.inputs.slow_tests == 'true' }}
+        run: |
+          cd tools/server/tests
+          export LLAMA_ARG_BACKEND_SAMPLING=1
          SLOW_TESTS=1 pytest -v -x

  windows:
@@ -144,7 +142,7 @@ jobs:
      - name: ccache
        uses: ggml-org/ccache-action@v1.2.21
        with:
-          key: server-windows-default
+          key: server-windows-2025-x64
          evict-old-files: 1d
          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}

@@ -169,7 +167,6 @@ jobs:

      - name: Tests
        id: server_integration_tests
-        if: ${{ !matrix.disabled_on_pr || !github.event.pull_request }}
        run: |
          cd tools/server/tests
          $env:PYTHONIOENCODING = ":replace"
@@ -177,7 +174,7 @@ jobs:

      - name: Slow tests
        id: server_integration_tests_slow
-        if: ${{ (github.event.schedule || github.event.inputs.slow_tests == 'true') && matrix.build_type == 'Release' }}
+        if: ${{ github.event.schedule || github.event.inputs.slow_tests == 'true' }}
        run: |
          cd tools/server/tests
          $env:SLOW_TESTS = "1"
@@ -0,0 +1,43 @@
+name: UI Build (self-hosted)
+
+on:
+  workflow_call:
+
+jobs:
+  build:
+    runs-on: [self-hosted, fast]
+    env:
+      BRANCH_NAME: ${{ github.head_ref || github.ref_name }}
+
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v6
+
+      - name: Setup Node.js
+        uses: actions/setup-node@v6
+        with:
+          node-version: "24"
+          cache: "npm"
+          cache-dependency-path: "tools/ui/package-lock.json"
+
+      - name: Install dependencies
+        run: npm ci
+        working-directory: tools/ui
+
+      - name: Build application
+        run: npm run build
+        working-directory: tools/ui
+
+      - name: Generate checksums
+        run: |
+          cd tools/ui/dist
+          for f in *; do
+            sha256sum "$f" | awk '{print $1, $2}' >> checksums.txt
+          done
+
+      - name: Upload built UI
+        uses: actions/upload-artifact@v6
+        with:
+          name: ui-build
+          path: tools/ui/dist/
+          retention-days: 1
@@ -5,7 +5,7 @@ on:

 jobs:
  build:
-    runs-on: [self-hosted, fast]
+    runs-on: ubuntu-slim
    env:
      BRANCH_NAME: ${{ github.head_ref || github.ref_name }}

@@ -20,7 +20,7 @@ jobs:
  publish:
    name: Publish UI Static Output
    needs: build
-    runs-on: ubuntu-24.04-arm
+    runs-on: ubuntu-slim

    permissions:
      contents: read
@@ -16,7 +16,7 @@ on:
      - master
    paths: [
      '.github/workflows/ui-self-hosted.yml',
-      '.github/workflows/ui-build.yml',
+      '.github/workflows/ui-build-self-hosted.yml',
      'tools/ui/**.*',
      'tools/server/tests/**.*'
    ]
@@ -24,7 +24,7 @@ on:
    types: [opened, synchronize, reopened]
    paths: [
      '.github/workflows/ui-self-hosted.yml',
-      '.github/workflows/ui-build.yml',
+      '.github/workflows/ui-build-self-hosted.yml',
      'tools/ui/**.*',
      'tools/server/tests/**.*'
    ]
@@ -42,7 +42,7 @@ concurrency:
 jobs:
  ui-build:
    name: Build static output
-    uses: ./.github/workflows/ui-build.yml
+    uses: ./.github/workflows/ui-build-self-hosted.yml

  ui-checks:
    name: Checks
@@ -222,19 +222,6 @@ if (LLAMA_BUILD_APP)
    add_subdirectory(app)
 endif()

-# Automatically add all files from the 'licenses' directory
-file(GLOB EXTRA_LICENSES "${CMAKE_SOURCE_DIR}/licenses/LICENSE-*")
-
-foreach(FILE_PATH ${EXTRA_LICENSES})
-    get_filename_component(FILE_NAME "${FILE_PATH}" NAME)
-    string(REGEX REPLACE "^LICENSE-" "" NAME "${FILE_NAME}")
-    license_add_file("${NAME}" "${FILE_PATH}")
-endforeach()
-
-if (LLAMA_BUILD_COMMON)
-    license_generate(llama-common)
-endif()
-
 #
 # install
 #
@@ -12,16 +12,16 @@

 ## Reporting a vulnerability

+> [!IMPORTANT]
+> The private security disclosure program is disabled until further notice. Please submit patches with fixes directly to the repo as public PRs. Emails will be ignored.
+
 If you have discovered a security vulnerability in this project that falls inside the [covered topics](#covered-topics), please report it privately. **Do not disclose it as a public issue.** This gives us time to work with you to fix the issue before public exposure, reducing the chance that the exploit will be used before a patch is released.

 Please disclose it as a private [security advisory](https://github.com/ggml-org/llama.cpp/security/advisories/new).

 A team of volunteers on a reasonable-effort basis maintains this project. As such, please give us at least 90 days to work on a fix before public exposure.

-> [!IMPORTANT]
-> For collaborators: if you are interested in helping out with reviewing private security disclosures, please see: https://github.com/ggml-org/llama.cpp/discussions/18080
-
-## Requirements
+### Requirements

 Before submitting your report, ensure you meet the following requirements:

@@ -31,7 +31,7 @@ Before submitting your report, ensure you meet the following requirements:

 Maintainers reserve the right to close the report if these requirements are not fulfilled.

-## Covered Topics
+### Covered Topics

 Only vulnerabilities that fall within these parts of the project are considered valid. For problems falling outside of this list, please report them as issues.

@@ -15,6 +15,17 @@ target_link_libraries(${TARGET} PRIVATE
 )
 target_compile_features(${TARGET} PRIVATE cxx_std_17)

+# Automatically add all files from the 'licenses' directory
+file(GLOB EXTRA_LICENSES "${CMAKE_SOURCE_DIR}/licenses/LICENSE-*")
+
+foreach(FILE_PATH ${EXTRA_LICENSES})
+    get_filename_component(FILE_NAME "${FILE_PATH}" NAME)
+    string(REGEX REPLACE "^LICENSE-" "" NAME "${FILE_NAME}")
+    license_add_file("${NAME}" "${FILE_PATH}")
+endforeach()
+
+license_generate(${TARGET})
+
 if(LLAMA_TOOLS_INSTALL)
    install(TARGETS ${TARGET} RUNTIME)
 endif()
@@ -5,6 +5,9 @@
 #include <string>
 #include <vector>

+// embedded data generated by cmake
+extern const char * LICENSES[];
+
 // visible
 int llama_server(int argc, char ** argv);
 int llama_cli(int argc, char ** argv);
@@ -17,8 +20,23 @@ int llama_fit_params(int argc, char ** argv);
 int llama_quantize(int argc, char ** argv);
 int llama_perplexity(int argc, char ** argv);

+// hands the update over to the install script, which downloads and swaps the binary
+static int llama_update(int argc, char ** argv) {
+    (void) argc;
+    (void) argv;
+
+#if defined(_WIN32)
+    return system("powershell -NoProfile -ExecutionPolicy Bypass -Command \"irm https://llama.app/install.ps1 | iex\"");
+#else
+    return system("curl -fsSL https://llama.app/install.sh | sh");
+#endif
+}
+
+static const char * progname;
+
 static int help(int argc, char ** argv);
 static int version(int argc, char ** argv);
+static int licenses(int argc, char ** argv);

 struct command {
    const char * name;
@@ -31,14 +49,16 @@ struct command {
 static const command cmds[] = {
    {"serve",         "HTTP API server",                                    {"server"},   false, llama_server       },
    {"cli",           "Command-line interactive interface",                 {"client"},   false, llama_cli          },
+    {"update",        "Update llama to the latest release",                 {},           false, llama_update       },
    {"completion",    "Text completion",                                    {"complete"}, true,  llama_completion   },
    {"bench",         "Benchmark prompt processing and text generation",    {},           true,  llama_bench        },
    {"batched-bench", "Benchmark batched decoding performance",             {},           true,  llama_batched_bench},
    {"fit-params",    "Compute parameters to fit a model in device memory", {},           true,  llama_fit_params   },
    {"quantize",      "Quantize a model",                                   {},           true,  llama_quantize     },
    {"perplexity",    "Compute model perplexity and KL divergence",         {},           true,  llama_perplexity   },
-    {"version",       "Show version",                                       {},           true,  version            },
-    {"help",          "Show available commands",                            {},           true,  help               },
+    {"version",       "Show version",                                       {},           false, version            },
+    {"licenses",      "Show third-party licenses",                          {"credits"},  false, licenses           },
+    {"help",          "Show available commands",                            {},           false, help               },
 };

 static int version(int argc, char ** argv) {
@@ -46,17 +66,29 @@ static int version(int argc, char ** argv) {
    return 0;
 }

+static int licenses(int argc, char ** argv) {
+    for (int i = 0; LICENSES[i]; ++i) {
+        printf("%s\n", LICENSES[i]);
+    }
+    return 0;
+}
+
 static int help(int argc, char ** argv) {
    const bool show_all = argc >= 2 && std::string(argv[1]) == "all";

-    printf("Usage: llama <command> [options]\n\nAvailable commands:\n");
+    printf("Usage: %s <command> [options]\n\nAvailable commands:\n", progname);

    for (const auto & cmd : cmds) {
        if (show_all || !cmd.hidden) {
            printf("  %-15s %s\n", cmd.name, cmd.desc);
        }
    }
-    printf("\nRun 'llama <command> --help' for command-specific usage.\n");
+    printf("\n");
+
+    if (!show_all) {
+        printf("Run '%s help all' to show additional commands.\n", progname);
+    }
+    printf("Run '%s <command> --help' for command-specific usage.\n", progname);

    return 0;
 }
@@ -74,13 +106,13 @@ static bool matches(const std::string & arg, const command & cmd) {
 }

 int main(int argc, char ** argv) {
+    progname = argv[0];
+
    const std::string arg = argc >= 2 ? argv[1] : "help";

    for (const auto & cmd : cmds) {
        if (matches(arg, cmd)) {
-
-            // router spawns children through this same binary, it needs the
-            // subcommand to relaunch as 'llama serve' and not bare options
+            // keep cmd.name so the router's child processes re-invoke correctly
 #ifdef _WIN32
            _putenv_s("LLAMA_APP_CMD", cmd.name);
 #else
@@ -8,6 +8,7 @@ TVOS_MIN_OS_VERSION=16.4

 BUILD_SHARED_LIBS=OFF
 LLAMA_BUILD_APP=OFF
+LLAMA_BUILD_COMMON=OFF
 LLAMA_BUILD_EXAMPLES=OFF
 LLAMA_BUILD_TOOLS=OFF
 LLAMA_BUILD_TESTS=OFF
@@ -33,6 +34,7 @@ COMMON_CMAKE_ARGS=(
    -DCMAKE_XCODE_ATTRIBUTE_DEVELOPMENT_TEAM=ggml
    -DBUILD_SHARED_LIBS=${BUILD_SHARED_LIBS}
    -DLLAMA_BUILD_APP=${LLAMA_BUILD_APP}
+    -DLLAMA_BUILD_COMMON=${LLAMA_BUILD_COMMON}
    -DLLAMA_BUILD_EXAMPLES=${LLAMA_BUILD_EXAMPLES}
    -DLLAMA_BUILD_TOOLS=${LLAMA_BUILD_TOOLS}
    -DLLAMA_BUILD_TESTS=${LLAMA_BUILD_TESTS}
@@ -416,7 +418,7 @@ cmake -B build-ios-sim -G Xcode \
    -DCMAKE_CXX_FLAGS="${COMMON_CXX_FLAGS}" \
    -DLLAMA_OPENSSL=OFF \
    -S .
-cmake --build build-ios-sim --config Release -- -quiet
+cmake --build build-ios-sim --config Release -j $(sysctl -n hw.logicalcpu) -- -quiet

 echo "Building for iOS devices..."
 cmake -B build-ios-device -G Xcode \
@@ -430,7 +432,7 @@ cmake -B build-ios-device -G Xcode \
    -DCMAKE_CXX_FLAGS="${COMMON_CXX_FLAGS}" \
    -DLLAMA_OPENSSL=OFF \
    -S .
-cmake --build build-ios-device --config Release -- -quiet
+cmake --build build-ios-device --config Release -j $(sysctl -n hw.logicalcpu) -- -quiet

 echo "Building for macOS..."
 cmake -B build-macos -G Xcode \
@@ -441,7 +443,7 @@ cmake -B build-macos -G Xcode \
    -DCMAKE_CXX_FLAGS="${COMMON_CXX_FLAGS}" \
    -DLLAMA_OPENSSL=OFF \
    -S .
-cmake --build build-macos --config Release -- -quiet
+cmake --build build-macos --config Release -j $(sysctl -n hw.logicalcpu) -- -quiet

 echo "Building for visionOS..."
 cmake -B build-visionos -G Xcode \
@@ -456,7 +458,7 @@ cmake -B build-visionos -G Xcode \
    -DLLAMA_OPENSSL=OFF \
    -DLLAMA_BUILD_SERVER=OFF \
    -S .
-cmake --build build-visionos --config Release -- -quiet
+cmake --build build-visionos --config Release -j $(sysctl -n hw.logicalcpu) -- -quiet

 echo "Building for visionOS simulator..."
 cmake -B build-visionos-sim -G Xcode \
@@ -471,7 +473,7 @@ cmake -B build-visionos-sim -G Xcode \
    -DLLAMA_OPENSSL=OFF \
    -DLLAMA_BUILD_SERVER=OFF \
    -S .
-cmake --build build-visionos-sim --config Release -- -quiet
+cmake --build build-visionos-sim --config Release -j $(sysctl -n hw.logicalcpu) -- -quiet

 # Add tvOS builds (might need the same u_int definitions as watchOS and visionOS)
 echo "Building for tvOS simulator..."
@@ -487,7 +489,7 @@ cmake -B build-tvos-sim -G Xcode \
    -DCMAKE_CXX_FLAGS="${COMMON_CXX_FLAGS}" \
    -DLLAMA_OPENSSL=OFF \
    -S .
-cmake --build build-tvos-sim --config Release -- -quiet
+cmake --build build-tvos-sim --config Release -j $(sysctl -n hw.logicalcpu) -- -quiet

 echo "Building for tvOS devices..."
 cmake -B build-tvos-device -G Xcode \
@@ -502,7 +504,7 @@ cmake -B build-tvos-device -G Xcode \
    -DCMAKE_CXX_FLAGS="${COMMON_CXX_FLAGS}" \
    -DLLAMA_OPENSSL=OFF \
    -S .
-cmake --build build-tvos-device --config Release -- -quiet
+cmake --build build-tvos-device --config Release -j $(sysctl -n hw.logicalcpu) -- -quiet

 # Setup frameworks and copy binaries and headers
 echo "Setting up framework structures..."
@@ -50,8 +50,6 @@

 #define LLAMA_MAX_URL_LENGTH 2084 // Maximum URL Length in Chrome: 2083

-extern const char * LICENSES[];
-
 using json = nlohmann::ordered_json;
 using namespace common_arg_utils;

@@ -342,9 +340,7 @@ struct handle_model_result {
 };

 static handle_model_result common_params_handle_model(struct common_params_model & model,
-                                                      const std::string          & bearer_token,
-                                                      bool                         offline,
-                                                      bool                         search_mtp = false) {
+                                                      const common_download_opts & opts) {
    handle_model_result result;

    if (!model.docker_repo.empty()) {
@@ -356,10 +352,9 @@ static handle_model_result common_params_handle_model(struct common_params_model
            model.hf_file = model.path;
            model.path = "";
        }
-        common_download_opts opts;
-        opts.bearer_token = bearer_token;
-        opts.offline = offline;
-        auto download_result = common_download_model(model, opts, true, search_mtp);
+        common_download_opts hf_opts = opts;
+        hf_opts.download_mmproj = true; // also look for mmproj when downloading hf model
+        auto download_result = common_download_model(model, hf_opts);

        if (download_result.model_path.empty()) {
            throw std::runtime_error("failed to download model from Hugging Face");
@@ -384,9 +379,6 @@ static handle_model_result common_params_handle_model(struct common_params_model
            model.path = fs_get_cache_file(string_split<std::string>(f, '/').back());
        }

-        common_download_opts opts;
-        opts.bearer_token = bearer_token;
-        opts.offline = offline;
        auto download_result = common_download_model(model, opts);
        if (download_result.model_path.empty()) {
            throw std::runtime_error("failed to download model from " + model.url);
@@ -443,35 +435,49 @@ static bool parse_bool_value(const std::string & value) {
 // CLI argument parsing functions
 //

-void common_params_handle_models(common_params & params, llama_example curr_ex) {
+bool common_params_handle_models(common_params & params, llama_example curr_ex) {
    const bool spec_type_draft_mtp = std::find(params.speculative.types.begin(),
                                         params.speculative.types.end(),
                                         COMMON_SPECULATIVE_TYPE_DRAFT_MTP) != params.speculative.types.end();

-    auto res = common_params_handle_model(params.model, params.hf_token, params.offline, spec_type_draft_mtp);
-    if (params.no_mmproj) {
-        params.mmproj = {};
-    } else if (res.found_mmproj && params.mmproj.path.empty() && params.mmproj.url.empty()) {
-        // optionally, handle mmproj model when -hf is specified
-        params.mmproj = res.mmproj;
-    }
-    // only download mmproj if the current example is using it
-    for (const auto & ex : mmproj_examples) {
-        if (curr_ex == ex) {
-            common_params_handle_model(params.mmproj,    params.hf_token, params.offline);
-            break;
+    common_download_opts opts;
+    opts.bearer_token  = params.hf_token;
+    opts.offline       = params.offline;
+    opts.skip_download = params.skip_download;
+    opts.download_mtp  = spec_type_draft_mtp;
+
+    try {
+        auto res = common_params_handle_model(params.model, opts);
+        if (params.no_mmproj) {
+            params.mmproj = {};
+        } else if (res.found_mmproj && params.mmproj.path.empty() && params.mmproj.url.empty()) {
+            // optionally, handle mmproj model when -hf is specified
+            params.mmproj = res.mmproj;
        }
+        // only download mmproj if the current example is using it
+        for (const auto & ex : mmproj_examples) {
+            if (curr_ex == ex) {
+                common_params_handle_model(params.mmproj, opts);
+                break;
+            }
+        }
+
+        // when --spec-type mtp is set and no draft model was provided explicitly,
+        // fall back to the MTP head discovered alongside the -hf model
+        if (spec_type_draft_mtp && res.found_mtp &&
+            params.speculative.draft.mparams.path.empty() &&
+            params.speculative.draft.mparams.hf_repo.empty() &&
+            params.speculative.draft.mparams.url.empty()) {
+            params.speculative.draft.mparams.path = res.mtp.path;
+        }
+        common_params_handle_model(params.speculative.draft.mparams, opts);
+        common_params_handle_model(params.vocoder.model,             opts);
+        return true;
+    } catch (const common_skip_download_exception &) {
+        return false;
+    } catch (const std::exception &) {
+        throw;
    }
-    // when --spec-type mtp is set and no draft model was provided explicitly,
-    // fall back to the MTP head discovered alongside the -hf model
-    if (spec_type_draft_mtp && res.found_mtp &&
-        params.speculative.draft.mparams.path.empty() &&
-        params.speculative.draft.mparams.hf_repo.empty() &&
-        params.speculative.draft.mparams.url.empty()) {
-        params.speculative.draft.mparams.path = res.mtp.path;
-    }
-    common_params_handle_model(params.speculative.draft.mparams, params.hf_token, params.offline);
-    common_params_handle_model(params.vocoder.model,             params.hf_token, params.offline);
 }

 static bool common_params_parse_ex(int argc, char ** argv, common_params_context & ctx_arg) {
@@ -1035,11 +1041,9 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
    // we define here to make sure it's included in llama-gen-docs
    if (ex == LLAMA_EXAMPLE_COMPLETION) {
        params.use_jinja = false;   // disable jinja by default
-
    } else if (ex == LLAMA_EXAMPLE_MTMD) {
        params.use_jinja = false;   // disable jinja by default
        params.sampling.temp = 0.2; // lower temp by default for better quality
-
    } else if (ex == LLAMA_EXAMPLE_SERVER) {
        params.n_parallel = -1;     // auto by default
    }
@@ -1060,7 +1064,6 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
        sampler_type_names.pop_back(); // remove last semicolon
    }

-
    /**
     * filter options by example
     * rules:
@@ -1074,7 +1077,6 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
        }
    };

-
    add_opt(common_arg(
        {"-h", "--help", "--usage"},
        "print usage and exit",
@@ -1091,16 +1093,6 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
            exit(0);
        }
    ));
-    add_opt(common_arg(
-        {"--license"},
-        "show source code license and dependencies",
-        [](common_params &) {
-            for (int i = 0; LICENSES[i]; ++i) {
-                printf("%s\n", LICENSES[i]);
-            }
-            exit(0);
-        }
-    ));
    add_opt(common_arg(
        {"-cl", "--cache-list"},
        "show list of models in cache",
@@ -2998,7 +2990,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
            }
            key_file.close();
        }
-    ).set_examples({LLAMA_EXAMPLE_SERVER}));
+    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_API_KEY_FILE"));
    add_opt(common_arg(
        {"--ssl-key-file"}, "FNAME",
        "path to file a PEM-encoded SSL private key",
@@ -129,8 +129,11 @@ bool common_params_to_map(int argc, char ** argv, llama_example ex, std::map<com
 // see: https://github.com/ggml-org/llama.cpp/issues/18163
 void common_params_add_preset_options(std::vector<common_arg> & args);

-// Populate model paths (main model, mmproj, etc) from -hf if necessary
-void common_params_handle_models(common_params & params, llama_example curr_ex);
+// populate model paths (main model, mmproj, etc) from -hf if necessary
+// return true if the model is ready to use
+// throw an exception if there is an error that prevents the model from being used (e.g. network error, model not found, etc)
+// if params.skip_download is true, no downloads will be attempted. return false if the model is invalid or missing (e.g. ETag check failed)
+bool common_params_handle_models(common_params & params, llama_example curr_ex);

 // initialize argument parser context - used by test-arg-parser and preset
 common_params_context common_params_parser_init(common_params & params, llama_example ex, void(*print_usage)(int, char **) = nullptr);
@@ -1563,6 +1563,7 @@ struct llama_context_params common_context_params_to_llama(const common_params &
    cparams.n_ctx             = params.n_ctx;
    cparams.n_seq_max         = params.n_parallel;
    cparams.n_rs_seq          = params.speculative.need_n_rs_seq();
+    cparams.n_outputs_max     = std::max(params.n_outputs_max, 0);
    cparams.n_batch           = params.n_batch;
    cparams.n_ubatch          = params.n_ubatch;
    cparams.n_threads         = params.cpuparams.n_threads;
@@ -277,6 +277,7 @@ struct common_params_sampling {
    std::vector<llama_token> reasoning_budget_end;             // end tag token sequence
    std::vector<llama_token> reasoning_budget_forced;          // forced sequence (message + end tag)
    std::string              reasoning_budget_message;         // message injected before end tag when budget exhausted
+    bool                     reasoning_control = false;        // create the budget sampler on demand so reasoning can be ended at runtime

    bool backend_sampling = false;

@@ -431,6 +432,7 @@ struct common_params {
    int32_t n_chunks              =    -1; // max number of chunks to process (-1 = unlimited)
    int32_t n_parallel            =     1; // number of parallel sequences to decode
    int32_t n_sequences           =     1; // number of sequences to decode
+    int32_t n_outputs_max         =     0; // max outputs in a batch (0 = n_batch)
    int32_t grp_attn_n            =     1; // group-attention factor
    int32_t grp_attn_w            =   512; // group-attention width
    int32_t n_print               =    -1; // print token count every n tokens (-1 = disabled)
@@ -479,7 +481,7 @@ struct common_params {

    std::set<std::string> model_alias;     // model aliases                                                 // NOLINT
    std::set<std::string> model_tags;      // model tags (informational, not used for routing)              // NOLINT
-    std::string hf_token             = ""; // HF token                                                      // NOLINT
+    std::string hf_token             = ""; // HF token (aka bearer token)                                   // NOLINT
    std::string prompt               = "";                                                                  // NOLINT
    std::string system_prompt        = "";                                                                  // NOLINT
    std::string prompt_file          = ""; // store the external prompt file name                           // NOLINT
@@ -507,6 +509,7 @@ struct common_params {
    int32_t control_vector_layer_start = -1; // layer range for control vector
    int32_t control_vector_layer_end   = -1; // layer range for control vector
    bool    offline                    = false;
+    bool    skip_download              = false; // skip model file downloading

    int32_t ppl_stride      = 0;     // stride for perplexity calculations. If left at 0, the pre-existing approach will be used.
    int32_t ppl_output_type = 0;     // = 0 -> ppl output is as usual, = 1 -> ppl output is num_tokens, ppl, one per line
@@ -587,7 +590,7 @@ struct common_params {
    // server params
    int32_t port                = 8080;          // server listens on this network port
    bool    reuse_port          = false;         // allow multiple sockets to bind to the same port
-    int32_t timeout_read        = 600;           // http read timeout in seconds
+    int32_t timeout_read        = 3600;          // http read timeout in seconds
    int32_t timeout_write       = timeout_read;  // http write timeout in seconds
    int32_t n_threads_http      = -1;    // number of threads to process HTTP requests (TODO: support threadpool)
    int32_t n_cache_reuse       = 0;     // min chunk size to reuse from the cache via KV shifting
@@ -292,6 +292,10 @@ static int common_download_file_single_online(const std::string & url,

    const bool file_exists = std::filesystem::exists(path);

+    if (!file_exists && opts.skip_download) {
+        return -2; // file is missing and download is disabled
+    }
+
    if (file_exists && skip_etag) {
        LOG_DBG("%s: using cached file: %s\n", __func__, path.c_str());
        return 304; // 304 Not Modified - fake cached response
@@ -357,6 +361,10 @@ static int common_download_file_single_online(const std::string & url,
            LOG_DBG("%s: using cached file (same etag): %s\n", __func__, path.c_str());
            return 304; // 304 Not Modified - fake cached response
        }
+        // pass this point, the file exists but is different from the server version, so we need to redownload it
+        if (opts.skip_download) {
+            return -2; // special code to indicate that the download was skipped due to etag mismatch
+        }
        if (remove(path.c_str()) != 0) {
            LOG_ERR("%s: unable to delete file: %s\n", __func__, path.c_str());
            return -1;
@@ -775,13 +783,13 @@ static std::vector<download_task> get_url_tasks(const common_params_model & mode
 }

 common_download_model_result common_download_model(const common_params_model  & model,
-                                                   const common_download_opts & opts,
-                                                   bool download_mmproj,
-                                                   bool download_mtp) {
+                                                   const common_download_opts & opts) {
    common_download_model_result result;
    std::vector<download_task> tasks;
    hf_plan hf;

+    bool download_mmproj = opts.download_mmproj;
+    bool download_mtp = opts.download_mtp;
    bool is_hf = !model.hf_repo.empty();

    if (is_hf) {
@@ -806,18 +814,22 @@ common_download_model_result common_download_model(const common_params_model  &
        return result;
    }

-    std::vector<std::future<bool>> futures;
+    std::vector<std::future<int>> futures;
    for (const auto & task : tasks) {
        futures.push_back(std::async(std::launch::async,
            [&task, &opts, is_hf]() {
-                int status = common_download_file_single(task.url, task.path, opts, is_hf);
-                return is_http_status_ok(status);
+                return common_download_file_single(task.url, task.path, opts, is_hf);
            }
        ));
    }

    for (auto & f : futures) {
-        if (!f.get()) {
+        int status = f.get();
+        if (status == -2 && opts.skip_download) {
+            throw common_skip_download_exception();
+        }
+        bool is_ok = is_http_status_ok(status);
+        if (!is_ok) {
            return {};
        }
    }
@@ -52,6 +52,9 @@ struct common_download_opts {
    std::string bearer_token;
    common_header_list headers;
    bool offline = false;
+    bool skip_download = false; // if true, only validation is performed, common_skip_download_exception may be thrown if the file is missing or invalid
+    bool download_mmproj = false;
+    bool download_mtp = false;
    common_download_callback * callback = nullptr;
 };

@@ -62,6 +65,11 @@ struct common_download_model_result {
    std::string mtp_path;
 };

+// throw if the file is missing or invalid (e.g. ETag check failed)
+struct common_skip_download_exception : public std::runtime_error {
+    common_skip_download_exception() : std::runtime_error("skip download") {}
+};
+
 // Download model from HuggingFace repo or URL
 //
 // input (via model struct):
@@ -89,9 +97,7 @@ struct common_download_model_result {
 // returns result with model_path, mmproj_path and mtp_path (empty when not found / on failure)
 common_download_model_result common_download_model(
    const common_params_model & model,
-    const common_download_opts & opts = {},
-    bool download_mmproj = false,
-    bool download_mtp    = false
+    const common_download_opts & opts = {}
 );

 // returns list of cached models
@@ -99,6 +105,7 @@ std::vector<common_cached_model_info> common_list_cached_models();

 // download single file from url to local path
 // returns status code or -1 on error
+// returns -2 if the download was skipped due to ETag mismatch (file outdated, skip_download=true)
 // skip_etag: if true, don't read/write .etag files (for HF cache where filename is the hash)
 int common_download_file_single(const std::string & url,
                                const std::string & path,
@@ -1,5 +1,7 @@
 #include "ngram-mod.h"

+#include <algorithm>
+
 //
 // common_ngram_mod
 //
@@ -247,3 +247,24 @@ common_reasoning_budget_state common_reasoning_budget_get_state(const struct lla
    }
    return ((const common_reasoning_budget_ctx *)smpl->ctx)->state;
 }
+
+bool common_reasoning_budget_force(struct llama_sampler * smpl) {
+    if (!smpl) {
+        return false;
+    }
+
+    auto * ctx = (common_reasoning_budget_ctx *) smpl->ctx;
+
+    // only a sampler that is actively counting down the budget may be forced;
+    // any other state (idle, already forcing/waiting, or done) is left untouched
+    if (ctx->state != REASONING_BUDGET_COUNTING) {
+        return false;
+    }
+
+    ctx->state = REASONING_BUDGET_FORCING;
+    ctx->force_pos = 0;
+    ctx->end_matcher.reset();
+    LOG_INF("reasoning-budget: forced into forcing state (manual transition)\n");
+
+    return true;
+}
@@ -40,3 +40,7 @@ struct llama_sampler * common_reasoning_budget_init(
        common_reasoning_budget_state    initial_state = REASONING_BUDGET_IDLE);

 common_reasoning_budget_state common_reasoning_budget_get_state(const struct llama_sampler * smpl);
+
+// Manually transition the reasoning budget sampler into the FORCING state.
+// Returns true if the transition occurred.
+bool common_reasoning_budget_force(struct llama_sampler * smpl);
@@ -293,7 +293,7 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, st
    }

    // reasoning budget sampler (skip when budget is unlimited unless a lazy grammar is active, which needs rbudget for thinking-block suppression)
-    if (!params.reasoning_budget_start.empty() && !params.reasoning_budget_end.empty() && (params.grammar_lazy || params.reasoning_budget_tokens >= 0)) {
+    if (!params.reasoning_budget_start.empty() && !params.reasoning_budget_end.empty() && (params.grammar_lazy || params.reasoning_budget_tokens >= 0 || params.reasoning_control)) {
        rbudget = common_reasoning_budget_init(
            vocab,
            params.reasoning_budget_start,
@@ -661,6 +661,14 @@ uint32_t common_sampler_get_seed(const struct common_sampler * gsmpl) {
    return llama_sampler_get_seed(gsmpl->chain);
 }

+bool common_sampler_reasoning_budget_force(struct common_sampler * gsmpl) {
+    if (!gsmpl) {
+        return false;
+    }
+
+    return common_reasoning_budget_force(gsmpl->rbudget);
+}
+
 // helpers

 llama_token_data_array * common_sampler_get_candidates(struct common_sampler * gsmpl, bool do_sort) {
@@ -87,6 +87,9 @@ std::vector<llama_token> common_sampler_sample_and_accept_n(struct common_sample

 uint32_t common_sampler_get_seed(const struct common_sampler * gsmpl);

+// force the reasoning budget sampler (if any) to begin forcing its end sequence now.
+bool common_sampler_reasoning_budget_force(struct common_sampler * gsmpl);
+
 // helpers

 // access the internal list of current candidate tokens
@@ -1317,6 +1317,40 @@ static uint32_t common_get_enabled_speculative_configs(const std::vector<common_
    return result;
 }

+int32_t common_speculative_n_max(const common_params_speculative * spec) {
+    int32_t n_max = 0;
+
+    for (const auto type : spec->types) {
+        switch (type) {
+            case COMMON_SPECULATIVE_TYPE_DRAFT_SIMPLE:
+            case COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3:
+            case COMMON_SPECULATIVE_TYPE_DRAFT_MTP:
+                n_max = std::max(n_max, std::max(0, spec->draft.n_max));
+                break;
+            case COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE:
+                n_max = std::max(n_max, (int32_t) spec->ngram_simple.size_m);
+                break;
+            case COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K:
+                n_max = std::max(n_max, (int32_t) spec->ngram_map_k.size_m);
+                break;
+            case COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K4V:
+                n_max = std::max(n_max, (int32_t) spec->ngram_map_k4v.size_m);
+                break;
+            case COMMON_SPECULATIVE_TYPE_NGRAM_MOD:
+                n_max = std::max(n_max, std::max(0, spec->ngram_mod.n_max));
+                break;
+            case COMMON_SPECULATIVE_TYPE_NGRAM_CACHE:
+                n_max = std::max(n_max, (int32_t) 8);
+                break;
+            case COMMON_SPECULATIVE_TYPE_NONE:
+            case COMMON_SPECULATIVE_TYPE_COUNT:
+                break;
+        }
+    }
+
+    return n_max;
+}
+
 // initialization of the speculative decoding system
 //
 common_speculative * common_speculative_init(common_params_speculative & params, uint32_t n_seq) {
@@ -1325,8 +1359,6 @@ common_speculative * common_speculative_init(common_params_speculative & params,
    {
        uint32_t enabled_configs = common_get_enabled_speculative_configs(params.types);

-        bool has_draft_model_path = !params.draft.mparams.path.empty();
-
        bool has_draft_simple = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_DRAFT_SIMPLE));
        bool has_draft_eagle3 = false; // TODO PR-18039: if params.speculative.eagle3
        bool has_mtp = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_DRAFT_MTP)) && params.draft.ctx_dft != nullptr;
@@ -1359,16 +1391,6 @@ common_speculative * common_speculative_init(common_params_speculative & params,
        if (has_ngram_cache) {
            configs.push_back(common_speculative_config(COMMON_SPECULATIVE_TYPE_NGRAM_CACHE, params));
        }
-        if (has_draft_simple) {
-            if (!has_draft_model_path) {
-                LOG_WRN("%s: draft model is not specified - cannot use 'draft' type\n", __func__);
-                has_draft_simple = false;
-            }
-        } else if (has_draft_model_path && !has_mtp && !has_draft_eagle3) {
-            LOG_WRN("%s: draft model is specified but 'draft' speculative type is not explicitly enabled - enabling it\n", __func__);
-            has_draft_simple = true;
-        }
-
        if (has_draft_simple) {
            configs.push_back(common_speculative_config(COMMON_SPECULATIVE_TYPE_DRAFT_SIMPLE, params));
        }
@@ -20,6 +20,9 @@ enum common_speculative_type common_speculative_type_from_name(const std::string
 // convert type to string
 std::string common_speculative_type_to_str(enum common_speculative_type type);

+// return the max number of draft tokens based on the speculative parameters
+int32_t common_speculative_n_max(const common_params_speculative * spec);
+
 common_speculative * common_speculative_init(common_params_speculative & params, uint32_t n_seq);

 void common_speculative_free(common_speculative * spec);
@@ -47,6 +47,7 @@ TEXT_MODEL_MAP: dict[str, str] = {
    "DeepseekForCausalLM": "deepseek",
    "DeepseekV2ForCausalLM": "deepseek",
    "DeepseekV3ForCausalLM": "deepseek",
+    "DeepseekV32ForCausalLM": "deepseek",
    "DistilBertForMaskedLM": "bert",
    "DistilBertForSequenceClassification": "bert",
    "DistilBertModel": "bert",
@@ -57,6 +58,7 @@ TEXT_MODEL_MAP: dict[str, str] = {
    "Ernie4_5_ForCausalLM": "ernie",
    "Ernie4_5_MoeForCausalLM": "ernie",
    "EuroBertModel": "bert",
+    "Exaone4_5_ForConditionalGeneration": "exaone",
    "Exaone4ForCausalLM": "exaone",
    "ExaoneForCausalLM": "exaone",
    "ExaoneMoEForCausalLM": "exaone",
@@ -236,8 +238,10 @@ TEXT_MODEL_MAP: dict[str, str] = {
 MMPROJ_MODEL_MAP: dict[str, str] = {
    "AudioFlamingo3ForConditionalGeneration": "ultravox",
    "CogVLMForCausalLM": "cogvlm",
+    "DeepseekOCR2ForCausalLM": "deepseek",
    "DeepseekOCRForCausalLM": "deepseek",
    "DotsOCRForCausalLM": "dotsocr",
+    "Exaone4_5_ForConditionalGeneration": "exaone",
    "Gemma3ForConditionalGeneration": "gemma",
    "Gemma3nForConditionalGeneration": "gemma",
    "Gemma4ForConditionalGeneration": "gemma",
@@ -119,7 +119,8 @@ class ModelBase:
                 small_first_shard: bool = False, hparams: dict[str, Any] | None = None, remote_hf_model_id: str | None = None,
                 disable_mistral_community_chat_template: bool = False,
                 sentence_transformers_dense_modules: bool = False,
-                 fuse_gate_up_exps: bool = False):
+                 fuse_gate_up_exps: bool = False,
+                 fp8_as_q8: bool = False):
        if type(self) is ModelBase or \
                type(self) is TextModel or \
                type(self) is MmprojModel:
@@ -148,6 +149,8 @@ class ModelBase:
        self.dir_model_card = dir_model  # overridden in convert_lora_to_gguf.py
        self._is_nvfp4 = False
        self._is_mxfp4 = False
+        self._fp8_as_q8 = fp8_as_q8
+        self._fp8_dequantized: set[str] = set()

        # Apply heuristics to figure out typical tensor encoding based on first tensor's dtype
        # NOTE: can't use field "torch_dtype" in config.json, because some finetunes lie.
@@ -429,6 +432,8 @@ class ModelBase:
                        s = self.model_tensors[name]
                        self.model_tensors[weight_name] = lambda w=w, s=s, bs=block_size: dequant_simple(w(), s(), bs)
                        tensors_to_remove.append(name)
+                        if self._fp8_as_q8:
+                            self._fp8_dequantized.add(weight_name)
                    if name.endswith(".activation_scale"):  # unused
                        tensors_to_remove.append(name)
                    if name.endswith("_activation_scale"):  # Mistral-Small-4-119B-2602, unused
@@ -440,6 +445,8 @@ class ModelBase:
                        s = self.model_tensors[name]
                        self.model_tensors[weight_name] = lambda w=w, s=s, bs=block_size: dequant_simple(w(), s(), bs)
                        tensors_to_remove.append(name)
+                        if self._fp8_as_q8:
+                            self._fp8_dequantized.add(weight_name)
                    if name.endswith(".qscale_act"):
                        tensors_to_remove.append(name)
            elif quant_method == "gptq":
@@ -483,6 +490,11 @@ class ModelBase:
                    strategy = weight_config.get("strategy")
                    assert strategy == "channel" or strategy == "block"
                    assert weight_config.get("group_size") is None  # didn't find a model using this yet
+                    is_fp8 = (
+                        quant_format == "float-quantized"
+                        and weight_config.get("type") == "float"
+                        and weight_config.get("num_bits") == 8
+                    )
                    for name in self.model_tensors.keys():
                        if name.endswith(".weight_scale"):
                            weight_name = name.removesuffix("_scale")
@@ -490,6 +502,8 @@ class ModelBase:
                            s = self.model_tensors[name]
                            self.model_tensors[weight_name] = lambda w=w, s=s: dequant_simple(w(), s(), block_size)
                            tensors_to_remove.append(name)
+                            if self._fp8_as_q8 and is_fp8:
+                                self._fp8_dequantized.add(weight_name)
                elif quant_format == "pack-quantized":
                    assert weight_config.get("strategy") == "group"
                    assert weight_config.get("type", "int") == "int"
@@ -524,10 +538,18 @@ class ModelBase:
                for name in self.model_tensors.keys():
                    if name.endswith(".weight_scale"):
                        weight_name = name.removesuffix("_scale")
+                        if weight_name not in self.model_tensors:
+                            tensors_to_remove.append(name)
+                            continue
                        w = self.model_tensors[weight_name]
                        s = self.model_tensors[name]
+                        is_fp8_weight = False
+                        if self._fp8_as_q8:
+                            is_fp8_weight = w().dtype in (torch.float8_e4m3fn, torch.float8_e5m2)
                        self.model_tensors[weight_name] = lambda w=w, s=s: dequant_simple(w(), s(), None)
                        tensors_to_remove.append(name)
+                        if is_fp8_weight:
+                            self._fp8_dequantized.add(weight_name)
                    if name.endswith((".input_scale", ".k_scale", ".v_scale")):
                        tensors_to_remove.append(name)
            elif quant_method is not None:
@@ -615,8 +637,10 @@ class ModelBase:
        return [(new_name, data_torch)]

    def tensor_force_quant(self, name: str, new_name: str, bid: int | None, n_dims: int) -> gguf.GGMLQuantizationType | bool:
-        del name, new_name, bid, n_dims  # unused
-
+        del new_name, bid  # unused
+        # Force FP8-original tensors to Q8_0 when requested; Q8_0 is faster than F16/BF16.
+        if self._fp8_as_q8 and name in self._fp8_dequantized and n_dims >= 2:
+            return gguf.GGMLQuantizationType.Q8_0
        return False

    # some models need extra generated tensors (like rope_freqs)
@@ -791,7 +815,7 @@ class ModelBase:
        if quant_algo != "NVFP4":
            if nvfp4_compressed_tensors:
                quant_algo = "NVFP4"
-            elif any(v.get("quant_algo") == "NVFP4" for v in quant_layers.values() if isinstance(v, dict)):
+            elif any(str(v.get("quant_algo")).endswith("NVFP4") for v in quant_layers.values() if isinstance(v, dict)):
                quant_algo = "NVFP4"

        self._is_nvfp4 = quant_algo == "NVFP4"
@@ -891,6 +915,8 @@ class ModelBase:
                            gguf.MODEL_TENSOR.SSM_CONV1D_Q,
                            gguf.MODEL_TENSOR.SSM_CONV1D_K,
                            gguf.MODEL_TENSOR.SSM_CONV1D_V,
+                            # DSA indexer weights should be F32
+                            gguf.MODEL_TENSOR.INDEXER_PROJ,
                        )
                    )
                    or new_name[-7:] not in (".weight", ".lora_a", ".lora_b")
@@ -1114,7 +1140,7 @@ class TextModel(ModelBase):
        # Skip multimodal tensors
        if name.startswith(("mlp", "vit.", "vpm.", "siglip2.", "conformer.", "merger.", "resampler.", "sound_encoder.", "sound_projection.", "speech_embeddings.")) \
                or "visual." in name or "vision." in name or "audio." in name or "talker." in name \
-                or "vision_" in name or "audio_" in name or "sam_model" in name \
+                or "vision_" in name or "audio_" in name \
                or "token2wav." in name or "code2wav." in name \
                or "projector." in name or "pre_mm_projector_norm" in name \
                or "image_newline" in name or "view_seperator" in name \
@@ -1421,6 +1447,9 @@ class TextModel(ModelBase):
        if chkhsh == "0fe1cf6eda062318a1af7270f3331a85c539a01778ff948e24388e949c5282f4":
            # ref: https://huggingface.co/evilfreelancer/ruGPT3XL
            res = "gpt-2"
+        if chkhsh == "9e454714343b69b99b71795c1d27a68c2a1d15dab111f4d353109f966af29da7":
+            # ref: https://huggingface.co/LiquidAI/LFM2.5-8B-A1B
+            res = "lfm2"
        if chkhsh == "0ef9807a4087ebef797fc749390439009c3b9eda9ad1a097abbe738f486c01e5":
            # ref: https://huggingface.co/meta-llama/Meta-Llama-3-8B
            res = "llama-bpe"
@@ -1572,7 +1601,7 @@ class TextModel(ModelBase):
            # ref: https://huggingface.co/K-intelligence/Midm-2.0-Base-Instruct
            res = "midm-2.0"
        if chkhsh == "169bf0296a13c4d9b7672313f749eb36501d931022de052aad6e36f2bf34dd51":
-            # ref: https://huggingface.co/LiquidAI/LFM2-Tokenizer
+            # ref: https://huggingface.co/LiquidAI/LFM2.5-350M
            res = "lfm2"
        if chkhsh == "2085e1638f6c377a0aa4ead21b27bb4cb941bf800df86ed391011769c1758dfb":
            # ref: https://huggingface.co/LGAI-EXAONE/EXAONE-4.0-32B
@@ -1663,6 +1692,16 @@ class TextModel(ModelBase):
        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
        special_vocab.add_to_gguf(self.gguf_writer)

+    def _set_vocab_whitespace(self) -> None:
+        tokens, toktypes, _ = self.get_vocab_base()
+        self.gguf_writer.add_tokenizer_model("whitespace")
+        self.gguf_writer.add_tokenizer_pre("whitespace") # pinned, not hash-detected: chktxt hash collides with jina-v1-en
+        self.gguf_writer.add_token_list(tokens)
+        self.gguf_writer.add_token_types(toktypes)
+
+        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
+        special_vocab.add_to_gguf(self.gguf_writer)
+
    def _set_vocab_hybriddna(self):
        from transformers import AutoTokenizer
        tokenizer = AutoTokenizer.from_pretrained(self.dir_model, trust_remote_code=True)
@@ -2417,10 +2456,9 @@ class MmprojModel(ModelBase):
        raise KeyError(f"could not find any of: {keys}")

    def tensor_force_quant(self, name, new_name, bid, n_dims):
-        del bid, name, n_dims  # unused
        if ".patch_embd.weight" in new_name or ".patch_merger.weight" in new_name:
            return gguf.GGMLQuantizationType.F16 if self.ftype == gguf.LlamaFileType.MOSTLY_F16 else gguf.GGMLQuantizationType.F32
-        return False
+        return super().tensor_force_quant(name, new_name, bid, n_dims)


 class LazyTorchTensor(gguf.LazyBase):
@@ -2555,7 +2593,7 @@ def get_model_architecture(hparams: dict[str, Any], model_type: ModelType) -> st
    # Step3-VL keeps text config under text_config but uses a custom top-level architecture.
    # For text conversion we route to a dedicated text-only class.
    # TODO: refactor this later to avoid adding exception here
-    if model_type == ModelType.TEXT and arch in ("StepVLForConditionalGeneration", "Sarashina2VisionForCausalLM"):
+    if model_type == ModelType.TEXT and arch in ("StepVLForConditionalGeneration", "Sarashina2VisionForCausalLM", "Exaone4_5_ForConditionalGeneration"):
        return arch

    # if "architectures" is found in the sub-config, use that instead
@@ -571,7 +571,16 @@ class JinaBertV2Model(BertModel):
        if tokenizer_class == 'BertTokenizer':
            super().set_vocab()
        elif tokenizer_class == 'RobertaTokenizer':
-            self._set_vocab_gpt2()
+            pre_tokenizer_type = None
+            tokenizer_json_path = self.dir_model / "tokenizer.json"
+            if tokenizer_json_path.is_file():
+                with open(tokenizer_json_path, "r", encoding="utf-8") as f:
+                    pre_tokenizer_type = json.load(f).get("pre_tokenizer", {}).get("type")
+
+            if pre_tokenizer_type == "Whitespace":
+                self._set_vocab_whitespace()
+            else:
+                self._set_vocab_gpt2()
            self.gguf_writer.add_token_type_count(2)
        else:
            raise NotImplementedError(f'Tokenizer {tokenizer_class} is not supported for JinaBertModel')
@@ -16,10 +16,14 @@ from .qwen import QwenModel

@ModelBase.register("DeepseekOCRForCausalLM")
 class DeepseekOCRVisionModel(MmprojModel):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.clip_projector_type = gguf.VisionProjectorType.DEEPSEEKOCR
+
    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        hparams = self.hparams
-        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.DEEPSEEKOCR)
+        self.gguf_writer.add_clip_projector_type(self.clip_projector_type)
        # default values below are taken from HF tranformers code
        self.gguf_writer.add_vision_attention_layernorm_eps(hparams.get("layer_norm_eps", 1e-6))
        self.gguf_writer.add_vision_use_gelu(True)
@@ -49,22 +53,27 @@ class DeepseekOCRVisionModel(MmprojModel):
            raise ValueError("DeepseekOCR model requires 'vision_config' in the model configuration, but it was not found")

        vision_config['sam'] = vision_config['width']['sam_vit_b']
-        vision_config.update(vision_config['width']['clip-l-14-224'])
-        vision_config['hidden_size'] = vision_config['width']
-        vision_config['num_heads'] = vision_config['heads']
-        vision_config['intermediate_size'] = vision_config['heads'] * 4
+        if vision_config['width'].get('clip-l-14-224') is not None:
+            vision_config.update(vision_config['width']['clip-l-14-224'])
+        if isinstance(vision_config['width'], int):
+            vision_config['hidden_size'] = vision_config['width']
+        if vision_config.get('heads') is not None:
+            vision_config['num_heads'] = vision_config['heads']
+            vision_config['intermediate_size'] = vision_config['heads'] * 4

        return vision_config

    def tensor_force_quant(self, name, new_name, bid, n_dims):
-        if ".embeddings." in name or 'pos_embed' in name:
-            return gguf.GGMLQuantizationType.F32
-        if ".rel_pos_h" in name or '.rel_pos_w' in name:
-            return gguf.GGMLQuantizationType.F32
-        if ".neck." in name or ".net_" in name:
-            return gguf.GGMLQuantizationType.F32
+        for nq_name in ('.embeddings.', 'pos_embed', '.rel_pos_h', '.rel_pos_w', '.neck.', '.net_'):
+            if nq_name in name:
+                return gguf.GGMLQuantizationType.F32
        return super().tensor_force_quant(name, new_name, bid, n_dims)

+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        if name.endswith("view_seperator"):
+            data_torch = data_torch.unsqueeze(0)
+        yield from super().modify_tensors(data_torch, name, bid)
+
    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item
@@ -81,6 +90,33 @@ class DeepseekOCRVisionModel(MmprojModel):
        return super().filter_tensors((name, gen))


+@ModelBase.register("DeepseekOCR2ForCausalLM")
+class DeepseekOCR2VisionModel(DeepseekOCRVisionModel):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.clip_projector_type = gguf.VisionProjectorType.DEEPSEEKOCR2
+
+    def set_gguf_parameters(self):
+        # the vision tower's qwen2 encoder is built from fixed defaults,
+        # see build_qwen2_decoder_as_encoder() in deepencoderv2.py
+        if self.hparams.get("patch_size") is None:
+            self.hparams["patch_size"] = 16
+        if self.hparams.get("intermediate_size") is None:
+            self.hparams["intermediate_size"] = 4864
+        if self.hparams.get("num_attention_heads") is None:
+            self.hparams["num_attention_heads"] = 14
+        super().set_gguf_parameters()
+        # qwen2 encoder is GQA: 14 Q heads, 2 KV heads
+        self.gguf_writer.add_vision_head_count_kv(2)
+
+    def get_vision_config(self) -> dict[str, Any]:
+        vision_config = super().get_vision_config()
+        vision_config['hidden_size'] = vision_config['width']['qwen2-0-5b']['dim']
+        if vision_config.get('layers') is None:
+            vision_config['layers'] = 24
+        return vision_config
+
+
@ModelBase.register("DeepseekForCausalLM")
 class DeepseekModel(TextModel):
    model_arch = gguf.MODEL_ARCH.DEEPSEEK
@@ -188,13 +224,21 @@ class DeepseekV2Model(TextModel):
        self.origin_hf_arch = hparams.get('architectures', [None])[0]

        # special handling for Deepseek OCR
-        if self.origin_hf_arch == "DeepseekOCRForCausalLM":
+        if self.origin_hf_arch in ("DeepseekOCRForCausalLM", "DeepseekOCR2ForCausalLM"):
            self.model_arch = gguf.MODEL_ARCH.DEEPSEEK2OCR
            self.gguf_writer.arch = gguf.MODEL_ARCH_NAMES[self.model_arch]
            self.gguf_writer.add_architecture()
            # default jinja template
            self.gguf_writer.add_chat_template("{% for m in messages %}{{m['content']}}{% endfor %}")

+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, _ = item
+        # DeepSeek-OCR vision encoder (SAM + DeepSeek-OCR-2 qwen2 tower)
+        if "sam_model" in name or "qwen2_model" in name:
+            return None
+        return super().filter_tensors(item)
+
    def set_vocab(self):
        try:
            self._set_vocab_gpt2()
@@ -386,3 +430,32 @@ class DeepseekV2Model(TextModel):
            experts = [k for d in self._experts for k in d.keys()]
            if len(experts) > 0:
                raise ValueError(f"Unprocessed experts: {experts}")
+
+
+@ModelBase.register("DeepseekV32ForCausalLM")
+class DeepseekV32Model(DeepseekV2Model):
+    model_arch = gguf.MODEL_ARCH.DEEPSEEK32
+    skip_mtp = False
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.block_count = self.hparams["num_hidden_layers"] + self.hparams.get("num_nextn_predict_layers", 0)
+        self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
+
+    def set_vocab(self):
+        from transformers import AutoTokenizer
+        tokenizer = AutoTokenizer.from_pretrained(self.dir_model)
+        assert getattr(tokenizer, "add_bos_token", False), "Change value of add_bos_token to true in tokenizer_config.json file."
+        self._set_vocab_gpt2()
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+
+        # NextN/MTP prediction layers
+        if (num_nextn_predict_layers := self.hparams.get("num_nextn_predict_layers")) is not None:
+            self.gguf_writer.add_nextn_predict_layers(num_nextn_predict_layers)
+
+        # DSA indexer parameters
+        self.gguf_writer.add_indexer_head_count(self.hparams["index_n_heads"])
+        self.gguf_writer.add_indexer_key_length(self.hparams["index_head_dim"])
+        self.gguf_writer.add_indexer_top_k(self.hparams["index_topk"])
@@ -3,14 +3,15 @@ from __future__ import annotations
 import math

 from pathlib import Path
-from typing import Iterable, TYPE_CHECKING
+from typing import Callable, Iterable, TYPE_CHECKING

 import torch

 if TYPE_CHECKING:
    from torch import Tensor

-from .base import ModelBase, TextModel, gguf
+from .base import MmprojModel, ModelBase, TextModel, gguf
+from .qwenvl import Qwen2VLVisionModel


@ModelBase.register("ExaoneForCausalLM")
@@ -208,3 +209,97 @@ class ExaoneMoEModel(Exaone4Model):
            experts = [k for d in self._experts for k in d.keys()]
            if len(experts) > 0:
                raise ValueError(f"Unprocessed experts: {experts}")
+
+
+@ModelBase.register("Exaone4_5_ForConditionalGeneration")
+class Exaone4_5_TextModel(Exaone4Model):
+    """Text tower of EXAONE 4.5; Tensors match EXAONE4"""
+
+    model_arch = gguf.MODEL_ARCH.EXAONE4
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        n_nextn = int(self.hparams.get("num_nextn_predict_layers", 0) or 0)
+        if n_nextn > 0:
+            self.block_count = self.hparams["num_hidden_layers"] + n_nextn
+            self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        n_nextn = int(self.hparams.get("num_nextn_predict_layers", 0) or 0)
+        if n_nextn > 0:
+            self.gguf_writer.add_nextn_predict_layers(n_nextn)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        if name.startswith("mtp."):
+            n_nextn = int(self.hparams.get("num_nextn_predict_layers", 0) or 0)
+            if n_nextn <= 0:
+                return
+            nh = self.hparams["num_hidden_layers"]
+            if ".layers." in name:
+                share = self.hparams.get("mtp_share_layers", False)
+                mtp_bid = bid if bid is not None else 0
+                if share:
+                    for k in range(n_nextn):
+                        nn = name.replace(f"mtp.layers.{mtp_bid}", f"model.layers.{nh + k}")
+                        yield from super().modify_tensors(data_torch, nn, nh + k)
+                    return
+                name = name.replace(f"mtp.layers.{mtp_bid}", f"model.layers.{mtp_bid + nh}")
+            else:
+                remapper = {
+                    "mtp.fc": gguf.MODEL_TENSOR.NEXTN_EH_PROJ,
+                    "mtp.pre_fc_norm_embedding": gguf.MODEL_TENSOR.NEXTN_ENORM,
+                    "mtp.pre_fc_norm_hidden": gguf.MODEL_TENSOR.NEXTN_HNORM,
+                    "mtp.norm": gguf.MODEL_TENSOR.NEXTN_SHARED_HEAD_NORM,
+                }
+                _n = Path(name)
+                key = _n.stem
+                if key not in remapper:
+                    return
+                for bid_mtp in range(nh, self.block_count):
+                    mapped_name = self.format_tensor_name(remapper[key], bid_mtp, suffix=_n.suffix)
+                    yield from ModelBase.modify_tensors(self, data_torch, mapped_name, bid_mtp)
+                return
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+
+@ModelBase.register("Exaone4_5_ForConditionalGeneration")
+class Exaone4_5VisionModel(Qwen2VLVisionModel):
+    """Vision tower for EXAONE 4.5; Qwen2-VL-style ViT (GQA) + patch merger"""
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+        name = name.replace("model.visual.", "visual.", 1)
+        return super().filter_tensors((name, gen))
+
+    def set_gguf_parameters(self):
+        MmprojModel.set_gguf_parameters(self)
+        assert self.hparams_vision is not None
+        hparams = self.hparams_vision
+        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.EXAONE4_5)
+        self.gguf_writer.add_vision_use_silu(True)
+        self.gguf_writer.add_vision_min_pixels(self.preprocessor_config["min_pixels"])
+        self.gguf_writer.add_vision_max_pixels(self.preprocessor_config["max_pixels"])
+        num_kv_head = self.find_vparam(["num_key_value_heads"], optional=True)
+        if num_kv_head is not None:
+            self.gguf_writer.add_vision_head_count_kv(num_kv_head)
+        eps = hparams.get("rms_norm_eps", self.global_config.get("rms_norm_eps", 1e-6))
+        self.gguf_writer.add_vision_attention_layernorm_eps(eps)
+        if (window_size := hparams.get("window_size")) is not None:
+            self.gguf_writer.add_vision_window_size(window_size)
+        fullatt_block_indexes = hparams.get("fullatt_block_indexes")
+        if fullatt_block_indexes:
+            n_wa_pattern = fullatt_block_indexes[0] + 1
+            for i in range(1, len(fullatt_block_indexes)):
+                if fullatt_block_indexes[i] - fullatt_block_indexes[i - 1] != n_wa_pattern:
+                    raise ValueError(f"Invalid EXAONE4.5 fullatt_block_indexes: {fullatt_block_indexes}")
+            self.gguf_writer.add_vision_n_wa_pattern(n_wa_pattern)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        if ".qkv." in name:
+            yield from ModelBase.modify_tensors(self, data_torch, name, bid)
+            return
+
+        yield from Qwen2VLVisionModel.modify_tensors(self, data_torch, name, bid)
@@ -786,14 +786,15 @@ class Gemma4VisionAudioModel(MmprojModel):
        super().set_gguf_parameters()

        # vision params
+        assert self.hparams_vision is not None
        self.gguf_writer.add_clip_vision_projector_type(gguf.VisionProjectorType.GEMMA4V)
-        self.gguf_writer.add_vision_attention_layernorm_eps(self.hparams.get("layer_norm_eps", 1e-6))
+        self.gguf_writer.add_vision_attention_layernorm_eps(self.hparams_vision.get("layer_norm_eps", 1e-6))

        # audio params
-        if self.hparams_audio:
-            self.gguf_writer.add_clip_audio_projector_type(gguf.VisionProjectorType.GEMMA4A)
-            self.gguf_writer.add_audio_num_mel_bins(self.hparams_audio["feat_in"])
-            self.gguf_writer.add_audio_attention_layernorm_eps(1e-5)
+        assert self.hparams_audio is not None
+        self.gguf_writer.add_clip_audio_projector_type(gguf.VisionProjectorType.GEMMA4A)
+        self.gguf_writer.add_audio_num_mel_bins(self.hparams_audio["feat_in"])
+        self.gguf_writer.add_audio_attention_layernorm_eps(self.hparams_audio.get("layer_norm_eps", 1e-6))

    def is_audio_tensor(self, name: str) -> bool:
        return "audio_tower" in name or "embed_audio" in name
@@ -148,6 +148,10 @@ def parse_args() -> argparse.Namespace:
        "--fuse-gate-up-exps", action="store_true",
        help="Fuse gate_exps and up_exps tensors into a single gate_up_exps tensor for MoE models.",
    )
+    parser.add_argument(
+        "--fp8-as-q8", action="store_true",
+        help="Store tensors dequantized from FP8 as Q8_0 instead of BF16/F16.",
+    )

    args = parser.parse_args()
    if not args.print_supported_models and args.model is None:
@@ -264,7 +268,8 @@ def main() -> None:
                                     small_first_shard=args.no_tensor_first_split,
                                     remote_hf_model_id=hf_repo_id, disable_mistral_community_chat_template=disable_mistral_community_chat_template,
                                     sentence_transformers_dense_modules=args.sentence_transformers_dense_modules,
-                                     fuse_gate_up_exps=args.fuse_gate_up_exps
+                                     fuse_gate_up_exps=args.fuse_gate_up_exps,
+                                     fp8_as_q8=args.fp8_as_q8,
                                     )

        if args.vocab_only:
@@ -139,7 +139,7 @@ models = [
    {"name": "seed-coder",       "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/ByteDance-Seed/Seed-Coder-8B-Base", },
    {"name": "a.x-4.0",          "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/skt/A.X-4.0", },
    {"name": "midm-2.0",         "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/K-intelligence/Midm-2.0-Base-Instruct", },
-    {"name": "lfm2",             "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LiquidAI/LFM2-Tokenizer"},
+    {"name": "lfm2",             "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LiquidAI/LFM2.5-350M", },
    {"name": "exaone4",          "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LGAI-EXAONE/EXAONE-4.0-32B", },
    {"name": "mellum",           "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/JetBrains/Mellum-4b-base", },
    {"name": "modern-bert",      "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/answerdotai/ModernBERT-base", },
@@ -183,6 +183,8 @@ pre_computed_hashes = [
    # jina-v2-de variants
    {"name": "jina-v2-de", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/aari1995/German_Semantic_V3", "chkhsh": "b3d1dd861f1d4c5c0d2569ce36baf3f90fe8a102db3de50dd71ff860d91be3df"},
    {"name": "gpt-2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/evilfreelancer/ruGPT3XL", "chkhsh": "0fe1cf6eda062318a1af7270f3331a85c539a01778ff948e24388e949c5282f4"},
+    # lfm2 variants
+    {"name": "lfm2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LiquidAI/LFM2.5-8B-A1B", "chkhsh": "9e454714343b69b99b71795c1d27a68c2a1d15dab111f4d353109f966af29da7"},
 ]


@@ -8,7 +8,7 @@
 - [Performance Reference](#performance-reference)
 - [Docker](#docker)
 - [Linux](#linux)
- [Windows](#windows)
+- [Windows](#windows-1)
 - [Environment Variable](#environment-variable)
 - [Design Rule](#design-rule)
 - [Known Issue](#known-issues)
@@ -72,10 +72,13 @@ The ZenDNN backend accelerates **matrix multiplication (MUL_MAT)** and **expert-
 |:----------------------:|:-------:|:---------------------------------------------:|
 | FP32                   | Support | Full precision floating point                 |
 | BF16                   | Support | BFloat16 (best performance on Zen 4/Zen 5)    |
+| Q8_0                   | Support | 8-bit quantized weights via [dynamic quantization](https://github.com/amd/ZenDNN/blob/main/docs/operator/lowoha_matmul_operator.md) |

 *Notes:*

 - **BF16** provides best performance on Zen 4 and Zen 5 EPYC™ processors (Genoa, Turin).
+- **Q8_0** is available for quantized model weights since ZenDNN supports dynamic quantization [LowOHA MatMul operator](https://github.com/amd/ZenDNN/blob/main/docs/operator/lowoha_matmul_operator.md).
+- Other quantization formats fall back to the standard CPU backend unless explicitly supported by the ZenDNN backend.

 ## Linux

@@ -140,6 +143,15 @@ Download LLaMA 3.1 8B Instruct BF16 model:
 huggingface-cli download meta-llama/Llama-3.1-8B-Instruct-GGUF --local-dir models/
 ```

+You can also use a Q8_0 GGUF model:
+
+```sh
+# Download a Q8_0 GGUF model from Hugging Face
+huggingface-cli download meta-llama/Llama-3.1-8B-Instruct-GGUF \
+    Llama-3.1-8B-Instruct-Q8_0.gguf \
+    --local-dir models/
+```
+
 #### 2. Start Server

 Run llama.cpp server with ZenDNN acceleration:
@@ -176,6 +188,10 @@ export ZENDNNL_MATMUL_ALGO=1    # Blocked AOCL DLP algo (recommended)

 For more details on available algorithms, see the [ZenDNN MatMul Algorithm Documentation](https://github.com/amd/ZenDNN/blob/a18adf8c605fb5f5e52cefd7eda08a7b18febbaf/docs/runtime_env.md#algorithm-details).

+### Q8_0 Performance Notes
+
+Q8_0 support is mainly beneficial for prompt processing / prefill workloads where large matrix multiplications dominate execution. Token generation performance may remain close to the standard CPU backend depending on the model, batch size, number of threads, and CPU topology.
+
 ### Profiling and Debugging

 For detailed profiling and logging options, refer to the [ZenDNN Logging Documentation](https://github.com/amd/ZenDNN/blob/a18adf8c605fb5f5e52cefd7eda08a7b18febbaf/docs/logging.md).
@@ -184,6 +200,7 @@ For detailed profiling and logging options, refer to the [ZenDNN Logging Documen

 - **Limited operation support**: Currently matrix multiplication (MUL_MAT) and expert-based matrix multiplication (MUL_MAT_ID) are accelerated via ZenDNN. Other operations fall back to the standard CPU backend. Future updates may expand supported operations.
 - **BF16 support**: BF16 operations require AMD Zen 4 or Zen 5 architecture (EPYC 9004/9005 series). On older CPUs, operations will use FP32.
+- **Q8_0 support scope**: Q8_0 acceleration is available for supported matrix multiplication paths. Other quantization formats still fall back to the standard CPU backend.
 - **NUMA awareness**: For multi-socket systems, manual NUMA binding may be required for optimal performance.

 ## Q&A
@@ -202,7 +219,7 @@ A: ZenDNN is optimized specifically for AMD processors. While it may work on oth

 **Q: Does ZenDNN support quantized models?**

-A: Currently, ZenDNN primarily supports FP32 and BF16 data types. Quantized model support is not available at this time.
+A: Yes. The ZenDNN backend supports Q8_0 quantized models for supported matrix multiplication operations. FP32 and BF16 are also supported. Other quantization formats may fall back to the standard CPU backend unless explicitly supported by the ZenDNN backend.

 **Q: Why is my inference not faster with ZenDNN?**

@@ -22,6 +22,7 @@ The following sections describe how to build with different backends and options
 * [HIP](#hip)
 * [Vulkan](#vulkan)
 * [CANN](#cann)
+* [ZenDNN](#zendnn)
 * [Arm® KleidiAI™](#arm-kleidiai)
 * [OpenCL](#opencl)
 * [Android](#android-1)
@@ -55,7 +55,7 @@ Legend:
 |                             GELU | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                         GELU_ERF | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                       GELU_QUICK | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ |
-|                         GET_ROWS | ❌ | 🟡 | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ❌ | ❌ |
+|                         GET_ROWS | ❌ | 🟡 | ✅ | 🟡 | 🟡 | 🟡 | ✅ | ✅ | 🟡 | ❌ | ❌ |
 |                    GET_ROWS_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
 |                       GROUP_NORM | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                      HARDSIGMOID | ❌ | ✅ | ✅ | 🟡 | ✅ | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
@@ -323,3 +323,8 @@ statistics ngram_map_k: #calls(b,g,a) = 6 1690 26, #gen drafts = 26, #acc drafts
 - `#gen tokens`: number of tokens generated by this implementation (including rejected tokens)
 - `#acc tokens`: number of tokens accepted by the main model
 - `dur(b,g,a): durations of begin (new prompt), generation and accumulation (process acceptance).
+
+## Benchmarking
+
+To measure the end-to-end effect of speculative decoding (throughput, latency, and draft acceptance) across diverse prompts, see the SPEED-Bench client in [tools/server/bench/speed-bench](../tools/server/bench/speed-bench/README.md).
+It runs against a running `llama-server` and can compare a baseline run against a speculative-decoding run.
@@ -5,7 +5,7 @@ project("ggml" C CXX ASM)
 ### GGML Version
 set(GGML_VERSION_MAJOR 0)
 set(GGML_VERSION_MINOR 13)
-set(GGML_VERSION_PATCH 0)
+set(GGML_VERSION_PATCH 1)
 set(GGML_VERSION_BASE "${GGML_VERSION_MAJOR}.${GGML_VERSION_MINOR}.${GGML_VERSION_PATCH}")

 list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/cmake/")
@@ -381,11 +381,15 @@ extern "C" {
        //   - most tensors have n_segments == 1 and a contiguous slice of the tensor data
        //   - some tensors have an inhomogenenous data layout along the split axis,
        //     those tensors are divided into segments which are each individually split across devices
-        //   - ne has one entry per segment and device that add up to ggml_tensor::ne for that axis,
-        //     the outer/inner loops are over segments/devices like [seg0_dev0, seg0_dev1, seg1_dev0, seg1_dev1],
+        //   - ne has one entry per segment and device and that segment repeats nr times,
+        //     in total when accounting for repetitions the segments add up to ggml_tensor::ne for that axis,
+        //     the outer/inner loops are over segments/devices like [seg0_dev0_r0, seg0_dev1_r0, seg0_dev0_r1, seg0_dev1_r1, seg1_dev0_r0, seg1_dev1_r0],
        //   - for example, a transformer may have a fused QKV matrix rather than 3 matrices, those would be 3 separate segments
-        //     that each need to be split individually across devices so that each device gets a slice of Q, K, and V
+        //     that each need to be split individually across devices so that each device gets a slice of Q, K, and V,
+        //     the Q matrix can be larger than the K and V matrices so this can either be expressed as 3 segments or as 2 segments
+        //     where the segment for K/V repeats twice
        int64_t  ne[16*GGML_BACKEND_META_MAX_DEVICES];
+        uint32_t nr[16];
        uint32_t n_segments;
    };

@@ -487,6 +487,9 @@ static struct ggml_backend_meta_split_state ggml_backend_meta_get_split_state(co

 static struct ggml_backend_meta_split_state ggml_backend_meta_get_split_state(
        ggml_backend_meta_simple_tensor_container & stc, const struct ggml_tensor * tensor, bool assume_sync) {
+    // FIXME Currently this function preserves/erases the information in n_segments and nr in an inconsistent way.
+    // Since the operations in question are developed specifically for llama.cpp this currently does not manifest as a bug there.
+    // However, in a broader ggml context with arbitrary ggml graphs this can lead to unexpected results.
    const size_t n_bufs = ggml_backend_meta_buffer_n_bufs(tensor->buffer);
    ggml_backend_meta_buffer_context * buf_ctx = (ggml_backend_meta_buffer_context *) tensor->buffer->context;

@@ -497,11 +500,11 @@ static struct ggml_backend_meta_split_state ggml_backend_meta_get_split_state(
        for (size_t j = 0; j < n_bufs; j++) {
            int64_t sum_a = 0;
            for (size_t s = 0; s < a.n_segments; s++) {
-                sum_a += a.ne[s*n_bufs + j];
+                sum_a += a.ne[s*n_bufs + j] * a.nr[s];
            }
            int64_t sum_b = 0;
            for (size_t s = 0; s < b.n_segments; s++) {
-                sum_b += b.ne[s*n_bufs + j];
+                sum_b += b.ne[s*n_bufs + j] * b.nr[s];
            }
            if (sum_a != sum_b) {
                return false;
@@ -511,7 +514,7 @@ static struct ggml_backend_meta_split_state ggml_backend_meta_get_split_state(
    };

    auto handle_generic = [&](const std::vector<ggml_backend_meta_split_state> & src_ss, bool scalar_only) -> ggml_backend_meta_split_state {
-        ggml_backend_meta_split_state ret = {GGML_BACKEND_SPLIT_AXIS_NONE, {0}, 1};
+        ggml_backend_meta_split_state ret = {GGML_BACKEND_SPLIT_AXIS_NONE, {0}, {1}, 1};
        for (size_t i = 0; i < GGML_MAX_SRC; i++) {
            if (tensor->src[i] == nullptr || tensor->src[i] == tensor) {
                continue;
@@ -519,15 +522,15 @@ static struct ggml_backend_meta_split_state ggml_backend_meta_get_split_state(
            if (ret.axis == GGML_BACKEND_SPLIT_AXIS_NONE) {
                ret = src_ss[i];
            } else if (!split_states_equal(src_ss[i], ret)) {
-                ret = {GGML_BACKEND_SPLIT_AXIS_UNKNOWN, {0}, 1};
+                ret = {GGML_BACKEND_SPLIT_AXIS_UNKNOWN, {0}, {1}, 1};
                break;
            }
        }
        if (ret.axis == GGML_BACKEND_SPLIT_AXIS_NONE) {
-            ret = {GGML_BACKEND_SPLIT_AXIS_UNKNOWN, {0}, 1};
+            ret = {GGML_BACKEND_SPLIT_AXIS_UNKNOWN, {0}, {1}, 1};
        }
        if (scalar_only && ret.axis >= 0 && ret.axis < GGML_MAX_DIMS) {
-            ret = {GGML_BACKEND_SPLIT_AXIS_UNKNOWN, {0}, 1};
+            ret = {GGML_BACKEND_SPLIT_AXIS_UNKNOWN, {0}, {1}, 1};
        }
        GGML_ASSERT(ret.axis != GGML_BACKEND_SPLIT_AXIS_UNKNOWN);
        return ret;
@@ -571,42 +574,24 @@ static struct ggml_backend_meta_split_state ggml_backend_meta_get_split_state(

    auto handle_mul_mat = [&](const std::vector<ggml_backend_meta_split_state> & src_ss) -> ggml_backend_meta_split_state {
        if (src_ss[0].axis == GGML_BACKEND_SPLIT_AXIS_MIRRORED && src_ss[1].axis == GGML_BACKEND_SPLIT_AXIS_MIRRORED) {
-            return {GGML_BACKEND_SPLIT_AXIS_MIRRORED, {0}, 1};
+            return {GGML_BACKEND_SPLIT_AXIS_MIRRORED, {0}, {1}, 1};
        }
        if (src_ss[0].axis == GGML_BACKEND_SPLIT_AXIS_1 && src_ss[1].axis == GGML_BACKEND_SPLIT_AXIS_MIRRORED) {
            ggml_backend_meta_split_state ret = src_ss[0];
            ret.axis = GGML_BACKEND_SPLIT_AXIS_0;
+            ret.nr[0] = 1;
            ret.n_segments = 1;
            return ret;
        }
        if (src_ss[1].axis == GGML_BACKEND_SPLIT_AXIS_1 && src_ss[0].axis == GGML_BACKEND_SPLIT_AXIS_MIRRORED) {
-            ggml_backend_meta_split_state ret = src_ss[1];
-            ret.n_segments = 1;
-            return ret;
+            return src_ss[1];
        }
        if (src_ss[0].axis == GGML_BACKEND_SPLIT_AXIS_0 && src_ss[1].axis == GGML_BACKEND_SPLIT_AXIS_0) {
            GGML_ASSERT(split_states_equal(src_ss[0], src_ss[1]));
-            return {assume_sync ? GGML_BACKEND_SPLIT_AXIS_MIRRORED : GGML_BACKEND_SPLIT_AXIS_PARTIAL, {0}, 1};
+            return {assume_sync ? GGML_BACKEND_SPLIT_AXIS_MIRRORED : GGML_BACKEND_SPLIT_AXIS_PARTIAL, {0}, {1}, 1};
        }
        GGML_ABORT("fatal error");
-        //return {GGML_BACKEND_SPLIT_AXIS_UNKNOWN, {0}, 1};
-    };
-
-    auto handle_cpy = [&](const std::vector<ggml_backend_meta_split_state> & src_ss) -> ggml_backend_meta_split_state {
-        if (src_ss[0].axis >= 0 && src_ss[0].axis < GGML_MAX_DIMS) {
-            int64_t ne_split_src = tensor->src[0]->ne[0];
-            for (int dim = 1; dim <= src_ss[0].axis; dim++) {
-                ne_split_src *= tensor->src[0]->ne[dim];
-            }
-            int64_t ne_split_dst = 1;
-            for (int dim = 0; dim < GGML_MAX_DIMS; dim++) {
-                ne_split_dst *= tensor->ne[dim];
-                if (ne_split_dst == ne_split_src) {
-                    return {ggml_backend_meta_split_axis(dim), {0}, 1};
-                }
-            }
-        }
-        return handle_generic(src_ss, /*scalar_only =*/ false);
+        //return {GGML_BACKEND_SPLIT_AXIS_UNKNOWN, {0}, {1}, 1};
    };

    auto handle_reshape = [&](const std::vector<ggml_backend_meta_split_state> & src_ss) -> ggml_backend_meta_split_state {
@@ -615,33 +600,25 @@ static struct ggml_backend_meta_split_state ggml_backend_meta_get_split_state(
            case GGML_BACKEND_SPLIT_AXIS_1:
            case GGML_BACKEND_SPLIT_AXIS_2:
            case GGML_BACKEND_SPLIT_AXIS_3: {
-                GGML_ASSERT(!ggml_is_permuted(tensor) && !ggml_is_permuted(tensor->src[0]));
-                if (src_ss[0].axis == ggml_n_dims(tensor->src[0]) - 1) {
-                    return {ggml_backend_meta_split_axis(ggml_n_dims(tensor) - 1), {0}, 1};
+                GGML_ASSERT(src_ss[0].n_segments == 1);
+                if (src_ss[0].axis == ggml_n_dims(tensor->src[0]) - 1 && src_ss[0].nr[0] == 1) {
+                    return {ggml_backend_meta_split_axis(ggml_n_dims(tensor) - 1), {0}, {1}, 1};
                }
-                std::vector<int64_t> base_ne_in;
-                base_ne_in.reserve(GGML_MAX_DIMS - src_ss[0].axis);
-                {
-                    base_ne_in.push_back(1);
-                    int dim = 0;
-                    for (; dim <= src_ss[0].axis; dim++) {
-                        base_ne_in[0] *= tensor->src[0]->ne[dim];
-                    }
-                    for (; dim <= GGML_MAX_DIMS; dim++) {
-                        base_ne_in.push_back(base_ne_in.back() * tensor->src[0]->ne[dim]);
-                    }
+                int64_t base_ne_in = tensor->src[0]->ne[0];
+                for (int dim = 1; dim <= src_ss[0].axis; dim++) {
+                    base_ne_in *= tensor->src[0]->ne[dim];
                }
+                base_ne_in /= src_ss[0].nr[0];
                int64_t base_ne_out = 1;
                for (int dim = 0; dim < GGML_MAX_DIMS; dim++) {
                    const int64_t base_ne_out_next = base_ne_out *= tensor->ne[dim];
-                    for (const int64_t & bni : base_ne_in) {
-                        if (bni == base_ne_out_next) {
-                            return {ggml_backend_meta_split_axis(dim), {0}, 1};
-                        }
+                    if (base_ne_out_next % base_ne_in == 0) {
+                        return {ggml_backend_meta_split_axis(dim), {0}, {uint32_t(base_ne_out_next/base_ne_in)}, 1};
                    }
-                    if (base_ne_out_next > base_ne_in[0]) {
-                        GGML_ASSERT(dim + 1 < GGML_MAX_DIMS);
-                        return {ggml_backend_meta_split_axis(dim + 1), {0}, 1};
+                    if (base_ne_out_next > base_ne_in) {
+                        GGML_ASSERT(src_ss[0].n_segments == 1);
+                        GGML_ASSERT(src_ss[0].nr[0]      == 1);
+                        return {ggml_backend_meta_split_axis(dim), {0}, {1}, 1};
                    }
                    base_ne_out = base_ne_out_next;
                }
@@ -653,11 +630,18 @@ static struct ggml_backend_meta_split_state ggml_backend_meta_get_split_state(
            }
            default: {
                GGML_ABORT("fatal error");
-                //return {GGML_BACKEND_SPLIT_AXIS_UNKNOWN, {0}, 1};
+                //return {GGML_BACKEND_SPLIT_AXIS_UNKNOWN, {0}, {1}, 1};
            }
        }
    };

+    auto handle_cpy = [&](const std::vector<ggml_backend_meta_split_state> & src_ss) -> ggml_backend_meta_split_state {
+        if (src_ss[0].axis >= 0 && src_ss[0].axis < GGML_MAX_DIMS) {
+            return handle_reshape(src_ss);
+        }
+        return handle_generic(src_ss, /*scalar_only =*/ false);
+    };
+
    auto handle_view = [&](const std::vector<ggml_backend_meta_split_state> & src_ss) -> ggml_backend_meta_split_state {
        if (ggml_is_contiguous(tensor) && ggml_is_contiguous(tensor->src[0])) {
            return handle_reshape(src_ss);
@@ -681,7 +665,7 @@ static struct ggml_backend_meta_split_state ggml_backend_meta_get_split_state(
        if (!ggml_is_permuted(tensor) && !ggml_is_permuted(tensor->src[0]) && axis >= 0 && axis < GGML_MAX_DIMS-1) {
            for (int dim = 0; dim < GGML_MAX_DIMS-1; dim++) {
                if (tensor->nb[dim+1] == tensor->src[0]->nb[axis+1]) {
-                    return {ggml_backend_meta_split_axis(dim), {0}, 1};
+                    return {ggml_backend_meta_split_axis(dim), {0}, {1}, 1};
                }
            }
            GGML_ABORT("fatal error");
@@ -690,7 +674,7 @@ static struct ggml_backend_meta_split_state ggml_backend_meta_get_split_state(
            return src_ss[0];
        }
        GGML_ABORT("view of permuted tensor not implemented");
-        //return {GGML_BACKEND_SPLIT_AXIS_UNKNOWN, {0}, 1};
+        //return {GGML_BACKEND_SPLIT_AXIS_UNKNOWN, {0}, {1}, 1};
    };

    auto handle_permute = [&](const std::vector<ggml_backend_meta_split_state> & src_ss) -> ggml_backend_meta_split_state {
@@ -699,7 +683,8 @@ static struct ggml_backend_meta_split_state ggml_backend_meta_get_split_state(
            case GGML_BACKEND_SPLIT_AXIS_1:
            case GGML_BACKEND_SPLIT_AXIS_2:
            case GGML_BACKEND_SPLIT_AXIS_3: {
-                return {ggml_backend_meta_split_axis(tensor->op_params[src_ss[0].axis]), {0}, 1};
+                GGML_ASSERT(src_ss[0].n_segments == 1 || src_ss[0].nr[0] == 1);
+                return {ggml_backend_meta_split_axis(tensor->op_params[src_ss[0].axis]), {0}, {src_ss[0].nr[0]}, 1};
            }
            case GGML_BACKEND_SPLIT_AXIS_MIRRORED:
            case GGML_BACKEND_SPLIT_AXIS_PARTIAL: {
@@ -707,7 +692,7 @@ static struct ggml_backend_meta_split_state ggml_backend_meta_get_split_state(
            }
            default: {
                GGML_ABORT("fatal error");
-                //return {GGML_BACKEND_SPLIT_AXIS_UNKNOWN, {0}, 1};
+                //return {GGML_BACKEND_SPLIT_AXIS_UNKNOWN, {0}, {1}, 1};
            }
        }
    };
@@ -716,7 +701,8 @@ static struct ggml_backend_meta_split_state ggml_backend_meta_get_split_state(
        switch (src_ss[0].axis) {
            case GGML_BACKEND_SPLIT_AXIS_0:
            case GGML_BACKEND_SPLIT_AXIS_1: {
-                return {ggml_backend_meta_split_axis(int(src_ss[0].axis) ^ 1), {0}, 1};
+                GGML_ASSERT(src_ss[0].n_segments == 1 || src_ss[0].nr[0] == 1);
+                return {ggml_backend_meta_split_axis(int(src_ss[0].axis) ^ 1), {0}, {src_ss[0].nr[0]}, 1};
            }
            case GGML_BACKEND_SPLIT_AXIS_2:
            case GGML_BACKEND_SPLIT_AXIS_3:
@@ -726,7 +712,7 @@ static struct ggml_backend_meta_split_state ggml_backend_meta_get_split_state(
            }
            default: {
                GGML_ABORT("fatal error");
-                //return {GGML_BACKEND_SPLIT_AXIS_UNKNOWN, {0}, 1};
+                //return {GGML_BACKEND_SPLIT_AXIS_UNKNOWN, {0}, {1}, 1};
            }
        }
    };
@@ -764,16 +750,16 @@ static struct ggml_backend_meta_split_state ggml_backend_meta_get_split_state(
        GGML_ASSERT(                             src_ss[2].axis == GGML_BACKEND_SPLIT_AXIS_2);
        GGML_ASSERT(tensor->src[4] == nullptr || src_ss[3].axis == GGML_BACKEND_SPLIT_AXIS_MIRRORED);
        GGML_ASSERT(tensor->src[4] == nullptr || src_ss[4].axis == GGML_BACKEND_SPLIT_AXIS_0);
-        return {GGML_BACKEND_SPLIT_AXIS_1, {0}, 1};
+        return {GGML_BACKEND_SPLIT_AXIS_1, {0}, {1}, 1};
    };

    auto handle_ssm_conv = [&](const std::vector<ggml_backend_meta_split_state> & src_ss) -> ggml_backend_meta_split_state {
        if (src_ss[0].axis == src_ss[1].axis) {
            if (src_ss[0].axis == GGML_BACKEND_SPLIT_AXIS_0) {
-                return {GGML_BACKEND_SPLIT_AXIS_1, {0}, 1};
+                return {GGML_BACKEND_SPLIT_AXIS_1, {0}, {1}, 1};
            }
            if (src_ss[0].axis == GGML_BACKEND_SPLIT_AXIS_1) {
-                return {GGML_BACKEND_SPLIT_AXIS_0, {0}, 1};
+                return {GGML_BACKEND_SPLIT_AXIS_0, {0}, {1}, 1};
            }
        }
        return handle_generic(src_ss, /*scalar_only =*/ false);
@@ -781,8 +767,8 @@ static struct ggml_backend_meta_split_state ggml_backend_meta_get_split_state(

    auto handle_gated_delta_net = [&](const std::vector<ggml_backend_meta_split_state> & src_ss) -> ggml_backend_meta_split_state {
        if (src_ss[0].axis == GGML_BACKEND_SPLIT_AXIS_MIRRORED && src_ss[1].axis == GGML_BACKEND_SPLIT_AXIS_MIRRORED &&
-            src_ss[2].axis == GGML_BACKEND_SPLIT_AXIS_MIRRORED && src_ss[3].axis == GGML_BACKEND_SPLIT_AXIS_MIRRORED &&
-            src_ss[4].axis == GGML_BACKEND_SPLIT_AXIS_MIRRORED && src_ss[5].axis == GGML_BACKEND_SPLIT_AXIS_MIRRORED) {
+                src_ss[2].axis == GGML_BACKEND_SPLIT_AXIS_MIRRORED && src_ss[3].axis == GGML_BACKEND_SPLIT_AXIS_MIRRORED &&
+                src_ss[4].axis == GGML_BACKEND_SPLIT_AXIS_MIRRORED && src_ss[5].axis == GGML_BACKEND_SPLIT_AXIS_MIRRORED) {
            return src_ss[0];
        }
        GGML_ASSERT(src_ss[0].axis == GGML_BACKEND_SPLIT_AXIS_1);
@@ -793,12 +779,12 @@ static struct ggml_backend_meta_split_state ggml_backend_meta_get_split_state(
        // state shape is (S_v*S_v*H, K, n_seqs); the heads dim is nested inside axis 0,
        // so a head-aligned split on the input cache reshapes to axis 0 here (not axis 2).
        GGML_ASSERT(src_ss[5].axis == GGML_BACKEND_SPLIT_AXIS_2 || src_ss[5].axis == GGML_BACKEND_SPLIT_AXIS_1 || src_ss[5].axis == GGML_BACKEND_SPLIT_AXIS_0);
-        return {GGML_BACKEND_SPLIT_AXIS_0, {0}, 1};
+        return {GGML_BACKEND_SPLIT_AXIS_0, {0}, {1}, 1};
    };

    auto calculate_split_state = [&]() -> ggml_backend_meta_split_state {
        if (ggml_nelements(tensor) == 0) {
-            return {GGML_BACKEND_SPLIT_AXIS_UNKNOWN, {0}, 1};
+            return {GGML_BACKEND_SPLIT_AXIS_UNKNOWN, {0}, {1}, 1};
        }
        if (ggml_backend_buffer_get_usage(tensor->buffer) != GGML_BACKEND_BUFFER_USAGE_COMPUTE && tensor->view_src == nullptr) {
            ggml_backend_dev_t dev = ggml_backend_buft_get_device(ggml_backend_buffer_get_type(tensor->buffer));
@@ -807,19 +793,21 @@ static struct ggml_backend_meta_split_state ggml_backend_meta_get_split_state(
            if (ret.axis >= 0 && ret.axis <= GGML_MAX_DIMS) {
                const int64_t granularity = ret.axis == GGML_BACKEND_SPLIT_AXIS_0 ? ggml_blck_size(tensor->type) : 1;
                int64_t ne_sum = 0;
-                for (size_t sj = 0; sj < ret.n_segments*n_bufs; sj++) {
-                    GGML_ASSERT(ret.ne[sj] % granularity == 0);
-                    ne_sum += ret.ne[sj];
+                for (size_t s = 0; s < ret.n_segments; s++) {
+                    for (size_t j = 0; j < n_bufs; j++) {
+                        GGML_ASSERT(ret.ne[s*n_bufs + j] % granularity == 0);
+                        ne_sum += ret.ne[s*n_bufs + j] * ret.nr[s];
+                    }
                }
                GGML_ASSERT(ne_sum == tensor->ne[ret.axis]);
            }
            return ret;
        }

-        std::vector<ggml_backend_meta_split_state> src_ss(GGML_MAX_SRC, {GGML_BACKEND_SPLIT_AXIS_NONE, {0}, 1});
+        std::vector<ggml_backend_meta_split_state> src_ss(GGML_MAX_SRC, {GGML_BACKEND_SPLIT_AXIS_NONE, {0}, {1}, 1});
        for (size_t i = 0; i < GGML_MAX_SRC; i++) {
            if (tensor->src[i] == nullptr || tensor->src[i] == tensor) {
-                src_ss[i] = {GGML_BACKEND_SPLIT_AXIS_UNKNOWN, {0}, 1};
+                src_ss[i] = {GGML_BACKEND_SPLIT_AXIS_UNKNOWN, {0}, {1}, 1};
                continue;
            }
            src_ss[i] = ggml_backend_meta_get_split_state(stc, tensor->src[i], /*assume_sync =*/ true);
@@ -829,7 +817,7 @@ static struct ggml_backend_meta_split_state ggml_backend_meta_get_split_state(
        ggml_backend_meta_split_state split_state;
        switch (tensor->op) {
            case GGML_OP_NONE: {
-                split_state = {GGML_BACKEND_SPLIT_AXIS_MIRRORED, {0}, 1};
+                split_state = {GGML_BACKEND_SPLIT_AXIS_MIRRORED, {0}, {1}, 1};
            } break;
            case GGML_OP_DUP: {
                split_state = handle_generic(src_ss, /*scalar_only =*/ true);
@@ -1016,7 +1004,7 @@ static struct ggml_backend_meta_split_state ggml_backend_meta_get_split_state(
            } break;
            default: {
                GGML_ABORT("ggml op not implemented: %s", ggml_op_name(tensor->op));
-                split_state = {GGML_BACKEND_SPLIT_AXIS_UNKNOWN, {0}, 1};
+                split_state = {GGML_BACKEND_SPLIT_AXIS_UNKNOWN, {0}, {1}, 1};
            } break;
        }
        if (split_state.axis >= 0 && split_state.axis < GGML_MAX_DIMS) {
@@ -1034,23 +1022,25 @@ static struct ggml_backend_meta_split_state ggml_backend_meta_get_split_state(
                            split_state.ne[s*n_bufs + j] = 0;
                        }
                        for (size_t s = 0; s < src_ss[i].n_segments; s++) {
-                            split_state.ne[j] += src_ss[i].ne[s*n_bufs + j];
+                            split_state.ne[j] += src_ss[i].ne[s*n_bufs + j] * src_ss[i].nr[s];
                        }
                        split_state.ne[j] *= tensor->ne[split_state.axis];
                        if (split_state.ne[j] != 0 || tensor->src[i]->ne[src_ss[i].axis] != 0) {
-                            GGML_ASSERT(split_state.ne[j] % tensor->src[i]->ne[src_ss[i].axis] == 0);
-                            split_state.ne[j] /= tensor->src[i]->ne[src_ss[i].axis];
+                            const int64_t div = tensor->src[i]->ne[src_ss[i].axis] * split_state.nr[0];
+                            GGML_ASSERT(split_state.ne[j] % div == 0);
+                            split_state.ne[j] /= div;
                        }
                    }
                } else {
+                    GGML_ASSERT(split_state.n_segments == 1);
                    for (size_t j = 0; j < n_bufs; j++) {
+                        // Assert that ratio is consistent:
                        int64_t sum = 0;
                        for (size_t s = 0; s < src_ss[i].n_segments; s++) {
-                            sum += src_ss[i].ne[s*n_bufs + j];
+                            sum += src_ss[i].ne[s*n_bufs + j] * src_ss[i].nr[s];
                        }
-                        // Assert that ratio is consistent:
-                        GGML_ASSERT(split_state.ne[j] * tensor->src[i]->ne[src_ss[i].axis]
-                                               == sum * tensor->ne[split_state.axis]);
+                        GGML_ASSERT(split_state.ne[j]*split_state.nr[0] * tensor->src[i]->ne[src_ss[i].axis]
+                                                                 == sum * tensor->ne[split_state.axis]);
                    }
                }
                first_src_split_by_axis = false;
@@ -1080,13 +1070,14 @@ static struct ggml_backend_meta_split_state ggml_backend_meta_get_split_state(
                    srcs_info += ", ";
                }
                const ggml_backend_meta_split_state split_state = ggml_backend_meta_get_split_state(tensor->src[0], true);
+                GGML_ASSERT(split_state.n_segments == 1);
                const char * axis_name = ggml_backend_meta_split_axis_name(split_state.axis);
                std::string ne_info;
                for (size_t j = 0; j < n_bufs; j++) {
                    if (!ne_info.empty()) {
                        ne_info += ", ";
                    }
-                    ne_info += std::to_string(split_state.ne[j]);
+                    ne_info += std::to_string(split_state.ne[j]) + "x" + std::to_string(split_state.nr[0]);
                }
                srcs_info += std::string(tensor->src[i]->name) + "[" + ggml_op_name(tensor->src[i]->op) + ", " + axis_name + ", {" + ne_info + "}]";
            }
@@ -1095,7 +1086,8 @@ static struct ggml_backend_meta_split_state ggml_backend_meta_get_split_state(
                if (!ne_info.empty()) {
                    ne_info += ", ";
                }
-                ne_info += std::to_string(buf_ctx->split_state_cache[key].first.ne[j]);
+                const ggml_backend_meta_split_state & ss = buf_ctx->split_state_cache[key].first;
+                ne_info += std::to_string(ss.ne[j]) + "x" + std::to_string(ss.nr[0]);
            }
            GGML_LOG_DEBUG("SPLIT_STATE: {%s} -> %s[%s, %s, {%s}]\n", srcs_info.c_str(), tensor->name, ggml_op_name(tensor->op),
                ggml_backend_meta_split_axis_name(buf_ctx->split_state_cache[key].first.axis), ne_info.c_str());
@@ -1107,8 +1099,10 @@ static struct ggml_backend_meta_split_state ggml_backend_meta_get_split_state(
 #ifndef NDEBUG
    if (ret.axis >= 0 && ret.axis < GGML_MAX_DIMS) {
        int64_t ne_ret = 0;
-        for (size_t sj = 0; sj < ret.n_segments*n_bufs; sj++) {
-            ne_ret += ret.ne[sj];
+        for (size_t s = 0; s < ret.n_segments; s++) {
+            for (size_t j = 0; j < n_bufs; j++) {
+                ne_ret += ret.ne[s*n_bufs + j] * ret.nr[s];
+            }
        }
        assert(ne_ret == tensor->ne[int(ret.axis)]);
    }
@@ -1155,7 +1149,7 @@ static enum ggml_status ggml_backend_meta_buffer_init_tensor_impl(ggml_backend_m
            // GGML_ASSERT(ggml_is_contiguously_allocated(tensor));
            ne[split_dim] = 0;
            for (size_t s = 0; s < split_state.n_segments; s++) {
-                ne[split_dim] += split_state.ne[s*n_simple_bufs + j];
+                ne[split_dim] += split_state.ne[s*n_simple_bufs + j] * split_state.nr[s];
            }
            for (int i = 0; i < GGML_MAX_DIMS; i++) {
                if (tensor->nb[i] > tensor->nb[split_dim]) {
@@ -1229,7 +1223,7 @@ static enum ggml_status ggml_backend_meta_buffer_init_tensor_impl(ggml_backend_m
        for (size_t j = 0; j < n_simple_bufs; j++) {
            int64_t ne_sum = 0;
            for (size_t s = 0; s < split_state_src.n_segments; s++) {
-                ne_sum += split_state_src.ne[s*n_simple_bufs + j];
+                ne_sum += split_state_src.ne[s*n_simple_bufs + j] * split_state_src.nr[s];
            }
            if (ne_sum == 0) {
                simple_tensors[j]->flags &= ~GGML_TENSOR_FLAG_COMPUTE;
@@ -1255,8 +1249,9 @@ static void ggml_backend_meta_buffer_set_tensor(ggml_backend_buffer_t buffer, gg

    const ggml_backend_meta_split_state split_state = ggml_backend_meta_get_split_state(tensor, /*assume_sync =*/ false);

-    if (split_state.n_segments != 1) {
+    if (split_state.n_segments != 1 || split_state.nr[0] != 1) {
        GGML_ASSERT(split_state.axis >= 0 && split_state.axis < GGML_MAX_DIMS);
+        GGML_ASSERT(split_state.nr[0] != 0);
        GGML_ASSERT(tensor->ne[3] == 1);

        size_t offset_data = 0;
@@ -1267,24 +1262,26 @@ static void ggml_backend_meta_buffer_set_tensor(ggml_backend_buffer_t buffer, gg
            const size_t row_stride = tensor->nb[1];
            GGML_ASSERT(offset % row_stride == 0);
            GGML_ASSERT(size   % row_stride == 0);
-            const int64_t r_start = offset / row_stride;
-            const int64_t r_count = size   / row_stride;
-            GGML_ASSERT(r_start + r_count <= tensor->ne[1]);
+            const int64_t row_start = offset / row_stride;
+            const int64_t row_count = size   / row_stride;
+            GGML_ASSERT(row_start + row_count <= tensor->ne[1]);

            const int64_t blck_size = ggml_blck_size(tensor->type);
            for (size_t s = 0; s < split_state.n_segments; s++) {
-                for (size_t j = 0; j < n_bufs; j++) {
-                    ggml_tensor * simple_tensor = ggml_backend_meta_buffer_simple_tensor(tensor, j);
-                    GGML_ASSERT(split_state.ne[s*n_bufs + j] % blck_size == 0);
-                    const size_t nbytes = split_state.ne[s*n_bufs + j]/blck_size * tensor->nb[0];
-                    ggml_backend_tensor_set_2d(simple_tensor, (const char *) data + offset_data,
-                        simple_offsets[j] + r_start * simple_tensor->nb[1], nbytes,
-                        r_count, simple_tensor->nb[1], tensor->nb[1]);
-                    offset_data       += nbytes;
-                    simple_offsets[j] += nbytes;
+                for (size_t r = 0; r < split_state.nr[s]; r++) {
+                    for (size_t j = 0; j < n_bufs; j++) {
+                        ggml_tensor * simple_tensor = ggml_backend_meta_buffer_simple_tensor(tensor, j);
+                        GGML_ASSERT(split_state.ne[s*n_bufs + j] % blck_size == 0);
+                        const size_t nbytes = split_state.ne[s*n_bufs + j]/blck_size * tensor->nb[0];
+                        ggml_backend_tensor_set_2d(simple_tensor, (const char *) data + offset_data,
+                            simple_offsets[j] + row_start * simple_tensor->nb[1], nbytes,
+                            row_count, simple_tensor->nb[1], tensor->nb[1]);
+                        offset_data       += nbytes;
+                        simple_offsets[j] += nbytes;
+                    }
                }
            }
-            GGML_ASSERT(offset_data*r_count == size);
+            GGML_ASSERT(offset_data*row_count == size);
            return;
        }
        GGML_ASSERT(split_state.axis == GGML_BACKEND_SPLIT_AXIS_1);
@@ -1292,22 +1289,24 @@ static void ggml_backend_meta_buffer_set_tensor(ggml_backend_buffer_t buffer, gg
        const size_t row_stride = tensor->nb[2];
        GGML_ASSERT(offset % row_stride == 0);
        GGML_ASSERT(size   % row_stride == 0);
-        const int64_t r_start = offset / row_stride;
-        const int64_t r_count = size   / row_stride;
-        GGML_ASSERT(r_start + r_count <= tensor->ne[2]);
+        const int64_t row_start = offset / row_stride;
+        const int64_t row_count = size   / row_stride;
+        GGML_ASSERT(row_start + row_count <= tensor->ne[2]);

        for (size_t s = 0; s < split_state.n_segments; s++) {
-            for (size_t j = 0; j < n_bufs; j++) {
-                ggml_tensor * simple_tensor = ggml_backend_meta_buffer_simple_tensor(tensor, j);
-                const size_t nbytes = split_state.ne[s*n_bufs + j] * tensor->nb[1];
-                ggml_backend_tensor_set_2d(simple_tensor, (const char *) data + offset_data,
-                    simple_offsets[j] + r_start * simple_tensor->nb[2], nbytes,
-                    r_count, simple_tensor->nb[2], tensor->nb[2]);
-                offset_data       += nbytes;
-                simple_offsets[j] += nbytes;
+            for (size_t r = 0; r < split_state.nr[s]; r++) {
+                for (size_t j = 0; j < n_bufs; j++) {
+                    ggml_tensor * simple_tensor = ggml_backend_meta_buffer_simple_tensor(tensor, j);
+                    const size_t nbytes = split_state.ne[s*n_bufs + j] * tensor->nb[1];
+                    ggml_backend_tensor_set_2d(simple_tensor, (const char *) data + offset_data,
+                        simple_offsets[j] + row_start * simple_tensor->nb[2], nbytes,
+                        row_count, simple_tensor->nb[2], tensor->nb[2]);
+                    offset_data       += nbytes;
+                    simple_offsets[j] += nbytes;
+                }
            }
        }
-        GGML_ASSERT(offset_data*r_count == size);
+        GGML_ASSERT(offset_data*row_count == size);
        return;
    }

@@ -1365,8 +1364,9 @@ static void ggml_backend_meta_buffer_get_tensor(ggml_backend_buffer_t buffer, co

    const ggml_backend_meta_split_state split_state = ggml_backend_meta_get_split_state(tensor, /*assume_sync =*/ false);

-    if (split_state.n_segments != 1) {
+    if (split_state.n_segments != 1 || split_state.nr[0] != 1) {
        GGML_ASSERT(split_state.axis >= 0 && split_state.axis < GGML_MAX_DIMS);
+        GGML_ASSERT(split_state.nr[0] != 0);
        GGML_ASSERT(tensor->ne[3] == 1);

        size_t offset_data = 0;
@@ -1377,24 +1377,26 @@ static void ggml_backend_meta_buffer_get_tensor(ggml_backend_buffer_t buffer, co
            const size_t row_stride = tensor->nb[1];
            GGML_ASSERT(offset % row_stride == 0);
            GGML_ASSERT(size   % row_stride == 0);
-            const int64_t r_start = offset / row_stride;
-            const int64_t r_count = size   / row_stride;
-            GGML_ASSERT(r_start + r_count <= tensor->ne[1]);
+            const int64_t row_start = offset / row_stride;
+            const int64_t row_count = size   / row_stride;
+            GGML_ASSERT(row_start + row_count <= tensor->ne[1]);

            const int64_t blck_size = ggml_blck_size(tensor->type);
            for (size_t s = 0; s < split_state.n_segments; s++) {
-                for (size_t j = 0; j < n_bufs; j++) {
-                    const ggml_tensor * simple_tensor = ggml_backend_meta_buffer_simple_tensor(tensor, j);
-                    GGML_ASSERT(split_state.ne[s*n_bufs + j] % blck_size == 0);
-                    const size_t nbytes = split_state.ne[s*n_bufs + j]/blck_size * tensor->nb[0];
-                    ggml_backend_tensor_get_2d(simple_tensor, (char *) data + offset_data,
-                        simple_offsets[j] + r_start * simple_tensor->nb[1], nbytes,
-                        r_count, simple_tensor->nb[1], tensor->nb[1]);
-                    offset_data       += nbytes;
-                    simple_offsets[j] += nbytes;
+                for (size_t r = 0; r < split_state.nr[s]; r++) {
+                    for (size_t j = 0; j < n_bufs; j++) {
+                        const ggml_tensor * simple_tensor = ggml_backend_meta_buffer_simple_tensor(tensor, j);
+                        GGML_ASSERT(split_state.ne[s*n_bufs + j] % blck_size == 0);
+                        const size_t nbytes = split_state.ne[s*n_bufs + j]/blck_size * tensor->nb[0];
+                        ggml_backend_tensor_get_2d(simple_tensor, (char *) data + offset_data,
+                            simple_offsets[j] + row_start * simple_tensor->nb[1], nbytes,
+                            row_count, simple_tensor->nb[1], tensor->nb[1]);
+                        offset_data       += nbytes;
+                        simple_offsets[j] += nbytes;
+                    }
                }
            }
-            GGML_ASSERT(offset_data*r_count == size);
+            GGML_ASSERT(offset_data*row_count == size);
            return;
        }
        GGML_ASSERT(split_state.axis == GGML_BACKEND_SPLIT_AXIS_1);
@@ -1402,22 +1404,24 @@ static void ggml_backend_meta_buffer_get_tensor(ggml_backend_buffer_t buffer, co
        const size_t row_stride = tensor->nb[2];
        GGML_ASSERT(offset % row_stride == 0);
        GGML_ASSERT(size   % row_stride == 0);
-        const int64_t r_start = offset / row_stride;
-        const int64_t r_count = size   / row_stride;
-        GGML_ASSERT(r_start + r_count <= tensor->ne[2]);
+        const int64_t row_start = offset / row_stride;
+        const int64_t row_count = size   / row_stride;
+        GGML_ASSERT(row_start + row_count <= tensor->ne[2]);

        for (size_t s = 0; s < split_state.n_segments; s++) {
-            for (size_t j = 0; j < n_bufs; j++) {
-                const ggml_tensor * simple_tensor = ggml_backend_meta_buffer_simple_tensor(tensor, j);
-                const size_t nbytes = split_state.ne[s*n_bufs + j] * tensor->nb[1];
-                ggml_backend_tensor_get_2d(simple_tensor, (char *) data + offset_data,
-                    simple_offsets[j] + r_start * simple_tensor->nb[2], nbytes,
-                    r_count, simple_tensor->nb[2], tensor->nb[2]);
-                offset_data       += nbytes;
-                simple_offsets[j] += nbytes;
+            for (size_t r = 0; r < split_state.nr[s]; r++) {
+                for (size_t j = 0; j < n_bufs; j++) {
+                    const ggml_tensor * simple_tensor = ggml_backend_meta_buffer_simple_tensor(tensor, j);
+                    const size_t nbytes = split_state.ne[s*n_bufs + j] * tensor->nb[1];
+                    ggml_backend_tensor_get_2d(simple_tensor, (char *) data + offset_data,
+                        simple_offsets[j] + row_start * simple_tensor->nb[2], nbytes,
+                        row_count, simple_tensor->nb[2], tensor->nb[2]);
+                    offset_data       += nbytes;
+                    simple_offsets[j] += nbytes;
+                }
            }
        }
-        GGML_ASSERT(offset_data*r_count == size);
+        GGML_ASSERT(offset_data*row_count == size);
        return;
    }

@@ -1675,6 +1679,7 @@ static void ggml_backend_meta_set_tensor_async(ggml_backend_t backend, ggml_tens

    const ggml_backend_meta_split_state split_state = ggml_backend_meta_get_split_state(tensor, /*assume_sync =*/ false);
    GGML_ASSERT(split_state.n_segments == 1);
+    GGML_ASSERT(split_state.nr[0]      == 1);

    switch (split_state.axis) {
        case GGML_BACKEND_SPLIT_AXIS_0:
@@ -1719,6 +1724,7 @@ static void ggml_backend_meta_get_tensor_async(ggml_backend_t backend, const ggm

    const ggml_backend_meta_split_state split_state = ggml_backend_meta_get_split_state(tensor, /*assume_sync =*/ false);
    GGML_ASSERT(split_state.n_segments == 1);
+    GGML_ASSERT(split_state.nr[0]      == 1);

    switch (split_state.axis) {
        case GGML_BACKEND_SPLIT_AXIS_0:
@@ -2076,6 +2082,7 @@ static enum ggml_status ggml_backend_meta_graph_compute(ggml_backend_t backend,
            node_zero->src[0] = node;
            ggml_set_op_params_f32(node_zero, 0, 0.0f);
            node_zero->data = node->data;
+            node_zero->buffer = node->buffer;
            node_zero->flags |= GGML_TENSOR_FLAG_COMPUTE;

            step_cgraphs[j] = get_cgraph_aux();
@@ -977,6 +977,35 @@ void ggml_vec_dot_q8_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const voi
    sumf = hsum_float_8(acc);

    *s = sumf;
+
+#elif defined(__loongarch_sx)
+
+    __m128 acc = (__m128)__lsx_vldi(0);
+
+    for (; ib < nb; ++ib) {
+        const float d = GGML_CPU_FP16_TO_FP32(x[ib].d) * GGML_CPU_FP16_TO_FP32(y[ib].d);
+        const __m128i qx_0 = __lsx_vld((const __m128i *)x[ib].qs, 0);
+        const __m128i qx_1 = __lsx_vld((const __m128i *)x[ib].qs + 1, 0);
+        const __m128i qy_0 = __lsx_vld((const __m128i *)y[ib].qs, 0);
+        const __m128i qy_1 = __lsx_vld((const __m128i *)y[ib].qs + 1, 0);
+
+        const __m128i p16_0 = lsx_maddubs_h(qx_0, qy_0);
+        const __m128i p16_1 = lsx_maddubs_h(qx_1, qy_1);
+
+        // Sum int16 pairs → int32
+        const __m128i s_0 = __lsx_vaddwev_w_h(p16_0, p16_1);
+        const __m128i s_1 = __lsx_vaddwod_w_h(p16_0, p16_1);
+
+        const __m128 q = __lsx_vffint_s_w(__lsx_vadd_w(s_0, s_1));
+        acc = __lsx_vfmadd_s(__lsx_vreplfr2vr_s(d), q, acc);
+    }
+
+    __m128 res = lsx_hadd_s(acc, acc);
+    res = lsx_hadd_s(res, res);
+    sumf = ((v4f32)res)[0];
+
+    *s = sumf;
+
 #else
    UNUSED(nb);
    UNUSED(ib);
@@ -1443,6 +1472,99 @@ void ggml_vec_dot_q6_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi

    *s = hsum_float_8(acc);

+#elif defined(__loongarch_sx)
+
+    const __m128i m32s = __lsx_vreplgr2vr_b(32);
+
+    __m128 acc_0 = (__m128)__lsx_vldi(0);
+    __m128 acc_1 = (__m128)__lsx_vldi(0);
+
+    for (int i = 0; i < nb; ++i) {
+
+        const float d = y[i].d * GGML_CPU_FP16_TO_FP32(x[i].d);
+
+        const uint8_t * GGML_RESTRICT q4 = x[i].ql;
+        const uint8_t * GGML_RESTRICT qh = x[i].qh;
+        const int8_t  * GGML_RESTRICT q8 = y[i].qs;
+
+        const __m128i scale_i8 = __lsx_vld(x[i].scales, 0);
+        const __m128i scales_lo = __lsx_vsllwil_h_b(scale_i8, 0);
+        const __m128i scales_hi = __lsx_vsllwil_h_b(__lsx_vbsrl_v(scale_i8, 8), 0);
+
+        __m128i sumi_0 = __lsx_vldi(0);
+        __m128i sumi_1 = __lsx_vldi(0);
+
+        for (int j = 0; j < QK_K/128; ++j) {
+
+            const __m128i q4bitsH_0 = __lsx_vld((const __m128i*)qh, 0); qh += 16;
+            const __m128i q4bitsH_1 = __lsx_vld((const __m128i*)qh, 0); qh += 16;
+
+            const __m128i q4h_0 = __lsx_vslli_b(__lsx_vandi_b(q4bitsH_0, 3), 4);
+            const __m128i q4h_1 = __lsx_vslli_b(__lsx_vandi_b(q4bitsH_1, 3), 4);
+            const __m128i q4h_2 = __lsx_vslli_b(__lsx_vandi_b(q4bitsH_0, 3 << 2), 2);
+            const __m128i q4h_3 = __lsx_vslli_b(__lsx_vandi_b(q4bitsH_1, 3 << 2), 2);
+            const __m128i q4h_4 = __lsx_vandi_b(q4bitsH_0, 3 << 4);
+            const __m128i q4h_5 = __lsx_vandi_b(q4bitsH_1, 3 << 4);
+            const __m128i q4h_6 = __lsx_vsrli_b(__lsx_vandi_b(q4bitsH_0, 3 << 6), 2);
+            const __m128i q4h_7 = __lsx_vsrli_b(__lsx_vandi_b(q4bitsH_1, 3 << 6), 2);
+
+            const __m128i q4bits1_0 = __lsx_vld((const __m128i*)q4, 0); q4 += 16;
+            const __m128i q4bits1_1 = __lsx_vld((const __m128i*)q4, 0); q4 += 16;
+            const __m128i q4bits2_0 = __lsx_vld((const __m128i*)q4, 0); q4 += 16;
+            const __m128i q4bits2_1 = __lsx_vld((const __m128i*)q4, 0); q4 += 16;
+
+            const __m128i q4_0 = __lsx_vor_v(__lsx_vandi_b(q4bits1_0, 0xf), q4h_0);
+            const __m128i q4_1 = __lsx_vor_v(__lsx_vandi_b(q4bits1_1, 0xf), q4h_1);
+            const __m128i q4_2 = __lsx_vor_v(__lsx_vandi_b(q4bits2_0, 0xf), q4h_2);
+            const __m128i q4_3 = __lsx_vor_v(__lsx_vandi_b(q4bits2_1, 0xf), q4h_3);
+            const __m128i q4_4 = __lsx_vor_v(__lsx_vsrli_b(q4bits1_0, 4), q4h_4);
+            const __m128i q4_5 = __lsx_vor_v(__lsx_vsrli_b(q4bits1_1, 4), q4h_5);
+            const __m128i q4_6 = __lsx_vor_v(__lsx_vsrli_b(q4bits2_0, 4), q4h_6);
+            const __m128i q4_7 = __lsx_vor_v(__lsx_vsrli_b(q4bits2_1, 4), q4h_7);
+
+            const __m128i q8_0 = __lsx_vld((const __m128i*)q8, 0); q8 += 16;
+            const __m128i q8_1 = __lsx_vld((const __m128i*)q8, 0); q8 += 16;
+            const __m128i q8_2 = __lsx_vld((const __m128i*)q8, 0); q8 += 16;
+            const __m128i q8_3 = __lsx_vld((const __m128i*)q8, 0); q8 += 16;
+            const __m128i q8_4 = __lsx_vld((const __m128i*)q8, 0); q8 += 16;
+            const __m128i q8_5 = __lsx_vld((const __m128i*)q8, 0); q8 += 16;
+            const __m128i q8_6 = __lsx_vld((const __m128i*)q8, 0); q8 += 16;
+            const __m128i q8_7 = __lsx_vld((const __m128i*)q8, 0); q8 += 16;
+
+            __m128i p16_0 = lsx_maddubs_h(__lsx_vsub_b(q4_0, m32s), q8_0);
+            __m128i p16_1 = lsx_maddubs_h(__lsx_vsub_b(q4_1, m32s), q8_1);
+            __m128i p16_2 = lsx_maddubs_h(__lsx_vsub_b(q4_2, m32s), q8_2);
+            __m128i p16_3 = lsx_maddubs_h(__lsx_vsub_b(q4_3, m32s), q8_3);
+            __m128i p16_4 = lsx_maddubs_h(__lsx_vsub_b(q4_4, m32s), q8_4);
+            __m128i p16_5 = lsx_maddubs_h(__lsx_vsub_b(q4_5, m32s), q8_5);
+            __m128i p16_6 = lsx_maddubs_h(__lsx_vsub_b(q4_6, m32s), q8_6);
+            __m128i p16_7 = lsx_maddubs_h(__lsx_vsub_b(q4_7, m32s), q8_7);
+
+            const __m128i sc_vec = j == 0 ? scales_lo : scales_hi;
+
+            p16_0 = lsx_madd_h(__lsx_vreplvei_h(sc_vec, 0), p16_0);
+            p16_1 = lsx_madd_h(__lsx_vreplvei_h(sc_vec, 1), p16_1);
+            p16_2 = lsx_madd_h(__lsx_vreplvei_h(sc_vec, 2), p16_2);
+            p16_3 = lsx_madd_h(__lsx_vreplvei_h(sc_vec, 3), p16_3);
+            p16_4 = lsx_madd_h(__lsx_vreplvei_h(sc_vec, 4), p16_4);
+            p16_5 = lsx_madd_h(__lsx_vreplvei_h(sc_vec, 5), p16_5);
+            p16_6 = lsx_madd_h(__lsx_vreplvei_h(sc_vec, 6), p16_6);
+            p16_7 = lsx_madd_h(__lsx_vreplvei_h(sc_vec, 7), p16_7);
+
+            sumi_0 = __lsx_vadd_w(sumi_0, __lsx_vadd_w(p16_0, p16_2));
+            sumi_1 = __lsx_vadd_w(sumi_1, __lsx_vadd_w(p16_1, p16_3));
+            sumi_0 = __lsx_vadd_w(sumi_0, __lsx_vadd_w(p16_4, p16_6));
+            sumi_1 = __lsx_vadd_w(sumi_1, __lsx_vadd_w(p16_5, p16_7));
+        }
+
+        __m128 p_0 = __lsx_vfmul_s(__lsx_vreplfr2vr_s(d), __lsx_vffint_s_w(sumi_0));
+        __m128 p_1 = __lsx_vfmul_s(__lsx_vreplfr2vr_s(d), __lsx_vffint_s_w(sumi_1));
+        acc_0 = __lsx_vfadd_s(p_0, acc_0);
+        acc_1 = __lsx_vfadd_s(p_1, acc_1);
+    }
+
+    *s = hsum_float_4x4(acc_0, acc_1, (__m128)__lsx_vldi(0), (__m128)__lsx_vldi(0));
+
 #else
    UNUSED(x);
    UNUSED(y);
@@ -2149,6 +2271,35 @@ void ggml_vec_dot_iq4_xs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const v

    *s = hsum_float_8(accum);

+#elif defined(__loongarch_sx)
+
+    const __m128i values128 = __lsx_vld((const __m128i*)kvalues_iq4nl, 0);
+
+    __m128 accum = (__m128)__lsx_vldi(0);
+    for (int ibl = 0; ibl < nb; ++ibl) {
+        const uint8_t * qs = x[ibl].qs;
+        const int8_t  * q8 = y[ibl].qs;
+        uint16_t sh = x[ibl].scales_h;
+        __m128i sumi = __lsx_vldi(0);
+        for (int ib = 0; ib < QK_K/32; ++ib) {
+            const __m128i q4bits = __lsx_vld((const __m128i*)qs, 0); qs += 16;
+            const __m128i q8b_0 = __lsx_vld((const __m128i*)q8, 0); q8 += 16;
+            const __m128i q8b_1 = __lsx_vld((const __m128i*)q8, 0); q8 += 16;
+            const __m128i q4b_0 = __lsx_vshuf_b(values128, values128, __lsx_vandi_b(q4bits, 0xf));
+            const __m128i q4b_1 = __lsx_vshuf_b(values128, values128, __lsx_vsrli_b(q4bits, 4));
+            const __m128i p16_0 = lsx_maddubs_h(q4b_0, q8b_0);
+            const __m128i p16_1 = lsx_maddubs_h(q4b_1, q8b_1);
+            const int16_t ls = (((x[ibl].scales_l[ib/2] >> ((ib & 1) * 4)) & 0xf) | ((sh & 0x3) << 4)) - 32;
+            sh >>= 2;
+            sumi = __lsx_vadd_w(lsx_madd_h(p16_0, __lsx_vreplgr2vr_h(ls)), sumi);
+            sumi = __lsx_vadd_w(lsx_madd_h(p16_1, __lsx_vreplgr2vr_h(ls)), sumi);
+        }
+        const float ds = GGML_CPU_FP16_TO_FP32(x[ibl].d) * y[ibl].d;
+        accum = __lsx_vfadd_s(__lsx_vfmul_s(__lsx_vreplfr2vr_s(ds), __lsx_vffint_s_w(sumi)), accum);
+    }
+
+    *s = ((v4f32)lsx_hadd_s(lsx_hadd_s(accum, accum), lsx_hadd_s(accum, accum)))[0];
+
 #else
    UNUSED(x);
    UNUSED(y);
@@ -2235,8 +2235,42 @@ static void ggml_compute_forward_fill_f32(const ggml_compute_params * params, gg
    }
 }

+static void ggml_compute_forward_fill_f16(const ggml_compute_params * params, ggml_tensor * dst) {
+    const ggml_fp16_t c = GGML_CPU_FP32_TO_FP16(ggml_get_op_params_f32(dst, 0));
+
+    GGML_TENSOR_LOCALS(int64_t, ne, dst, ne);
+    GGML_TENSOR_LOCALS(size_t,  nb, dst, nb);
+
+    const auto [ir0, ir1] = get_thread_range(params, dst);
+
+    for (int64_t ir = ir0; ir < ir1; ++ir) {
+        const int64_t i03 = ir/(ne2*ne1);
+        const int64_t i02 = (ir - i03*ne2*ne1)/ne1;
+        const int64_t i01 = (ir - i03*ne2*ne1 - i02*ne1);
+
+        ggml_fp16_t * dst_ptr  = (ggml_fp16_t *) ((char *) dst->data + i03*nb3 + i02*nb2 + i01*nb1);
+
+        ggml_vec_set_f16(ne0, dst_ptr, c);
+    }
+}
+
 void ggml_compute_forward_fill(const ggml_compute_params * params, ggml_tensor * dst) {
-    ggml_compute_forward_fill_f32(params, dst);
+    const ggml_tensor * src0 = dst->src[0];
+
+    switch (src0->type) {
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_fill_f32(params, dst);
+            } break;
+        case GGML_TYPE_F16:
+            {
+                ggml_compute_forward_fill_f16(params, dst);
+            } break;
+        default:
+            {
+                GGML_ABORT("unsupported type for ggml_compute_forward_fill: %s", ggml_type_name(src0->type));
+            }
+    }
 }

 // ggml_compute_tri
@@ -1125,25 +1125,12 @@ static inline void __lasx_f32cx8_store(ggml_fp16_t * x, __m256 y) {
 #define GGML_F16_EPR  4

 static inline __m128 __lsx_f16x4_load(const ggml_fp16_t * x) {
-    float tmp[4];
-
-    tmp[0] = GGML_CPU_FP16_TO_FP32(x[0]);
-    tmp[1] = GGML_CPU_FP16_TO_FP32(x[1]);
-    tmp[2] = GGML_CPU_FP16_TO_FP32(x[2]);
-    tmp[3] = GGML_CPU_FP16_TO_FP32(x[3]);
-
-    return (__m128)__lsx_vld(tmp, 0);
+    return __lsx_vfcvtl_s_h(__lsx_vld((const void *)x, 0));
 }

 static inline void __lsx_f16x4_store(ggml_fp16_t * x, __m128 y) {
-    float arr[4];
-
-    __lsx_vst(y, arr, 0);
-
-    x[0] = GGML_CPU_FP32_TO_FP16(arr[0]);
-    x[1] = GGML_CPU_FP32_TO_FP16(arr[1]);
-    x[2] = GGML_CPU_FP32_TO_FP16(arr[2]);
-    x[3] = GGML_CPU_FP32_TO_FP16(arr[3]);
+    __m128i a = __lsx_vfcvt_h_s(y, y);
+    memcpy(x, &a, sizeof(ggml_fp16_t) * 4);
 }

 #define GGML_F32Cx4             __m128
@@ -273,67 +273,51 @@ void ggml_vec_dot_f16(int n, float * GGML_RESTRICT s, size_t bs, ggml_fp16_t * G

 #if defined(GGML_SIMD)
    #if defined(__ARM_FEATURE_SVE)
-        const int sve_register_length = svcntb() * 8; //get vector length
-        const int ggml_f16_epr = sve_register_length / 16; // running when 16
-        const int ggml_f16_step = 8 * ggml_f16_epr; // choose 8 SVE registers
+        const int ggml_f16_epr = svcnth();
+        const int ggml_f16_step = 8 * ggml_f16_epr;
+        const int np = n - (n % ggml_f16_step);
+        const int np2 = n - (n % ggml_f16_epr);

-        const int np= (n & ~(ggml_f16_step - 1));
-        svfloat16_t sum1 = svdup_n_f16(0.0f);
-        svfloat16_t sum2 = svdup_n_f16(0.0f);
-        svfloat16_t sum3 = svdup_n_f16(0.0f);
-        svfloat16_t sum4 = svdup_n_f16(0.0f);
+        svfloat32_t sum1_lo = svdup_n_f32(0.0f);
+        svfloat32_t sum1_hi = svdup_n_f32(0.0f);
+        svfloat32_t sum2_lo = svdup_n_f32(0.0f);
+        svfloat32_t sum2_hi = svdup_n_f32(0.0f);
+        svfloat32_t sum3_lo = svdup_n_f32(0.0f);
+        svfloat32_t sum3_hi = svdup_n_f32(0.0f);
+        svfloat32_t sum4_lo = svdup_n_f32(0.0f);
+        svfloat32_t sum4_hi = svdup_n_f32(0.0f);

-        svfloat16_t ax1, ax2, ax3, ax4, ax5, ax6, ax7, ax8;
-        svfloat16_t ay1, ay2, ay3, ay4, ay5, ay6, ay7, ay8;
        for (int i = 0; i < np; i += ggml_f16_step) {
-            ax1 = GGML_F16x_VEC_LOAD(x + i + 0 * ggml_f16_epr, 0);
-            ay1 = GGML_F16x_VEC_LOAD(y + i + 0 * ggml_f16_epr, 0);
-            sum1 = GGML_F16x_VEC_FMA(sum1, ax1, ay1);
-
-            ax2 = GGML_F16x_VEC_LOAD(x + i + 1 * ggml_f16_epr, 1);
-            ay2 = GGML_F16x_VEC_LOAD(y + i + 1 * ggml_f16_epr, 1);
-            sum2 = GGML_F16x_VEC_FMA(sum2, ax2, ay2);
-
-            ax3 = GGML_F16x_VEC_LOAD(x + i + 2 * ggml_f16_epr, 2);
-            ay3 = GGML_F16x_VEC_LOAD(y + i + 2 * ggml_f16_epr, 2);
-            sum3 = GGML_F16x_VEC_FMA(sum3, ax3, ay3);
-
-            ax4 = GGML_F16x_VEC_LOAD(x + i + 3 * ggml_f16_epr, 3);
-            ay4 = GGML_F16x_VEC_LOAD(y + i + 3 * ggml_f16_epr, 3);
-            sum4 = GGML_F16x_VEC_FMA(sum4, ax4, ay4);
-
-            ax5 = GGML_F16x_VEC_LOAD(x + i + 4 * ggml_f16_epr, 4);
-            ay5 = GGML_F16x_VEC_LOAD(y + i + 4 * ggml_f16_epr, 4);
-            sum1 = GGML_F16x_VEC_FMA(sum1, ax5, ay5);
-
-            ax6 = GGML_F16x_VEC_LOAD(x + i + 5 * ggml_f16_epr, 5);
-            ay6 = GGML_F16x_VEC_LOAD(y + i + 5 * ggml_f16_epr, 5);
-            sum2 = GGML_F16x_VEC_FMA(sum2, ax6, ay6);
-
-            ax7 = GGML_F16x_VEC_LOAD(x + i + 6 * ggml_f16_epr, 6);
-            ay7 = GGML_F16x_VEC_LOAD(y + i + 6 * ggml_f16_epr, 6);
-            sum3 = GGML_F16x_VEC_FMA(sum3, ax7, ay7);
-
-            ax8 = GGML_F16x_VEC_LOAD(x + i + 7 * ggml_f16_epr, 7);
-            ay8 = GGML_F16x_VEC_LOAD(y + i + 7 * ggml_f16_epr, 7);
-            sum4 = GGML_F16x_VEC_FMA(sum4, ax8, ay8);
+            ggml_sve_f16_fma_widened(&sum1_lo, &sum1_hi, GGML_F16x_VEC_LOAD(x + i + 0 * ggml_f16_epr, 0), GGML_F16x_VEC_LOAD(y + i + 0 * ggml_f16_epr, 0));
+            ggml_sve_f16_fma_widened(&sum2_lo, &sum2_hi, GGML_F16x_VEC_LOAD(x + i + 1 * ggml_f16_epr, 1), GGML_F16x_VEC_LOAD(y + i + 1 * ggml_f16_epr, 1));
+            ggml_sve_f16_fma_widened(&sum3_lo, &sum3_hi, GGML_F16x_VEC_LOAD(x + i + 2 * ggml_f16_epr, 2), GGML_F16x_VEC_LOAD(y + i + 2 * ggml_f16_epr, 2));
+            ggml_sve_f16_fma_widened(&sum4_lo, &sum4_hi, GGML_F16x_VEC_LOAD(x + i + 3 * ggml_f16_epr, 3), GGML_F16x_VEC_LOAD(y + i + 3 * ggml_f16_epr, 3));
+            ggml_sve_f16_fma_widened(&sum1_lo, &sum1_hi, GGML_F16x_VEC_LOAD(x + i + 4 * ggml_f16_epr, 4), GGML_F16x_VEC_LOAD(y + i + 4 * ggml_f16_epr, 4));
+            ggml_sve_f16_fma_widened(&sum2_lo, &sum2_hi, GGML_F16x_VEC_LOAD(x + i + 5 * ggml_f16_epr, 5), GGML_F16x_VEC_LOAD(y + i + 5 * ggml_f16_epr, 5));
+            ggml_sve_f16_fma_widened(&sum3_lo, &sum3_hi, GGML_F16x_VEC_LOAD(x + i + 6 * ggml_f16_epr, 6), GGML_F16x_VEC_LOAD(y + i + 6 * ggml_f16_epr, 6));
+            ggml_sve_f16_fma_widened(&sum4_lo, &sum4_hi, GGML_F16x_VEC_LOAD(x + i + 7 * ggml_f16_epr, 7), GGML_F16x_VEC_LOAD(y + i + 7 * ggml_f16_epr, 7));
        }

-        const int np2 = (n & ~(ggml_f16_epr - 1)); // round down to multiple of 8
-        for (int k = np; k < np2; k += ggml_f16_epr) {
-            svfloat16_t rx = GGML_F16x_VEC_LOAD(x + k, 0);
-            svfloat16_t ry = GGML_F16x_VEC_LOAD(y + k, 0);
-            sum1 = GGML_F16x_VEC_FMA(sum1, rx, ry);
+        for (int i = np; i < np2; i += ggml_f16_epr) {
+            ggml_sve_f16_fma_widened(&sum1_lo, &sum1_hi, GGML_F16x_VEC_LOAD(x + i, 0), GGML_F16x_VEC_LOAD(y + i, 0));
        }

        if (np2 < n) {
-            svbool_t pg = svwhilelt_b16(np2, n);
-            svfloat16_t hx = svld1_f16(pg, (const __fp16 *)(x + np2));
-            svfloat16_t hy = svld1_f16(pg, (const __fp16 *)(y + np2));
+            const svbool_t pg = svwhilelt_b16(np2, n);
+            const svfloat16_t rx = svld1_f16(pg, (const __fp16 *)(x + np2));
+            const svfloat16_t ry = svld1_f16(pg, (const __fp16 *)(y + np2));

-            sum1 = svmad_f16_x(pg, hx, hy, sum1);
+            ggml_sve_f16_fma_widened(&sum1_lo, &sum1_hi, rx, ry);
        }
-        GGML_F16x_VEC_REDUCE(sumf, sum1, sum2, sum3, sum4);
+
+        sum1_lo = svadd_f32_m(DEFAULT_PG32, sum1_lo, sum2_lo);
+        sum1_hi = svadd_f32_m(DEFAULT_PG32, sum1_hi, sum2_hi);
+        sum3_lo = svadd_f32_m(DEFAULT_PG32, sum3_lo, sum4_lo);
+        sum3_hi = svadd_f32_m(DEFAULT_PG32, sum3_hi, sum4_hi);
+        sum1_lo = svadd_f32_m(DEFAULT_PG32, sum1_lo, sum3_lo);
+        sum1_hi = svadd_f32_m(DEFAULT_PG32, sum1_hi, sum3_hi);
+
+        sumf = ggml_sve_sum_f32x2(sum1_lo, sum1_hi);
    #elif defined(__riscv_v_intrinsic)
        #if defined(__riscv_zvfh)
            int vl = __riscv_vsetvlmax_e32m2();
@@ -14,6 +14,35 @@
 // floating point type used to accumulate sums
 typedef double ggml_float;

+#if defined(__ARM_FEATURE_SVE)
+inline static void ggml_sve_f16_fma_widened(
+        svfloat32_t * acc_lo,
+        svfloat32_t * acc_hi,
+        svfloat16_t x,
+        svfloat16_t y) {
+#if defined(__ARM_FEATURE_SVE2)
+    *acc_lo = svmlalb_f32(*acc_lo, x, y);
+    *acc_hi = svmlalt_f32(*acc_hi, x, y);
+#else
+    // Plain SVE fallback path if SVE2 instructions not available
+    svfloat16_t x_even = svtrn1_f16(x, x);
+    svfloat16_t x_odd = svtrn2_f16(x, x);
+
+    svfloat16_t y_even = svtrn1_f16(y, y);
+    svfloat16_t y_odd = svtrn2_f16(y, y);
+
+    svbool_t pg = svptrue_b32();
+
+    *acc_lo = svmla_f32_x(pg, *acc_lo, svcvt_f32_f16_x(pg, x_even), svcvt_f32_f16_x(pg, y_even));
+    *acc_hi = svmla_f32_x(pg, *acc_hi, svcvt_f32_f16_x(pg, x_odd), svcvt_f32_f16_x(pg, y_odd));
+#endif
+}
+
+inline static ggml_float ggml_sve_sum_f32x2(svfloat32_t sum_lo, svfloat32_t sum_hi) {
+    return (ggml_float) (svaddv_f32(svptrue_b32(), sum_lo) + svaddv_f32(svptrue_b32(), sum_hi));
+}
+#endif
+
 #define GGML_GELU_FP16
 #define GGML_GELU_QUICK_FP16

@@ -122,108 +151,61 @@ inline static void ggml_vec_dot_f16_unroll(const int n, const int xs, float * GG
 #if defined(GGML_SIMD)
    #if defined(__ARM_FEATURE_SVE)

-        const int sve_register_length = svcntb() * 8;
-        const int ggml_f16_epr = sve_register_length / 16; // running when 16
-        const int ggml_f16_step = 8 * ggml_f16_epr; // choose 8 SVE registers
+        const int ggml_f16_epr = svcnth();
+        const int ggml_f16_step = 2 * ggml_f16_epr;
+        int np = n - (n % ggml_f16_step);
+        int np2 = n - (n % ggml_f16_epr);

-        int np = (n & ~(ggml_f16_step - 1));
-
-        svfloat16_t sum_00 = svdup_n_f16(0.0f);
-        svfloat16_t sum_01 = svdup_n_f16(0.0f);
-        svfloat16_t sum_02 = svdup_n_f16(0.0f);
-        svfloat16_t sum_03 = svdup_n_f16(0.0f);
-
-        svfloat16_t sum_10 = svdup_n_f16(0.0f);
-        svfloat16_t sum_11 = svdup_n_f16(0.0f);
-        svfloat16_t sum_12 = svdup_n_f16(0.0f);
-        svfloat16_t sum_13 = svdup_n_f16(0.0f);
-
-        svfloat16_t ax1, ax2, ax3, ax4, ax5, ax6, ax7, ax8;
-        svfloat16_t ay1, ay2, ay3, ay4, ay5, ay6, ay7, ay8;
+        svfloat32_t sum_0_0_lo = svdup_n_f32(0.0f);
+        svfloat32_t sum_0_0_hi = svdup_n_f32(0.0f);
+        svfloat32_t sum_0_1_lo = svdup_n_f32(0.0f);
+        svfloat32_t sum_0_1_hi = svdup_n_f32(0.0f);
+        svfloat32_t sum_1_0_lo = svdup_n_f32(0.0f);
+        svfloat32_t sum_1_0_hi = svdup_n_f32(0.0f);
+        svfloat32_t sum_1_1_lo = svdup_n_f32(0.0f);
+        svfloat32_t sum_1_1_hi = svdup_n_f32(0.0f);

        for (int i = 0; i < np; i += ggml_f16_step) {
-            ay1 = GGML_F16x_VEC_LOAD(y + i + 0 * ggml_f16_epr, 0); // 8 elements
+            const svfloat16_t ay0 = GGML_F16x_VEC_LOAD(y + i, 0);
+            const svfloat16_t ax00 = GGML_F16x_VEC_LOAD(x[0] + i, 0);
+            const svfloat16_t ax01 = GGML_F16x_VEC_LOAD(x[1] + i, 0);

-            ax1 = GGML_F16x_VEC_LOAD(x[0] + i + 0*ggml_f16_epr, 0); // 8 elements
-            sum_00 = GGML_F16x_VEC_FMA(sum_00, ax1, ay1);     // sum_00 = sum_00+ax1*ay1
-            ax1 = GGML_F16x_VEC_LOAD(x[1] + i + 0*ggml_f16_epr, 0); // 8 elements
-            sum_10 = GGML_F16x_VEC_FMA(sum_10, ax1, ay1);
+            ggml_sve_f16_fma_widened(&sum_0_0_lo, &sum_0_0_hi, ax00, ay0);
+            ggml_sve_f16_fma_widened(&sum_1_0_lo, &sum_1_0_hi, ax01, ay0);

-            ay2 = GGML_F16x_VEC_LOAD(y + i + 1 * ggml_f16_epr, 1); // next 8 elements
+            const svfloat16_t ay1 = GGML_F16x_VEC_LOAD(y + i + 1 * ggml_f16_epr, 0);
+            const svfloat16_t ax10 = GGML_F16x_VEC_LOAD(x[0] + i + 1 * ggml_f16_epr, 0);
+            const svfloat16_t ax11 = GGML_F16x_VEC_LOAD(x[1] + i + 1 * ggml_f16_epr, 0);

-            ax2 = GGML_F16x_VEC_LOAD(x[0] + i + 1*ggml_f16_epr, 1); // next 8 elements
-            sum_01 = GGML_F16x_VEC_FMA(sum_01, ax2, ay2);
-            ax2 = GGML_F16x_VEC_LOAD(x[1] + i + 1*ggml_f16_epr, 1);
-            sum_11 = GGML_F16x_VEC_FMA(sum_11, ax2, ay2);
-
-            ay3 = GGML_F16x_VEC_LOAD(y + i + 2 * ggml_f16_epr, 2);
-
-            ax3 = GGML_F16x_VEC_LOAD(x[0] + i + 2*ggml_f16_epr, 2);
-            sum_02 = GGML_F16x_VEC_FMA(sum_02, ax3, ay3);
-            ax3 = GGML_F16x_VEC_LOAD(x[1] + i + 2*ggml_f16_epr, 2);
-            sum_12 = GGML_F16x_VEC_FMA(sum_12, ax3, ay3);
-
-            ay4 = GGML_F16x_VEC_LOAD(y + i + 3 * ggml_f16_epr, 3);
-
-            ax4 = GGML_F16x_VEC_LOAD(x[0] + i + 3*ggml_f16_epr, 3);
-            sum_03 = GGML_F16x_VEC_FMA(sum_03, ax4, ay4);
-            ax4 = GGML_F16x_VEC_LOAD(x[1] + i + 3*ggml_f16_epr, 3);
-            sum_13 = GGML_F16x_VEC_FMA(sum_13, ax4, ay4);
-
-            ay5 = GGML_F16x_VEC_LOAD(y + i + 4 * ggml_f16_epr, 4);
-
-            ax5 = GGML_F16x_VEC_LOAD(x[0] + i + 4*ggml_f16_epr, 4);
-
-            sum_00 = GGML_F16x_VEC_FMA(sum_00, ax5, ay5);
-            ax5 = GGML_F16x_VEC_LOAD(x[1] + i + 4*ggml_f16_epr, 4);
-            sum_10 = GGML_F16x_VEC_FMA(sum_10, ax5, ay5);
-
-            ay6 = GGML_F16x_VEC_LOAD(y + i + 5 * ggml_f16_epr, 5);
-
-            ax6 = GGML_F16x_VEC_LOAD(x[0] + i + 5*ggml_f16_epr, 5);
-
-            sum_01 = GGML_F16x_VEC_FMA(sum_01, ax6, ay6);
-            ax6 = GGML_F16x_VEC_LOAD(x[1] + i + 5*ggml_f16_epr, 5);
-            sum_11 = GGML_F16x_VEC_FMA(sum_11, ax6, ay6);
-
-            ay7 = GGML_F16x_VEC_LOAD(y + i + 6 * ggml_f16_epr, 6);
-
-            ax7 = GGML_F16x_VEC_LOAD(x[0] + i + 6*ggml_f16_epr, 6);
-
-            sum_02 = GGML_F16x_VEC_FMA(sum_02, ax7, ay7);
-            ax7 = GGML_F16x_VEC_LOAD(x[1] + i + 6*ggml_f16_epr, 6);
-            sum_12 = GGML_F16x_VEC_FMA(sum_12, ax7, ay7);
-
-            ay8 = GGML_F16x_VEC_LOAD(y + i + 7 * ggml_f16_epr, 7);
-
-            ax8 = GGML_F16x_VEC_LOAD(x[0] + i + 7*ggml_f16_epr, 7);
-
-            sum_03 = GGML_F16x_VEC_FMA(sum_03, ax8, ay8);
-            ax8 = GGML_F16x_VEC_LOAD(x[1] + i + 7*ggml_f16_epr, 7);
-            sum_13 = GGML_F16x_VEC_FMA(sum_13, ax8, ay8);
+            ggml_sve_f16_fma_widened(&sum_0_1_lo, &sum_0_1_hi, ax10, ay1);
+            ggml_sve_f16_fma_widened(&sum_1_1_lo, &sum_1_1_hi, ax11, ay1);
        }

-        const int np2 = (n & ~(ggml_f16_epr - 1));
-        for (int k = np; k < np2; k += ggml_f16_epr) {
-            svfloat16_t ry = GGML_F16x_VEC_LOAD(y + k, 0);
+        for (int i = np; i < np2; i += ggml_f16_epr) {
+            const svfloat16_t ry = GGML_F16x_VEC_LOAD(y + i, 0);
+            const svfloat16_t rx0 = GGML_F16x_VEC_LOAD(x[0] + i, 0);
+            const svfloat16_t rx1 = GGML_F16x_VEC_LOAD(x[1] + i, 0);

-            svfloat16_t rx = GGML_F16x_VEC_LOAD(x[0] + k, 0);
-            sum_00 = GGML_F16x_VEC_FMA(sum_00, rx, ry);
-            rx = GGML_F16x_VEC_LOAD(x[1] + k, 0);
-            sum_10 = GGML_F16x_VEC_FMA(sum_10, rx, ry);
+            ggml_sve_f16_fma_widened(&sum_0_0_lo, &sum_0_0_hi, rx0, ry);
+            ggml_sve_f16_fma_widened(&sum_1_0_lo, &sum_1_0_hi, rx1, ry);
        }

        if (np2 < n) {
-            svbool_t pg = svwhilelt_b16(np2, n);
-            svfloat16_t hx_0 = svld1_f16(pg, (const __fp16 *)(x[0] + np2));
-            svfloat16_t hx_1 = svld1_f16(pg, (const __fp16 *)(x[1] + np2));
-            svfloat16_t hy = svld1_f16(pg, (const __fp16 *)(y + np2));
+            const svbool_t pg = svwhilelt_b16(np2, n);
+            const svfloat16_t ay = svld1_f16(pg, (const __fp16 *)(y + np2));
+            const svfloat16_t ax0 = svld1_f16(pg, (const __fp16 *)(x[0] + np2));
+            const svfloat16_t ax1 = svld1_f16(pg, (const __fp16 *)(x[1] + np2));

-            sum_00 = svmad_f16_x(pg, hx_0, hy, sum_00);
-            sum_10 = svmad_f16_x(pg, hx_1, hy, sum_10);
+            ggml_sve_f16_fma_widened(&sum_0_0_lo, &sum_0_0_hi, ax0, ay);
+            ggml_sve_f16_fma_widened(&sum_1_0_lo, &sum_1_0_hi, ax1, ay);
        }
-        GGML_F16x_VEC_REDUCE(sumf[0], sum_00, sum_01, sum_02, sum_03);
-        GGML_F16x_VEC_REDUCE(sumf[1], sum_10, sum_11, sum_12, sum_13);
+
+        svfloat32_t sum_0_lo = svadd_f32_x(DEFAULT_PG32, sum_0_0_lo, sum_0_1_lo);
+        svfloat32_t sum_0_hi = svadd_f32_x(DEFAULT_PG32, sum_0_0_hi, sum_0_1_hi);
+        svfloat32_t sum_1_lo = svadd_f32_x(DEFAULT_PG32, sum_1_0_lo, sum_1_1_lo);
+        svfloat32_t sum_1_hi = svadd_f32_x(DEFAULT_PG32, sum_1_0_hi, sum_1_1_hi);
+        sumf[0] = ggml_sve_sum_f32x2(sum_0_lo, sum_0_hi);
+        sumf[1] = ggml_sve_sum_f32x2(sum_1_lo, sum_1_hi);
        np = n;
    #elif defined(__riscv_v_intrinsic)
        #if defined(__riscv_zvfh)
@@ -7,6 +7,7 @@
 #include <cstdint>
 #include <cstdlib>
 #include <memory>
+#include <mutex>

 #if defined(GGML_USE_HIP)
 #define GGML_COMMON_DECL_HIP
@@ -1552,6 +1553,62 @@ struct ggml_cuda_pdl_config {
    ggml_cuda_pdl_config& operator=(ggml_cuda_pdl_config&&) = delete;

 };
+
+static bool ggml_cuda_kernel_can_use_pdl(const void * kernel) {
+    const int device = ggml_cuda_get_device();
+
+    struct cache_key {
+        int          device;
+        const void * kernel;
+
+        bool operator==(const cache_key & other) const { return device == other.device && kernel == other.kernel; }
+    };
+
+    struct cache_key_hash {
+        // MurmurHash3 mixing function for better hash distribution (vs. just std::hash which in some implementations simply returns the identity)
+        static size_t hash_mix(size_t x) {
+            std::uint64_t       y = x;
+            const std::uint64_t m = 0xe9846af9b1a615d;
+
+            y ^= y >> 32;
+            y *= m;
+            y ^= y >> 32;
+            y *= m;
+            y ^= y >> 28;
+
+            return static_cast<size_t>(y);
+        }
+
+        size_t operator()(const cache_key & key) const {
+            // Use a nonzero seed to avoid mapping all-zero keys to zero
+            size_t h = 42;
+            h        = hash_mix(h + key.device);
+            h        = hash_mix(h + reinterpret_cast<size_t>(key.kernel));
+            return h;
+        }
+    };
+
+    static std::mutex                                          cache_mutex;
+    static std::unordered_map<cache_key, bool, cache_key_hash> cache;
+
+    const cache_key             key = { device, kernel };
+    std::lock_guard<std::mutex> lock(cache_mutex);
+    const auto                  it = cache.find(key);
+    if (it != cache.end()) {
+        return it->second;
+    }
+
+    cudaFuncAttributes attr = {};
+    CUDA_CHECK(cudaFuncGetAttributes(&attr, kernel));
+
+    // PDL device-side primitives are emitted only for PTX versions >= 90.
+    // We have to guard on a loaded kernel's PTX version so a kernel forward-JIT'ed
+    // from pre-Hopper PTX to a Hopper-or-newer GPU does not opt into PDL.
+    const bool can_use_pdl = attr.ptxVersion >= 90;
+    cache.emplace(key, can_use_pdl);
+    return can_use_pdl;
+}
+
 #endif //defined(GGML_CUDA_USE_PDL)


@@ -1564,8 +1621,7 @@ static __inline__ void ggml_cuda_kernel_launch(Kernel kernel, const ggml_cuda_ke
        return env == nullptr || std::atoi(env) != 0;
    }();

-    const int cc = ggml_cuda_info().devices[ggml_cuda_get_device()].cc;
-    if (env_pdl_enabled && ggml_cuda_highest_compiled_arch(cc) >= GGML_CUDA_CC_HOPPER) {
+    if (env_pdl_enabled && ggml_cuda_kernel_can_use_pdl(reinterpret_cast<const void *>(kernel))) {
        auto pdl_cfg = ggml_cuda_pdl_config(launch_params);

        CUDA_CHECK(cudaLaunchKernelEx(&pdl_cfg.cfg, kernel, std::forward<Args>(args)... ));
@@ -1153,8 +1153,8 @@ void launch_fattn(

    GGML_ASSERT(block_dim.x % warp_size == 0);

-    const ggml_cuda_kernel_launch_params launch_params = ggml_cuda_kernel_launch_params(blocks_num, block_dim, nbytes_shared, main_stream);
-    ggml_cuda_kernel_launch(fattn_kernel, launch_params,
+        // disabled PDL enrollment for now due to a compiler bug.
+        fattn_kernel<<<blocks_num, block_dim, nbytes_shared, main_stream>>>(
        (const char *) Q->data,
        K_data,
        V_data,
@@ -472,7 +472,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_load_mask(

            const int i = 8 * (threadIdx.x % (nbatch_fa/8));

-            cp_async_cg_16<preload>(tile_mask_32 + j_sram*(nbatch_fa*sizeof(half) + 16) + i*sizeof(half), mask_h + j_vram*stride_mask + i);
+            cp_async_cg_16<preload>(tile_mask_32 + j_sram*(nbatch_fa*sizeof(half) + 16) + i*sizeof(half), mask_h + int64_t(j_vram)*stride_mask + i);
        }
    } else if constexpr (oob_check) {
 #pragma unroll
@@ -488,7 +488,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_load_mask(
            for (int i0 = 0; i0 < nbatch_fa; i0 += warp_size) {
                const int i = i0 + threadIdx.x;

-                tile_mask[j_sram*(nbatch_fa + 8) + i] = i < i_sup ? mask_h[j_vram*stride_mask + i] : half(0.0f);
+                tile_mask[j_sram*(nbatch_fa + 8) + i] = i < i_sup ? mask_h[int64_t(j_vram)*stride_mask + i] : half(0.0f);
            }
        }
    } else if constexpr (nbatch_fa < 2*warp_size) {
@@ -505,7 +505,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_load_mask(

            const int i = threadIdx.x % (warp_size/cols_per_warp);

-            ggml_cuda_memcpy_1<sizeof(half2)>(tile_mask + j_sram*(nbatch_fa + 8) + 2*i, mask_h + j_vram*stride_mask + 2*i);
+            ggml_cuda_memcpy_1<sizeof(half2)>(tile_mask + j_sram*(nbatch_fa + 8) + 2*i, mask_h + int64_t(j_vram)*stride_mask + 2*i);
        }
    } else {
 #pragma unroll
@@ -521,7 +521,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_load_mask(
            for (int i0 = 0; i0 < nbatch_fa; i0 += 2*warp_size) {
                const int i = i0 + 2*threadIdx.x;

-                ggml_cuda_memcpy_1<sizeof(half2)>(tile_mask + j_sram*(nbatch_fa + 8) + i, mask_h + j_vram*stride_mask + i);
+                ggml_cuda_memcpy_1<sizeof(half2)>(tile_mask + j_sram*(nbatch_fa + 8) + i, mask_h + int64_t(j_vram)*stride_mask + i);
            }
        }
    }
@@ -568,7 +568,6 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter(
    constexpr bool Q_in_reg        = ggml_cuda_fattn_mma_get_Q_in_reg (DKQ, DV, ncols);
    constexpr int  nstages         = ggml_cuda_fattn_mma_get_nstages  (DKQ, DV, ncols1, ncols2);

-    constexpr int stride_tile_Q = DKQ/2     + 4;
    constexpr int stride_tile_K = nbatch_K2 + 4;

    constexpr int stride_tile_V = V_is_K_view ? stride_tile_K : nbatch_V2 + 4;
@@ -604,9 +603,9 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter(
 #pragma unroll
    for (int k0_start = (DKQ/2-1) - (DKQ/2-1) % nbatch_K2; k0_start >= 0; k0_start -= nbatch_K2) {
        const int k0_stop = k0_start + nbatch_K2 < DKQ/2 ? k0_start + nbatch_K2 : DKQ/2;
-        const int k0_diff = k0_stop - k0_start;

        if constexpr (nstages <= 1) {
+            const int k0_diff = k0_stop - k0_start;
            constexpr bool use_cp_async = nstages == 1;
            flash_attn_ext_f16_load_tile<stride_tile_K, nwarps, nbatch_fa, use_cp_async, oob_check>
                (K_h2 + int64_t(k_VKQ_0)*stride_K + k0_start, tile_K, k0_diff, stride_K, k_VKQ_sup);
@@ -640,6 +639,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter(
                }
            }
        } else {
+            constexpr int stride_tile_Q = DKQ/2 + 4;
 #pragma unroll
            for (int k_KQ_0 = k0_start; k_KQ_0 < k0_stop; k_KQ_0 += T_A_KQ::J) {
                load_ldmatrix(Q_B[0], tile_Q + (threadIdx.y / np)*(T_B_KQ::I*stride_tile_Q) + k_KQ_0, stride_tile_Q);
@@ -954,9 +954,9 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter(
    for (int i0_start = 0; i0_start < DV; i0_start += 2*nbatch_V2) {
        static_assert(DV % (2*nbatch_V2) == 0, "bad loop size");
        const int i0_stop = i0_start + 2*nbatch_V2;
-        const int i0_diff = i0_stop - i0_start;

        if constexpr (nstages <= 1) {
+            const int i0_diff = i0_stop - i0_start;
            if (!V_is_K_view || i0_stop > 2*nbatch_K2) {
                constexpr bool use_cp_async = nstages == 1;
                flash_attn_ext_f16_load_tile<stride_tile_V, nwarps, nbatch_fa, use_cp_async, oob_check>
@@ -43,7 +43,6 @@ gated_delta_net_cuda(const float * q,
    // output state layout (per-slot D * n_seqs) — same per-(seq,head) offset as before.
    const int64_t state_in_offset      = sequence * K * H * S_v * S_v + h_idx * S_v * S_v;
    const int64_t state_out_offset     = (sequence * H + h_idx) * S_v * S_v;
-    const int64_t state_size_per_token = S_v * S_v * H * n_seqs; // per-slot stride in output
    state += state_out_offset;
    curr_state += state_in_offset + col * S_v;
    attn_data += (sequence * n_tokens * H + h_idx) * S_v;
@@ -61,10 +60,6 @@ gated_delta_net_cuda(const float * q,
        s_shard[r]  = curr_state[i];
    }

-    // slot mapping: target_slot = t - shift. When n_tokens < K only the last n_tokens slots
-    // are written; earlier slots are left untouched (caller-owned).
-    const int shift = (int) n_tokens - K;
-
    for (int t = 0; t < n_tokens; t++) {
        const float * q_t = q + iq3 * sq3 + t * sq2 + iq1 * sq1;
        const float * k_t = k + iq3 * sq3 + t * sq2 + iq1 * sq1;
@@ -148,6 +143,11 @@ gated_delta_net_cuda(const float * q,
        attn_data += S_v * H;

        if constexpr (keep_rs_t) {
+            // slot mapping: target_slot = t - shift. When n_tokens < K only the last n_tokens slots
+            // are written; earlier slots are left untouched (caller-owned).
+            const int shift = (int) n_tokens - K;
+
+            const int64_t state_size_per_token = S_v * S_v * H * n_seqs; // per-slot stride in output
            const int target_slot = t - shift;
            if (target_slot >= 0 && target_slot < K) {
                float * curr_state = (dst + attn_score_elems) + target_slot * state_size_per_token + state_out_offset;
@@ -2570,6 +2570,7 @@ static void ggml_cuda_mul_mat(ggml_backend_cuda_context & ctx, const ggml_tensor
            use_mul_mat_q           = use_mul_mat_q             && ggml_cuda_should_use_mmq(src0->type, cc, src1->ne[1], /*n_experts=*/0);
            use_mul_mat_f           = use_mul_mat_f             && ggml_cuda_should_use_mmf(src0->type, cc, warp_size, src0->ne, src0->nb, src1->ne[1], /*mul_mat_id=*/false);
            use_mul_mat_vec_f       = use_mul_mat_vec_f         && ggml_cuda_should_use_mmvf(src0->type, cc, src0->ne, src0->nb, src1->ne[1]);
+            use_mul_mat_vec_q       = use_mul_mat_vec_q         && ggml_cuda_should_use_mmvq(src0->type, cc, src1->ne[1]);
            any_gpus_with_slow_fp16 = any_gpus_with_slow_fp16   || !fast_fp16_hardware_available(cc);
        }
    } else {
@@ -2578,6 +2579,7 @@ static void ggml_cuda_mul_mat(ggml_backend_cuda_context & ctx, const ggml_tensor
        use_mul_mat_q           = use_mul_mat_q             && ggml_cuda_should_use_mmq(src0->type, cc, src1->ne[1], /*n_experts=*/0);
        use_mul_mat_f           = use_mul_mat_f             && ggml_cuda_should_use_mmf(src0->type, cc, warp_size, src0->ne, src0->nb, src1->ne[1], /*mul_mat_id=*/false);
        use_mul_mat_vec_f       = use_mul_mat_vec_f         && ggml_cuda_should_use_mmvf(src0->type, cc, src0->ne, src0->nb, src1->ne[1]);
+        use_mul_mat_vec_q       = use_mul_mat_vec_q         && ggml_cuda_should_use_mmvq(src0->type, cc, src1->ne[1]);
        any_gpus_with_slow_fp16 = any_gpus_with_slow_fp16   || !fast_fp16_hardware_available(cc);
    }

@@ -4992,8 +4994,14 @@ static void ggml_backend_cuda_device_get_memory(ggml_backend_dev_t dev, size_t *
 }

 static enum ggml_backend_dev_type ggml_backend_cuda_device_get_type(ggml_backend_dev_t dev) {
-    GGML_UNUSED(dev);
-    return GGML_BACKEND_DEVICE_TYPE_GPU;
+    ggml_backend_cuda_device_context * ctx = (ggml_backend_cuda_device_context *) dev->context;
+
+    cudaDeviceProp prop;
+    CUDA_CHECK(cudaGetDeviceProperties(&prop, ctx->device));
+
+    return prop.integrated
+        ? GGML_BACKEND_DEVICE_TYPE_IGPU
+        : GGML_BACKEND_DEVICE_TYPE_GPU;
 }

 static void ggml_backend_cuda_device_get_props(ggml_backend_dev_t dev, ggml_backend_dev_props * props) {
@@ -91,7 +91,7 @@ static __global__ void mul_mat_f(
    const int row0        = blockIdx.x * rows_per_block;

    int expert_idx = 0;
-    int col_base = 0;
+    [[maybe_unused]] int col_base = 0;

    const int channel_dst = has_ids ? 0 : blockIdx.y;

@@ -122,12 +122,12 @@ static __global__ void mul_mat_f(
        ids += col_offset * stride_row_id;
    }

-    const float2 * y2 = (const float2 *) y;
+    [[maybe_unused]] const float2 * y2 = (const float2 *) y;

    extern __shared__ char data_mmv[];

    char * shmem_base = data_mmv;
-    int  * slot_map   = (int *) shmem_base;
+    [[maybe_unused]] int * slot_map = (int *) shmem_base;
    char * compute_base = has_ids ? (shmem_base + GGML_PAD(cols_per_block, 16) * sizeof(int)) : shmem_base;

    tile_C C[ntA][ntB];
@@ -80,9 +80,8 @@ static __global__ void mul_mat_vec_f(
        gate_x += int64_t(sample_x)  *stride_sample_x   + channel_x  *stride_channel_x   + row*stride_row;
    }

-    const int channel_bias = ids ? channel_x : channel_dst;
-
    if constexpr (has_fusion) {
+        const int channel_bias = ids ? channel_x : channel_dst;
        if (use_bias) {
            x_bias += int64_t(sample_dst)*stride_sample_dst + channel_bias*stride_channel_dst;
        }
@@ -95,7 +94,7 @@ static __global__ void mul_mat_vec_f(

    extern __shared__ char data_mmv[];
    float * buf_iw = (float *) data_mmv;
-    float * buf_iw_gate = nullptr;
+    [[maybe_unused]] float * buf_iw_gate = nullptr;
    if constexpr (has_fusion) {
        buf_iw_gate = (float *) (data_mmv + warp_size*sizeof(float));
    }
@@ -123,7 +122,7 @@ static __global__ void mul_mat_vec_f(

    if constexpr (std::is_same_v<T, float>) {
        const float2 * x2 = (const float2 *) x;
-        const float2 * gate_x2 = nullptr;
+        [[maybe_unused]] const float2 * gate_x2 = nullptr;
        if constexpr (has_fusion) {
            if (use_gate) {
                gate_x2 = (const float2 *) gate_x;
@@ -155,7 +154,7 @@ static __global__ void mul_mat_vec_f(
        }
    } else if constexpr (std::is_same_v<T, half>) {
        const half2 * x2 = (const half2 *) x;
-        const half2 * gate_x2 = nullptr;
+        [[maybe_unused]] const half2 * gate_x2 = nullptr;
        if constexpr (has_fusion) {
            if (use_gate) {
                gate_x2 = (const half2 *) gate_x;
@@ -266,7 +265,7 @@ static __global__ void mul_mat_vec_f(
        }
 #else
        const nv_bfloat162 * x2 = (const nv_bfloat162 *) x;
-        const nv_bfloat162 * gate_x2 = nullptr;
+        [[maybe_unused]] const nv_bfloat162 * gate_x2 = nullptr;
        if constexpr (has_fusion) {
            if (use_gate) {
                gate_x2 = (const nv_bfloat162 *) gate_x;
@@ -274,7 +273,7 @@ static __global__ void mul_mat_vec_f(
        }
        for (int col2 = tid; col2 < ncols2; col2 += block_size) {
            const nv_bfloat162 tmpx = x2[col2];
-            nv_bfloat162 tmpx_gate;
+            [[maybe_unused]] nv_bfloat162 tmpx_gate;
            if constexpr (has_fusion) {
                if (use_gate) {
                    tmpx_gate = gate_x2[col2];
@@ -63,6 +63,7 @@ static constexpr __host__ __device__ int get_vdr_mmvq(ggml_type type) {

 enum mmvq_parameter_table_id {
    MMVQ_PARAMETERS_GENERIC = 0,
+    MMVQ_PARAMETERS_TURING,
    MMVQ_PARAMETERS_GCN,
    MMVQ_PARAMETERS_RDNA2,
    MMVQ_PARAMETERS_RDNA3_0,
@@ -78,6 +79,8 @@ static constexpr __device__ mmvq_parameter_table_id get_device_table_id() {
    return MMVQ_PARAMETERS_RDNA2;
 #elif defined(GCN) || defined(CDNA)
    return MMVQ_PARAMETERS_GCN;
+#elif defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= GGML_CUDA_CC_TURING && __CUDA_ARCH__ < GGML_CUDA_CC_AMPERE
+    return MMVQ_PARAMETERS_TURING;
 #else
    return MMVQ_PARAMETERS_GENERIC;
 #endif
@@ -96,6 +99,9 @@ static __host__ mmvq_parameter_table_id get_device_table_id(int cc) {
    if (GGML_CUDA_CC_IS_GCN(cc) || GGML_CUDA_CC_IS_CDNA(cc)) {
        return MMVQ_PARAMETERS_GCN;
    }
+    if (GGML_CUDA_CC_IS_NVIDIA(cc) && ggml_cuda_highest_compiled_arch(cc) >= GGML_CUDA_CC_TURING && ggml_cuda_highest_compiled_arch(cc) < GGML_CUDA_CC_AMPERE) {
+        return MMVQ_PARAMETERS_TURING;
+    }
    return MMVQ_PARAMETERS_GENERIC;
 }

@@ -271,6 +277,53 @@ int get_mmvq_mmid_max_batch(ggml_type type, int cc) {
    return MMVQ_MAX_BATCH_SIZE;
 }

+bool ggml_cuda_should_use_mmvq(enum ggml_type type, int cc, int64_t ne11) {
+    if (GGML_CUDA_CC_IS_CDNA(cc)) {
+        if (GGML_CUDA_CC_IS_CDNA1(cc)) {
+            switch (type) {
+                case GGML_TYPE_Q4_0:
+                case GGML_TYPE_Q4_1:
+                    return ne11 <= 7;
+                case GGML_TYPE_Q5_1:
+                    return ne11 <= 7;
+                case GGML_TYPE_Q8_0:
+                    return ne11 <= 6;
+                case GGML_TYPE_Q2_K:
+                    return ne11 <= 4;
+                case GGML_TYPE_Q3_K:
+                    return ne11 <= 3;
+                case GGML_TYPE_Q4_K:
+                    return ne11 <= 2;
+                case GGML_TYPE_Q5_K:
+                    return ne11 <= 3;
+                case GGML_TYPE_Q6_K:
+                    return ne11 <= 4;
+                case GGML_TYPE_IQ1_S:
+                    return ne11 <= 5;
+                case GGML_TYPE_IQ2_XXS:
+                case GGML_TYPE_IQ3_S:
+                case GGML_TYPE_IQ4_XS:
+                    return ne11 <= 6;
+                default:
+                    return ne11 <= MMVQ_MAX_BATCH_SIZE;
+            }
+        }
+        switch (type) { // tuned for CDNA2
+            case GGML_TYPE_Q2_K:
+                return ne11 <= 5;
+            case GGML_TYPE_Q3_K:
+            case GGML_TYPE_Q4_K:
+            case GGML_TYPE_Q5_K:
+                return ne11 <= 3;
+            case GGML_TYPE_Q6_K:
+                return ne11 <= 5;
+            default:
+                return ne11 <= MMVQ_MAX_BATCH_SIZE;
+        }
+    }
+    return ne11 <= MMVQ_MAX_BATCH_SIZE;
+}
+
 // Device constexpr: returns the max batch size for the current arch+type at compile time.
 template <ggml_type type>
 static constexpr __device__ int get_mmvq_mmid_max_batch_for_device() {
@@ -370,11 +423,38 @@ static constexpr __host__ __device__ int calc_nwarps(ggml_type type, int ncols_d
        }
        return 1;
    }
+    if (table_id == MMVQ_PARAMETERS_TURING) {
+        if (ncols_dst == 1) {
+            switch (type) {
+                case GGML_TYPE_Q2_K:
+                case GGML_TYPE_Q3_K:
+                case GGML_TYPE_Q4_K:
+                case GGML_TYPE_Q5_K:
+                case GGML_TYPE_Q6_K:
+                    return 2;
+                default:
+                    return 4;
+            }
+        }
+        switch (ncols_dst) {
+            case 2:
+            case 3:
+            case 4:
+                return 4;
+            case 5:
+            case 6:
+            case 7:
+            case 8:
+                return 2;
+            default:
+                return 1;
+        }
+    }
    return 1;
 }

 static constexpr __host__ __device__ int calc_rows_per_block(int ncols_dst, int table_id, bool small_k = false, int nwarps = 1) {
-    if (table_id == MMVQ_PARAMETERS_GENERIC || table_id == MMVQ_PARAMETERS_GCN) {
+    if (table_id == MMVQ_PARAMETERS_GENERIC || table_id == MMVQ_PARAMETERS_GCN || table_id == MMVQ_PARAMETERS_TURING) {
        switch (ncols_dst) {
            case 1:
                return small_k ? nwarps : 1;
@@ -435,7 +515,7 @@ static __global__ void mul_mat_vec_q(
    bool use_gate = false;
    bool use_bias = false;
    bool use_gate_bias = false;
-    const void * vgate = nullptr;
+    [[maybe_unused]] const void * vgate = nullptr;
    const float * x_bias = nullptr;
    const float * gate_bias = nullptr;
    ggml_glu_op active_glu;
@@ -451,8 +531,8 @@ static __global__ void mul_mat_vec_q(
    }


-    float x_biases[ncols_dst]    = { 0.0f };
-    float gate_biases[ncols_dst] = { 0.0f };
+    [[maybe_unused]] float x_biases[ncols_dst]    = { 0.0f };
+    [[maybe_unused]] float gate_biases[ncols_dst] = { 0.0f };
    if constexpr (has_fusion) {
        const uint32_t channel_bias = ids ? channel_x : channel_dst;
        if (use_bias) {
@@ -509,12 +589,7 @@ static __global__ void mul_mat_vec_q(
    }

    __shared__ float tmp_shared[nwarps-1 > 0 ? nwarps-1 : 1][ncols_dst][rows_per_cuda_block][warp_size];
-    __shared__ float tmp_shared_gate[(has_fusion && (nwarps-1 > 0)) ? nwarps-1 : 1][ncols_dst][rows_per_cuda_block][warp_size];
-    if constexpr (!has_fusion) {
-        (void) tmp_shared_gate;
-    } else if (!use_gate) {
-        (void) tmp_shared_gate;
-    }
+    [[maybe_unused]] __shared__ float tmp_shared_gate[(has_fusion && (nwarps-1 > 0)) ? nwarps-1 : 1][ncols_dst][rows_per_cuda_block][warp_size];

    if (threadIdx.y > 0) {
 #pragma unroll
@@ -2,6 +2,8 @@

 #define MMVQ_MAX_BATCH_SIZE 8 // Max. batch size for which to use MMVQ kernels.

+bool ggml_cuda_should_use_mmvq(enum ggml_type type, int cc, int64_t ne11);
+
 // Returns the maximum batch size for which MMVQ should be used for MUL_MAT_ID,
 // based on the quantization type and GPU architecture (compute capability).
 int get_mmvq_mmid_max_batch(ggml_type type, int cc);
@@ -134,7 +134,7 @@ __launch_bounds__(4 * WARP_SIZE, 1) __global__ void topk_moe_cuda(const float *

    // selection_wt is only needed when bias is present (selection uses wt + bias)
    // when no bias, we use wt directly for both selection and weight values
-    float selection_wt[has_bias ? experts_per_thread : 1];
+    [[maybe_unused]] float selection_wt[has_bias ? experts_per_thread : 1];

    if constexpr (has_bias) {
 #pragma unroll
@@ -39,7 +39,7 @@
 #include "ggml-hexagon.h"
 #include "ggml-impl.h"
 #include "ggml-quants.h"
-#include "op-desc.h"
+#include "htp-opnode.h"
 #include "htp-ops.h"
 #include "htp_iface.h"
 #include "htp-drv.h"
@@ -68,6 +68,7 @@ static u32vec opt_pmu_evt { 0x3, 0x111, 0x100, 0x105, 0x240, 0x256, 0x7D, 0x8C }
 static int opt_opstage  = HTP_OPSTAGE_QUEUE | HTP_OPSTAGE_COMPUTE;
 static int opt_opbatch  = 1024; // max number of ops in a batch
 static int opt_opqueue  = 16;   // max number of pending batches
+static int opt_oppoll   = 0;    // polling for batch completions

 static std::regex* opt_opfilter = NULL; // regex of ops to not claim

@@ -101,23 +102,23 @@ static const char * status_to_str(uint32_t status) {

 // ** debug helpers

-static void ggml_hexagon_dump_op_exec(const std::string &sess_name, const ggml_tensor * op, const uint32_t req_flags) {
+static void ggml_hexagon_dump_op_exec(const std::string &sess_name, const htp_opnode & node, const uint32_t req_flags) {
    if (!opt_verbose) return;

-    op_desc desc(op);
+    htp_opformat fmt(node);
    GGML_LOG_DEBUG("ggml-hex: %s execute-op %s: %s : %s : %s : %s : %s : flags 0x%x\n", sess_name.c_str(),
-                ggml_op_desc(op), desc.names, desc.dims, desc.types, desc.strides, desc.buffs, req_flags);
+                node.op_name().c_str(), fmt.names, fmt.dims, fmt.types, fmt.strides, fmt.buffs, req_flags);
 }

 static void ggml_hexagon_dump_op_supp(const std::string &sess_name, const struct ggml_tensor * op, bool supp) {
    if (!opt_verbose) return;

-    op_desc desc(op);
+    htp_opformat fmt(htp_opformat(htp_opnode{const_cast<ggml_tensor*>(op), {}, HTP_OP_INVALID}));
    GGML_LOG_DEBUG("ggml-hex: %s supports-op %s: %s : %s : %s : %s : %s : %s\n", sess_name.c_str(),
-                ggml_op_desc(op), desc.names, desc.dims, desc.types, desc.strides, desc.buffs, supp ? "yes" : "no");
+                ggml_op_desc(op), fmt.names, fmt.dims, fmt.types, fmt.strides, fmt.buffs, supp ? "yes" : "no");
 }

-static void ggml_hexagon_dump_op_prof(const std::string &sess_name, const ggml_tensor * op,
+static void ggml_hexagon_dump_op_prof(const std::string &sess_name, const htp_opnode & node,
                                      uint32_t op_usec, uint32_t op_cycles, const uint32_t pmu[]) {
    if (!opt_profile) return;

@@ -128,15 +129,16 @@ static void ggml_hexagon_dump_op_prof(const std::string &sess_name, const ggml_t
                pmu[0], pmu[1], pmu[2], pmu[3], pmu[4], pmu[5], pmu[6], pmu[7]);
    }

-    op_desc desc(op);
+    htp_opformat fmt(node);
    GGML_LOG_DEBUG("ggml-hex: %s profile-op %s: %s : %s : %s : %s : usec %u cycles %u%s\n", sess_name.c_str(),
-            ggml_op_desc(op), desc.names, desc.dims, desc.types, desc.strides, op_usec, op_cycles, pmu_str);
+            node.op_name().c_str(), fmt.names, fmt.dims, fmt.types, fmt.strides, op_usec, op_cycles, pmu_str);
 }

 // ** backend sessions

 struct ggml_hexagon_opbatch;
 struct ggml_hexagon_opqueue;
+struct htp_opnode;

 struct ggml_hexagon_session {
    std::string      name;
@@ -166,7 +168,7 @@ struct ggml_hexagon_session {
    void allocate(int dev_id) noexcept(false);
    void release() noexcept(true);

-    void enqueue_op(htp_op_code opcode, const ggml_tensor *op);
+    void enqueue_op(const htp_opnode & node);
    void flush(bool all = true);

    void flush_pending(bool all = false);
@@ -550,7 +552,7 @@ static void repack_q4_0_q4x4x2(ggml_tensor * t, const void * data, size_t size)

    size_t row_size    = ggml_row_size(t->type, t->ne[0]);
    size_t row_size_pd = ggml_row_size(t->type, hex_round_up(t->ne[0], QK_Q4_0x4x2));  // extra elements for the pad
-    size_t row_size_rp = row_size * 2;  // extra space for tmp pad (if any)
+    size_t row_size_rp = row_size_pd;  // scratch must hold one full padded tile (qblk_size/2 quants + scales)

    // Ensure we don't try to read more data than is available in the source buffer 'data'
    // or write more than the tensor can hold.
@@ -611,7 +613,7 @@ static void repack_q4x4x2_q4_0(void * data, const ggml_tensor * t, size_t size)

    size_t row_size    = ggml_row_size(t->type, t->ne[0]);
    size_t row_size_pd = ggml_row_size(t->type, hex_round_up(t->ne[0], QK_Q4_0x4x2));  // extra elements for the pad
-    size_t row_size_rp = row_size * 2;  // extra space for tmp pad (if any)
+    size_t row_size_rp = row_size_pd;  // scratch must hold one full padded tile (qblk_size/2 quants + scales)

    // Ensure we don't try to copy more data than the tensor actually contains.
    const size_t total_tensor_size = (size_t)nrows * row_size;
@@ -660,6 +662,239 @@ static void repack_q4x4x2_q4_0(void * data, const ggml_tensor * t, size_t size)
    ggml_aligned_free(buf_rp, row_size_rp);
 }

+static void unpack_q4_1_quants(uint8_t * qs, const block_q4_1 * x, unsigned int bi) {
+    static const int qk = QK4_1;
+
+    for (unsigned int i = 0; i < qk / 2; ++i) {
+        const int x0             = (x->qs[i] & 0x0F);
+        const int x1             = (x->qs[i] >> 4);
+        qs[bi * qk + i + 0]      = x0;
+        qs[bi * qk + i + qk / 2] = x1;
+    }
+}
+
+static void pack_q4_1_quants(block_q4_1 * x, const uint8_t * qs, unsigned int bi) {
+    static const int qk = QK4_1;
+
+    for (unsigned int i = 0; i < qk / 2; ++i) {
+        const uint8_t x0 = qs[bi * qk + i + 0];
+        const uint8_t x1 = qs[bi * qk + i + qk / 2];
+        x->qs[i]         = x0 | (x1 << 4);
+    }
+}
+
+static void repack_row_q4_1x4x2(uint8_t * y, const block_q4_1 * x, int64_t k) {
+    static const int qk = QK_Q4_0x4x2;
+    const int        nb = (k + qk - 1) / qk;  // number of blocks (padded)
+    const int        nloe = k % qk;           // leftovers
+
+    const int dblk_size = 8 * 4;              // 8x (d, m) __fp16 = 32 bytes
+    const int qblk_size = qk / 2;             // int4 = 128 bytes
+    const int qrow_size = k / 2;              // int4 (not padded to blocks)
+
+    uint8_t * y_q = y + 0;                    // quants first
+    uint8_t * y_d = y + qrow_size;            // then scales/offsets
+
+    // Repack the quants
+    for (int i = 0; i < nb; i++) {
+        uint8_t qs[QK_Q4_0x4x2];  // unpacked quants
+        unpack_q4_1_quants(qs, &x[i * 8 + 0], 0);
+        unpack_q4_1_quants(qs, &x[i * 8 + 1], 1);
+        unpack_q4_1_quants(qs, &x[i * 8 + 2], 2);
+        unpack_q4_1_quants(qs, &x[i * 8 + 3], 3);
+        unpack_q4_1_quants(qs, &x[i * 8 + 4], 4);
+        unpack_q4_1_quants(qs, &x[i * 8 + 5], 5);
+        unpack_q4_1_quants(qs, &x[i * 8 + 6], 6);
+        unpack_q4_1_quants(qs, &x[i * 8 + 7], 7);
+
+        bool partial = (nloe && i == nb-1);
+
+        uint8_t * q = y_q + (i * qblk_size);
+        for (int j = 0; j < qk / 2; j++) {
+            q[j] = partial ? (qs[j*2+1] << 4) | qs[j*2+0] : (qs[j+128] << 4) | qs[j+000];
+        }
+    }
+
+    // Repack the scales and offsets
+    for (int i = 0; i < nb; i++) {
+        ggml_half * d_m = (ggml_half *) (y_d + i * dblk_size);
+        for (int j = 0; j < 8; j++) {
+            d_m[j * 2 + 0] = x[i * 8 + j].d;
+            d_m[j * 2 + 1] = x[i * 8 + j].m;
+        }
+    }
+}
+
+static void unpack_row_q4_1x4x2(block_q4_1 * x, const uint8_t * y, int64_t k) {
+    static const int qk = QK_Q4_0x4x2;
+    const int        nb = (k + qk - 1) / qk;  // number of blocks (padded)
+    const int        nloe = k % qk;           // leftovers
+
+    const int dblk_size = 8 * 4;              // 8x (d, m) __fp16 = 32 bytes
+    const int qblk_size = qk / 2;             // int4 = 128 bytes
+    const int qrow_size = k / 2;              // int4 (not padded to blocks)
+
+    const uint8_t * y_q = y + 0;              // quants first
+    const uint8_t * y_d = y + qrow_size;      // then scales/offsets
+
+    // Unpack the quants
+    for (int i = 0; i < nb; i++) {
+        uint8_t qs[QK_Q4_0x4x2];
+        bool partial = (nloe && i == nb-1);
+
+        const uint8_t * q = y_q + (i * qblk_size);
+        for (int j = 0; j < qk / 2; j++) {
+            if (partial) {
+                qs[j*2+0] = q[j] & 0x0F;
+                qs[j*2+1] = q[j] >> 4;
+            } else {
+                qs[j+000] = q[j] & 0x0F;
+                qs[j+128] = q[j] >> 4;
+            }
+        }
+
+        pack_q4_1_quants(&x[i * 8 + 0], qs, 0);
+        pack_q4_1_quants(&x[i * 8 + 1], qs, 1);
+        pack_q4_1_quants(&x[i * 8 + 2], qs, 2);
+        pack_q4_1_quants(&x[i * 8 + 3], qs, 3);
+        pack_q4_1_quants(&x[i * 8 + 4], qs, 4);
+        pack_q4_1_quants(&x[i * 8 + 5], qs, 5);
+        pack_q4_1_quants(&x[i * 8 + 6], qs, 6);
+        pack_q4_1_quants(&x[i * 8 + 7], qs, 7);
+    }
+
+    // Unpack the scales and offsets
+    for (int i = 0; i < nb; i++) {
+        const ggml_half * d_m = (const ggml_half *) (y_d + i * dblk_size);
+        for (int j = 0; j < 8; j++) {
+            x[i * 8 + j].d = d_m[j * 2 + 0];
+            x[i * 8 + j].m = d_m[j * 2 + 1];
+        }
+    }
+}
+
+static void init_row_q4_1x4x2(block_q4_1 * x, int64_t k) {
+    static const int qk = QK_Q4_0x4x2;
+    const int        nb = (k + qk - 1) / qk;  // number of blocks (padded)
+
+    uint8_t qs[QK_Q4_0x4x2];  // unpacked quants
+    memset(qs, 0, sizeof(qs));
+
+    for (int i = 0; i < nb; i++) {
+        pack_q4_1_quants(&x[i * 8 + 0], qs, 0);
+        pack_q4_1_quants(&x[i * 8 + 1], qs, 1);
+        pack_q4_1_quants(&x[i * 8 + 2], qs, 2);
+        pack_q4_1_quants(&x[i * 8 + 3], qs, 3);
+        pack_q4_1_quants(&x[i * 8 + 4], qs, 4);
+        pack_q4_1_quants(&x[i * 8 + 5], qs, 5);
+        pack_q4_1_quants(&x[i * 8 + 6], qs, 6);
+        pack_q4_1_quants(&x[i * 8 + 7], qs, 7);
+    }
+
+    for (int i = 0; i < nb; i++) {
+        for (int j = 0; j < 8; j++) {
+            x[i * 8 + j].d = 0;
+            x[i * 8 + j].m = 0;
+        }
+    }
+}
+
+static void repack_q4_1_q4x4x2(ggml_tensor * t, const void * data, size_t size) {
+    int64_t nrows = ggml_nrows(t);
+
+    size_t row_size    = ggml_row_size(t->type, t->ne[0]);
+    size_t row_size_pd = ggml_row_size(t->type, hex_round_up(t->ne[0], QK_Q4_0x4x2));
+    size_t row_size_rp = row_size_pd;  // scratch must hold one full padded tile (qblk_size/2 quants + scales)
+
+    const size_t total_tensor_size = (size_t)nrows * row_size;
+    const size_t n_bytes_to_copy = size < total_tensor_size ? size : total_tensor_size;
+
+    const int64_t n_full_rows = n_bytes_to_copy / row_size;
+    const size_t  n_rem_bytes = n_bytes_to_copy % row_size;
+
+    void * buf_pd = ggml_aligned_malloc(row_size_pd);
+    GGML_ASSERT(buf_pd != NULL);
+
+    void * buf_rp = ggml_aligned_malloc(row_size_rp);
+    GGML_ASSERT(buf_rp != NULL);
+
+    HEX_VERBOSE("ggml-hex: repack-q4_1-q4x4x2 %s : data %p size %zu dims %ldx%ld row-size %zu\n", t->name, data, size,
+                t->ne[0], nrows, row_size);
+
+    init_row_q4_1x4x2((block_q4_1 *) buf_pd, t->ne[0]);
+
+    for (int64_t i = 0; i < n_full_rows; i++) {
+        const uint8_t * src = (const uint8_t *) data + (i * row_size);
+        uint8_t *       dst = (uint8_t *) t->data + (i * row_size);
+
+        memcpy(buf_pd, src, row_size);
+        repack_row_q4_1x4x2((uint8_t *) buf_rp, (const block_q4_1 *) buf_pd, t->ne[0]);
+        memcpy(dst, buf_rp, row_size);
+    }
+
+    if (n_rem_bytes > 0) {
+        const int64_t i = n_full_rows;
+        const uint8_t * src = (const uint8_t *) data + (i * row_size);
+        uint8_t *       dst = (uint8_t *) t->data + (i * row_size);
+
+        init_row_q4_1x4x2((block_q4_1 *) buf_pd, t->ne[0]);
+        memcpy(buf_pd, src, n_rem_bytes);
+        repack_row_q4_1x4x2((uint8_t *) buf_rp, (const block_q4_1 *) buf_pd, t->ne[0]);
+        memcpy(dst, buf_rp, n_rem_bytes);
+    }
+
+    ggml_aligned_free(buf_pd, row_size_pd);
+    ggml_aligned_free(buf_rp, row_size_rp);
+}
+
+static void repack_q4x4x2_q4_1(void * data, const ggml_tensor * t, size_t size) {
+    int64_t nrows = ggml_nrows(t);
+
+    size_t row_size    = ggml_row_size(t->type, t->ne[0]);
+    size_t row_size_pd = ggml_row_size(t->type, hex_round_up(t->ne[0], QK_Q4_0x4x2));
+    size_t row_size_rp = row_size_pd;  // scratch must hold one full padded tile (qblk_size/2 quants + scales)
+
+    const size_t total_tensor_size = (size_t)nrows * row_size;
+    const size_t n_bytes_to_copy = size < total_tensor_size ? size : total_tensor_size;
+
+    const int64_t n_full_rows = n_bytes_to_copy / row_size;
+    const size_t  n_rem_bytes = n_bytes_to_copy % row_size;
+
+    void * buf_pd = ggml_aligned_malloc(row_size_pd);
+    GGML_ASSERT(buf_pd != NULL);
+
+    void * buf_rp = ggml_aligned_malloc(row_size_rp);
+    GGML_ASSERT(buf_rp != NULL);
+
+    HEX_VERBOSE("ggml-hex: repack-q4x4x2-q4_1 %s : data %p size %zu dims %ldx%ld row-size %zu\n", t->name, data, size,
+                t->ne[0], nrows, row_size);
+
+    memset(buf_rp, 0, row_size_rp);  // clear-out padded buffer to make sure the tail is all zeros
+
+    for (int64_t i = 0; i < n_full_rows; i++) {
+        const uint8_t * src = (const uint8_t *) t->data + (i * row_size);
+        uint8_t *       dst = (uint8_t *) data + (i * row_size);
+
+        memcpy(buf_rp, src, row_size);
+        unpack_row_q4_1x4x2((block_q4_1 *) buf_pd, (const uint8_t *) buf_rp, t->ne[0]);
+        memcpy(dst, buf_pd, row_size);
+    }
+
+    if (n_rem_bytes > 0) {
+        const int64_t i = n_full_rows;
+        const uint8_t * src = (const uint8_t *) t->data + (i * row_size);
+        uint8_t *       dst = (uint8_t *) data + (i * row_size);
+
+        // We still need to read and unpack the entire source row because quantization is block-based.
+        memcpy(buf_rp, src, row_size);
+        unpack_row_q4_1x4x2((block_q4_1 *) buf_pd, (const uint8_t *) buf_rp, t->ne[0]);
+        memcpy(dst, buf_pd, n_rem_bytes);
+    }
+
+    ggml_aligned_free(buf_pd, row_size_pd);
+    ggml_aligned_free(buf_rp, row_size_rp);
+}
+
 // ======== Q8x4x2 ====================
 static void dump_block_q8_0(const block_q8_0 * b, int i) {
    HEX_VERBOSE("ggml-hex: repack q8_0 %d: %d %d %d %d ... %d %d %d %d : %.6f\n", i, b->qs[0], b->qs[1], b->qs[2],
@@ -876,7 +1111,7 @@ static void repack_q8_0_q8x4x2(ggml_tensor * t, const void * data, size_t size)

    size_t row_size    = ggml_row_size(t->type, t->ne[0]);
    size_t row_size_pd = ggml_row_size(t->type, hex_round_up(t->ne[0], QK_Q8_0x4x2));  // extra elements for the pad
-    size_t row_size_rp = row_size * 2;  // extra space for tmp pad (if any)
+    size_t row_size_rp = row_size_pd;  // scratch must hold one full padded tile (qblk_size quants + scales)

    // Ensure we don't try to read more data than is available in the source buffer 'data'
    // or write more than the tensor can hold.
@@ -937,7 +1172,7 @@ static void repack_q8x4x2_q8_0(void * data, const ggml_tensor * t, size_t size)

    size_t row_size    = ggml_row_size(t->type, t->ne[0]);
    size_t row_size_pd = ggml_row_size(t->type, hex_round_up(t->ne[0], QK_Q8_0x4x2));  // extra elements for the pad
-    size_t row_size_rp = row_size * 2;  // extra space for tmp pad (if any)
+    size_t row_size_rp = row_size_pd;  // scratch must hold one full padded tile (qblk_size quants + scales)

    // Ensure we don't try to copy more data than the tensor actually contains.
    const size_t total_tensor_size = (size_t)nrows * row_size;
@@ -1238,7 +1473,7 @@ static void repack_mxfp4_mxfp4x4x2(ggml_tensor * t, const void * data, size_t si

    size_t row_size    = ggml_row_size(t->type, t->ne[0]);
    size_t row_size_pd = ggml_row_size(t->type, hex_round_up(t->ne[0], QK_MXFP4x4x2));  // extra elements for the pad
-    size_t row_size_rp = row_size * 2;  // extra space for tmp pad (if any)
+    size_t row_size_rp = row_size_pd;  // scratch must hold one full padded tile (qblk_size/2 quants + scales)

    // Ensure we don't try to read more data than is available in the source buffer 'data'
    // or write more than the tensor can hold.
@@ -1299,7 +1534,7 @@ static void repack_mxfp4x4x2_mxfp4(void * data, const ggml_tensor * t, size_t si

    size_t row_size    = ggml_row_size(t->type, t->ne[0]);
    size_t row_size_pd = ggml_row_size(t->type, hex_round_up(t->ne[0], QK_MXFP4x4x2));  // extra elements for the pad
-    size_t row_size_rp = row_size * 2;  // extra space for tmp pad (if any)
+    size_t row_size_rp = row_size_pd;  // scratch must hold one full padded tile (qblk_size/2 quants + scales)

    // Ensure we don't try to copy more data than the tensor actually contains.
    const size_t total_tensor_size = (size_t)nrows * row_size;
@@ -1365,6 +1600,12 @@ static void ggml_backend_hexagon_buffer_set_tensor(ggml_backend_buffer_t buffer,
            repack_q4_0_q4x4x2(tensor, data, size);
            break;

+        case GGML_TYPE_Q4_1:
+            GGML_ASSERT(offset == 0);
+            GGML_ASSERT(offset + size <= ggml_nbytes(tensor));
+            repack_q4_1_q4x4x2(tensor, data, size);
+            break;
+
        case GGML_TYPE_Q8_0:
            GGML_ASSERT(offset == 0);
            GGML_ASSERT(offset + size <= ggml_nbytes(tensor));
@@ -1407,6 +1648,12 @@ static void ggml_backend_hexagon_buffer_get_tensor(ggml_backend_buffer_t buffer,
            repack_q4x4x2_q4_0(data, tensor, size);
            break;

+        case GGML_TYPE_Q4_1:
+            GGML_ASSERT(offset == 0);
+            GGML_ASSERT(offset + size <= ggml_nbytes(tensor));
+            repack_q4x4x2_q4_1(data, tensor, size);
+            break;
+
        case GGML_TYPE_Q8_0:
            GGML_ASSERT(offset == 0);
            GGML_ASSERT(offset + size <= ggml_nbytes(tensor));
@@ -1536,12 +1783,10 @@ static ggml_backend_buffer_type_i ggml_backend_hexagon_repack_buffer_type_interf
    /* .is_host          = */ ggml_backend_hexagon_repack_buffer_type_is_host,
 };

-// Backend session implementation
-
 struct ggml_hexagon_opbatch {
    ggml_hexagon_session*            sess;

-    std::vector<const ggml_tensor*>  ops;       // pointers to original ops
+    std::vector<htp_opnode>          ops;       // htp_opnode of ops

    std::vector<htp_buf_desc>        h_bufs;    // htp buffer descriptors
    std::vector<htp_tensor>          h_tens;    // htp tensor descriptors
@@ -1673,7 +1918,7 @@ struct ggml_hexagon_opbatch {
        return ti;
    }

-    bool fit_op(const struct ggml_tensor *t) const {
+    bool fit_op(const htp_opnode & node) const {
        if (n_ops >= n_ops_max ) return false;

        // check how much extras we will need
@@ -1682,6 +1927,7 @@ struct ggml_hexagon_opbatch {
        size_t extra_tens = 0;

        auto fit_tensor = [&](const ggml_tensor *t) {
+            if (!t) return;
            if (!t_map.count(t)) {
                extra_tens++;

@@ -1693,10 +1939,10 @@ struct ggml_hexagon_opbatch {
            }
        };

-        for (unsigned int i=0; i < HTP_OP_MAX_INPUTS && t->src[i]; i++) {
-            fit_tensor(t->src[i]);
+        for (const auto * src : node.get_inputs()) {
+            fit_tensor(src);
        }
-        fit_tensor(t);
+        fit_tensor(node.dst());

        if ((extra_bufs + n_bufs) > n_bufs_max) return false;
        if ((extra_tens + n_tens) > n_tens_max) return false;
@@ -1706,29 +1952,30 @@ struct ggml_hexagon_opbatch {
    }

    // assumes that fit_op() was called first and returned true
-    void add_op(htp_op_code opcode, const struct ggml_tensor * t) {
+    void add_op(const htp_opnode & node) {
        // Add new op

        unsigned int n = n_ops++;
        GGML_ASSERT(n_ops <= n_ops_max);

-        ops[n] = t;
+        ops[n] = node;

        htp_op_desc &o = h_ops[n];
-        memcpy(&o.params, &t->op_params, sizeof(t->op_params));
-        o.opcode = opcode;
+        memcpy(&o.params, &node.node->op_params, sizeof(node.node->op_params));
+        o.opcode = node.opcode;
        o.flags  = 0;

        if (!(opt_opstage & HTP_OPSTAGE_COMPUTE)) {
            o.flags |= HTP_OPFLAGS_SKIP_COMPUTE;
        }

-        ggml_hexagon_dump_op_exec(sess->c_name(), t, o.flags);
+        ggml_hexagon_dump_op_exec(sess->c_name(), node, o.flags);

+        auto inputs = node.get_inputs();
        for (unsigned int i=0; i < HTP_OP_MAX_INPUTS; i++) {
-            o.src[i] = t->src[i] ? add_tensor(t->src[i]) : 0xffff;
+            o.src[i] = (i < inputs.size() && inputs[i]) ? add_tensor(inputs[i]) : 0xffff;
        }
-        o.dst = add_tensor(t);
+        o.dst = add_tensor(node.dst());
    }
 };

@@ -1737,7 +1984,7 @@ struct ggml_hexagon_opqueue {
    ggml_hexagon_shared_buffer *shm_buf;
    size_t                      shm_blk_size;

-    using opvec = std::vector<const ggml_tensor*>;
+    using opvec = std::vector<htp_opnode>;

    std::queue<unsigned int>    done;       // completed batch ids
    std::vector<opvec>          op_cache;   // per batch op cache
@@ -1886,7 +2133,8 @@ void ggml_hexagon_session::flush_pending(bool all) {
        uint32_t               n_dbufs;

        // Read response packet from queue
-        int err = dspqueue_read(this->queue, &flags, 1, &n_dbufs, &dbuf, sizeof(rsp), &rsp_size, (uint8_t *) &rsp, DSPQUEUE_TIMEOUT);
+        const uint32_t timeo = opt_oppoll ? 0 : DSPQUEUE_TIMEOUT;
+        int err = dspqueue_read(this->queue, &flags, 1, &n_dbufs, &dbuf, sizeof(rsp), &rsp_size, (uint8_t *) &rsp, timeo);
        if (err == AEE_EEXPIRED) {
            continue;
        }
@@ -1935,11 +2183,11 @@ void ggml_hexagon_session::flush_batch() {
    }
 }

-void ggml_hexagon_session::enqueue_op(htp_op_code opcode, const ggml_tensor *op) {
-    if (!op_batch->fit_op(op)) {
+void ggml_hexagon_session::enqueue_op(const htp_opnode & node) {
+    if (!op_batch->fit_op(node)) {
        flush_batch();
    }
-    op_batch->add_op(opcode, op);
+    op_batch->add_op(node);
 }

 // Flush HTP response queue i.e wait for all outstanding requests to complete
@@ -2290,6 +2538,7 @@ static bool ggml_hexagon_supported_gated_delta_net(const struct ggml_hexagon_ses
    const int64_t H        = v->ne[1];
    const int64_t n_tokens = v->ne[2];
    const int64_t n_seqs   = v->ne[3];
+    const int64_t K        = state->ne[1];

    if (S_v <= 0 || S_v > 128 || H <= 0 || n_tokens <= 0 || n_seqs <= 0) {
        return false;
@@ -2302,10 +2551,10 @@ static bool ggml_hexagon_supported_gated_delta_net(const struct ggml_hexagon_ses
    if ((g->ne[0] != 1 && g->ne[0] != S_v) || beta->ne[0] != 1) {
        return false;
    }
-    if (ggml_nelements(state) != S_v * S_v * H * n_seqs) {
+    if (ggml_nelements(state) != S_v * S_v * H * n_seqs * K) {
        return false;
    }
-    if (dst->ne[0] != S_v * H || dst->ne[1] != n_tokens * n_seqs + S_v * n_seqs) {
+    if (dst->ne[0] != S_v * H || dst->ne[1] != n_tokens * n_seqs + S_v * n_seqs * K) {
        return false;
    }

@@ -2327,6 +2576,7 @@ static bool ggml_hexagon_supported_mul_mat(const struct ggml_hexagon_session * s

    switch (src0->type) {
        case GGML_TYPE_Q4_0:
+        case GGML_TYPE_Q4_1:
        case GGML_TYPE_Q8_0:
        case GGML_TYPE_IQ4_NL:
        case GGML_TYPE_MXFP4:
@@ -2353,6 +2603,27 @@ static bool ggml_hexagon_supported_mul_mat(const struct ggml_hexagon_session * s
                GGML_LOG_DEBUG("ggml_hexagon_supported_mul_mat: permuted F16 src0 not supported\n");
                return false;
            }
+            if (src1->ne[2] < src0->ne[2] || src1->ne[3] < src0->ne[3]) {
+                GGML_LOG_DEBUG("ggml_hexagon_supported_mul_mat: src1 broadcasting not supported\n");
+                return false;
+            }
+            if (ggml_nrows(src1) > 1024) {
+                return false;  // no huge batches (for now)
+            }
+            break;
+
+        case GGML_TYPE_F32:
+            if (src1->type != GGML_TYPE_F32) {
+                return false;
+            }
+            if (src0->nb[1] < src0->nb[0]) {
+                GGML_LOG_DEBUG("ggml_hexagon_supported_mul_mat: permuted F32 src0 not supported\n");
+                return false;
+            }
+            if (src1->ne[2] < src0->ne[2] || src1->ne[3] < src0->ne[3]) {
+                GGML_LOG_DEBUG("ggml_hexagon_supported_mul_mat: src1 broadcasting not supported\n");
+                return false;
+            }
            if (ggml_nrows(src1) > 1024) {
                return false;  // no huge batches (for now)
            }
@@ -2377,6 +2648,7 @@ static bool ggml_hexagon_supported_mul_mat_id(const struct ggml_hexagon_session

    switch (src0->type) {
        case GGML_TYPE_Q4_0:
+        case GGML_TYPE_Q4_1:
        case GGML_TYPE_Q8_0:
        case GGML_TYPE_IQ4_NL:
        case GGML_TYPE_MXFP4:
@@ -2892,13 +3164,14 @@ static htp_op_code op_remap_to_htp(const ggml_tensor * t) {

        case GGML_OP_UNARY:
            switch (ggml_get_unary_op(t)) {
-                case GGML_UNARY_OP_SILU:     return HTP_OP_UNARY_SILU;
-                case GGML_UNARY_OP_GELU:     return HTP_OP_UNARY_GELU;
-                case GGML_UNARY_OP_SIGMOID:  return HTP_OP_UNARY_SIGMOID;
-                case GGML_UNARY_OP_NEG:      return HTP_OP_UNARY_NEG;
-                case GGML_UNARY_OP_EXP:      return HTP_OP_UNARY_EXP;
-                case GGML_UNARY_OP_SOFTPLUS: return HTP_OP_UNARY_SOFTPLUS;
-                case GGML_UNARY_OP_TANH:     return HTP_OP_UNARY_TANH;
+                case GGML_UNARY_OP_SILU:       return HTP_OP_UNARY_SILU;
+                case GGML_UNARY_OP_GELU:       return HTP_OP_UNARY_GELU;
+                case GGML_UNARY_OP_GELU_QUICK: return HTP_OP_UNARY_GELU;
+                case GGML_UNARY_OP_SIGMOID:    return HTP_OP_UNARY_SIGMOID;
+                case GGML_UNARY_OP_NEG:        return HTP_OP_UNARY_NEG;
+                case GGML_UNARY_OP_EXP:        return HTP_OP_UNARY_EXP;
+                case GGML_UNARY_OP_SOFTPLUS:   return HTP_OP_UNARY_SOFTPLUS;
+                case GGML_UNARY_OP_TANH:       return HTP_OP_UNARY_TANH;
            default:
                break;
            }
@@ -2929,10 +3202,43 @@ static ggml_status ggml_backend_hexagon_graph_compute(ggml_backend_t backend, gg

    HEX_VERBOSE("ggml-hex: %s graph-compute n_nodes %d\n", sess->c_name(), graph->n_nodes);

+    std::vector<htp_opnode> nodes;
+    nodes.reserve(graph->n_nodes);
+
+    // Fusion
    for (int i = 0; i < graph->n_nodes; ++i) {
        ggml_tensor * n = graph->nodes[i];
-        if (op_is_compute(n) && (opt_opstage & HTP_OPSTAGE_QUEUE)) {
-            sess->enqueue_op(op_remap_to_htp(n), n);
+        if (!op_is_compute(n)) {
+            continue;
+        }
+
+        ggml_tensor * next_node = (i + 1 < graph->n_nodes) ? graph->nodes[i + 1] : nullptr;
+
+        htp_opnode node = {
+            /*.node =*/ n,
+            /*.fused =*/ {},
+            /*.opcode =*/ HTP_OP_INVALID
+        };
+
+        if (n->op == GGML_OP_RMS_NORM && next_node) {
+            if (next_node->op == GGML_OP_MUL && op_is_compute(next_node) && ggml_can_fuse(graph, i, { GGML_OP_RMS_NORM, GGML_OP_MUL })) {
+                node.add_fused(next_node);
+                node.opcode = HTP_OP_RMS_NORM_MUL;
+                i++; // skip the fused MUL node
+            }
+        }
+
+        if (node.opcode == HTP_OP_INVALID) {
+            node.opcode = op_remap_to_htp(n);
+        }
+
+        nodes.push_back(std::move(node));
+    }
+
+    // Queue and execute
+    if (opt_opstage & HTP_OPSTAGE_QUEUE) {
+        for (const auto & node : nodes) {
+            sess->enqueue_op(node);
        }
    }

@@ -2951,51 +3257,7 @@ static void ggml_backend_hexagon_synchronize(ggml_backend_t backend) {
    sess->flush();
 }

-struct node_info {
-    ggml_tensor * node;
-
-    std::vector<ggml_tensor *> fused;
-
-    ggml_op op() const {
-        return node->op;
-    }
-
-    const ggml_tensor * dst() const {
-        return fused.empty() ? node : fused.back();
-    }
-
-    const ggml_tensor * src0() const {
-        return node->src[0];
-    }
-
-    const ggml_tensor * src1() const {
-        return node->src[1];
-    }
-
-    bool is_empty() const {
-        return ggml_op_is_empty(node->op);
-    }
-
-    void add_fused(ggml_tensor * t) {
-        fused.push_back(t);
-    }
-
-    bool stackable() const {
-        switch (this->op()) {
-            case GGML_OP_MUL_MAT:
-            case GGML_OP_MUL_MAT_ID:
-                return ggml_is_quantized(this->src0()->type);
-            default:
-                return false;
-        }
-    }
-
-    bool same_input(const node_info& n) const {
-        return n.src1() == this->src1();
-    }
-};
-
-static std::vector<int> ggml_hexagon_graph_optimize_reorder(const std::vector<node_info> & nodes) {
+static std::vector<int> ggml_hexagon_graph_optimize_reorder(const std::vector<htp_opnode> & nodes) {
    const int n = nodes.size();

    std::vector<int> res;
@@ -3049,14 +3311,14 @@ static void ggml_backend_hexagon_graph_optimize(ggml_backend_t backend, ggml_cgr

    enum ggml_op ops[MAX_FUSE];

-    std::vector<node_info> nodes;
+    std::vector<htp_opnode> nodes;
    nodes.reserve(gf->n_nodes);

    // fuse nodes:
    // we don't want to make reorders that break fusing, so we first pack all fusable tensors
    //   and perform the reorder over the fused nodes. after the reorder is done, we unfuse
    for (int i = 0; i < n; i++) {
-        node_info node = {
+        htp_opnode node = {
            /*.node =*/gf->nodes[i],
            /*.fused =*/{},
        };
@@ -3391,6 +3653,7 @@ static bool ggml_backend_hexagon_device_supports_op(ggml_backend_dev_t dev, cons
                    break;
                case GGML_UNARY_OP_SILU:
                case GGML_UNARY_OP_GELU:
+                case GGML_UNARY_OP_GELU_QUICK:
                    supp = ggml_hexagon_supported_activations(sess, op);
                    break;
                default:
@@ -3622,6 +3885,8 @@ static void ggml_hexagon_init(ggml_backend_reg * reg) {
    // Basic sanity checks to make sure definitions match
    static_assert((unsigned int) HTP_TYPE_Q4_0 == (unsigned int) GGML_TYPE_Q4_0,
                  "please update hexagon_type to match ggml_type");
+    static_assert((unsigned int) HTP_TYPE_Q4_1 == (unsigned int) GGML_TYPE_Q4_1,
+                  "please update hexagon_type to match ggml_type");
    static_assert((unsigned int) HTP_TYPE_Q8_0 == (unsigned int) GGML_TYPE_Q8_0,
                  "please update hexagon_type to match ggml_type");
    static_assert((unsigned int) HTP_TYPE_MXFP4 == (unsigned int) GGML_TYPE_MXFP4,
@@ -3634,6 +3899,7 @@ static void ggml_hexagon_init(ggml_backend_reg * reg) {
    const char * str_opstage  = getenv("GGML_HEXAGON_OPSTAGE");
    const char * str_opbatch  = getenv("GGML_HEXAGON_OPBATCH");
    const char * str_opqueue  = getenv("GGML_HEXAGON_OPQUEUE");
+    const char * str_oppoll   = getenv("GGML_HEXAGON_OPPOLL");
    const char * str_opfilter = getenv("GGML_HEXAGON_OPFILTER");
    const char * str_profile  = getenv("GGML_HEXAGON_PROFILE");
    const char * str_etm      = getenv("GGML_HEXAGON_ETM");
@@ -3671,6 +3937,7 @@ static void ggml_hexagon_init(ggml_backend_reg * reg) {
    opt_opstage   = str_opstage  ? strtoul(str_opstage, NULL, 0)          : opt_opstage;
    opt_opbatch   = str_opbatch  ? strtoul(str_opbatch, NULL, 0)          : opt_opbatch;
    opt_opqueue   = str_opqueue  ? strtoul(str_opqueue, NULL, 0)          : opt_opqueue;
+    opt_oppoll    = str_oppoll   ? strtoul(str_oppoll,  NULL, 0)          : opt_oppoll;
    opt_profile   = str_profile  ? atoi(str_profile)                      : 0;
    opt_etm       = str_etm      ? atoi(str_etm)                          : 0;
    opt_nhvx      = str_nhvx     ? strtoul(str_nhvx, NULL, 0)             : opt_nhvx;
@@ -0,0 +1,265 @@
+#ifndef HTP_OPNODE_H
+#define HTP_OPNODE_H
+
+#define GGML_COMMON_IMPL_CPP
+#include "ggml-backend-impl.h"
+#include "ggml-common.h"
+
+#include <string>
+#include <vector>
+#include <stdio.h>
+#include "htp-ops.h"
+
+struct htp_opnode {
+    ggml_tensor * node = nullptr;
+
+    std::vector<ggml_tensor *> fused;
+
+    htp_op_code opcode = HTP_OP_INVALID;
+
+    ggml_op op() const {
+        return node->op;
+    }
+
+    const ggml_tensor * dst() const {
+        return fused.empty() ? node : fused.back();
+    }
+
+    const ggml_tensor * src0() const {
+        return node->src[0];
+    }
+
+    const ggml_tensor * src1() const {
+        return node->src[1];
+    }
+
+    bool is_empty() const {
+        return ggml_op_is_empty(node->op);
+    }
+
+    void add_fused(ggml_tensor * t) {
+        fused.push_back(t);
+    }
+
+    bool stackable() const {
+        switch (this->op()) {
+            case GGML_OP_MUL_MAT:
+            case GGML_OP_MUL_MAT_ID:
+                return ggml_is_quantized(this->src0()->type);
+            default:
+                return false;
+        }
+    }
+
+    bool same_input(const htp_opnode& n) const {
+        return n.src1() == this->src1();
+    }
+
+    std::vector<const ggml_tensor *> get_inputs() const {
+        std::vector<const ggml_tensor *> inputs(GGML_MAX_SRC, nullptr);
+        std::vector<const ggml_tensor *> outputs;
+        outputs.push_back(node);
+        for (const auto * f : fused) {
+            outputs.push_back(f);
+        }
+
+        auto contains = [&](const std::vector<const ggml_tensor *> & vec, const ggml_tensor * t) {
+            for (const auto * x : vec) {
+                if (x == t) return true;
+            }
+            return false;
+        };
+
+        int count = 0;
+        auto add_input = [&](const ggml_tensor * t) {
+            if (t && !contains(outputs, t) && !contains(inputs, t)) {
+                if (count < (int)inputs.size()) {
+                    inputs[count++] = t;
+                } else {
+                    inputs.push_back(t);
+                }
+            }
+        };
+
+        for (int i = 0; i < GGML_MAX_SRC; i++) {
+            if (fused.empty()) {
+                inputs[i] = node->src[i];
+            } else {
+                if (node->src[i]) {
+                    add_input(node->src[i]);
+                }
+            }
+        }
+        for (const auto * f : fused) {
+            for (int i = 0; i < GGML_MAX_SRC; i++) {
+                if (f->src[i]) {
+                    add_input(f->src[i]);
+                }
+            }
+        }
+
+        if (!fused.empty()) {
+            inputs.resize(count);
+        }
+
+        return inputs;
+    }
+
+    std::string op_name() const {
+        if (fused.empty()) {
+            return ggml_op_desc(node);
+        }
+        std::string name = ggml_op_desc(node);
+        for (const auto * f : fused) {
+            name += "+";
+            name += ggml_op_desc(f);
+        }
+        return name;
+    }
+};
+
+struct htp_opformat {
+    char strides[64 * GGML_MAX_SRC];
+    char dims[64 * GGML_MAX_SRC];
+    char types[16 * GGML_MAX_SRC];
+    char buffs[64 * GGML_MAX_SRC];
+    char names[64 * GGML_MAX_SRC];
+
+    int format_tensor_dims(char * str, const struct ggml_tensor * t) {
+        if (!t) {
+            return sprintf(str, "NONE");
+        }
+        if (t->ne[2] == 1 && t->ne[3] == 1) {
+            return sprintf(str, "%d:%d", (int) t->ne[0], (int) t->ne[1]);
+        } else {
+            return sprintf(str, "%d:%d:%d:%d", (int) t->ne[0], (int) t->ne[1], (int) t->ne[2], (int) t->ne[3]);
+        }
+    }
+
+    void format_op_dims(char * str, const htp_opnode & node) {
+        char * p = str;
+        auto inputs = node.get_inputs();
+
+        if (!inputs.empty()) {
+            p += format_tensor_dims(p, inputs[0]);
+
+            for (size_t i = 1; i < inputs.size(); i++) {
+                p += sprintf(p, " x ");
+                p += format_tensor_dims(p, inputs[i]);
+            }
+
+            p += sprintf(p, " -> ");
+        }
+
+        char self[64];
+        format_tensor_dims(self, node.dst());
+        p += sprintf(p, "%s", self);
+    }
+
+    int format_tensor_strides(char * str, const struct ggml_tensor * t) {
+        if (!t) {
+            return sprintf(str, "NONE");
+        }
+        const char * c = ggml_is_contiguous(t) ? "" : "!";
+
+        if (t->ne[2] == 1 && t->ne[3] == 1) {
+            return sprintf(str, "%zu:%zu%s", (size_t) t->nb[0], (size_t) t->nb[1], c);
+        } else {
+            return sprintf(str, "%zu:%zu:%zu:%zu%s", (size_t) t->nb[0], (size_t) t->nb[1], (size_t) t->nb[2], (size_t) t->nb[3], c);
+        }
+    }
+
+    void format_op_strides(char * str, const htp_opnode & node) {
+        char * p = str;
+        auto inputs = node.get_inputs();
+
+        if (!inputs.empty()) {
+            p += format_tensor_strides(p, inputs[0]);
+
+            for (size_t i = 1; i < inputs.size(); i++) {
+                p += sprintf(p, " x ");
+                p += format_tensor_strides(p, inputs[i]);
+            }
+
+            p += sprintf(p, " -> ");
+        }
+
+        char self[64];
+        format_tensor_strides(self, node.dst());
+        p += sprintf(p, "%s", self);
+    }
+
+    void format_op_types(char * str, const htp_opnode & node) {
+        char * p = str;
+        auto inputs = node.get_inputs();
+
+        if (!inputs.empty()) {
+            p += sprintf(p, "%s", inputs[0] ? ggml_type_name(inputs[0]->type) : "NONE");
+
+            for (size_t i = 1; i < inputs.size(); i++) {
+                p += sprintf(p, " x ");
+                p += sprintf(p, "%s", inputs[i] ? ggml_type_name(inputs[i]->type) : "NONE");
+            }
+
+            p += sprintf(p, " -> ");
+        }
+
+        p += sprintf(p, "%s", ggml_type_name(node.dst()->type));
+    }
+
+    const char * tensor_buff_name(const struct ggml_tensor * t) {
+        if (t && t->buffer) {
+            return ggml_backend_buffer_name(t->buffer);
+        }
+        return "NONE";
+    }
+
+    void format_op_buffs(char * str, const htp_opnode & node) {
+        char * p = str;
+        auto inputs = node.get_inputs();
+
+        if (!inputs.empty()) {
+            p += sprintf(p, "%s", tensor_buff_name(inputs[0]));
+
+            for (size_t i = 1; i < inputs.size(); i++) {
+                p += sprintf(p, " x ");
+                p += sprintf(p, "%s", tensor_buff_name(inputs[i]));
+            }
+
+            p += sprintf(p, " -> ");
+        }
+
+        p += sprintf(p, "%s", tensor_buff_name(node.dst()));
+    }
+
+    void format_op_names(char * str, const htp_opnode & node) {
+        char * p = str;
+        auto inputs = node.get_inputs();
+
+        if (!inputs.empty()) {
+            p += sprintf(p, "%s", inputs[0] ? inputs[0]->name : "NONE");
+
+            for (size_t i = 1; i < inputs.size(); i++) {
+                p += sprintf(p, " x ");
+                p += sprintf(p, "%s", inputs[i] ? inputs[i]->name : "NONE");
+            }
+
+            p += sprintf(p, " -> ");
+        }
+
+        p += sprintf(p, "%s", node.dst()->name);
+    }
+
+    void format(const htp_opnode & node) {
+        format_op_dims(dims, node);
+        format_op_strides(strides, node);
+        format_op_types(types, node);
+        format_op_buffs(buffs, node);
+        format_op_names(names, node);
+    }
+
+    htp_opformat() {}
+    htp_opformat(const htp_opnode & node) { format(node); }
+};
+
+#endif // HTP_OPNODE_H
@@ -19,6 +19,43 @@ add_library(${HTP_LIB} SHARED
    htp_iface_skel.c
    worker-pool.c
    hex-dma.c
+)
+
+target_compile_definitions(${HTP_LIB} PRIVATE
+    $<IF:$<BOOL:${HEXAGON_HTP_DEBUG}>,HTP_DEBUG=1,NDEBUG=1>
+    $<IF:$<BOOL:${HEXAGON_HTP_DEBUG}>,FARF_HIGH=1,>
+    FP32_QUANTIZE_GROUP_SIZE=${GGML_HEXAGON_FP32_QUANTIZE_GROUP_SIZE})
+
+if (GGML_HEXAGON_FA_EXP2_HF)
+    message(STATUS "ggml-htp: HMX_FA_USE_EXP2_HF=1 (use FP16 exp2 polynomial in FA softmax)")
+    target_compile_definitions(${HTP_LIB} PRIVATE HMX_FA_USE_EXP2_HF=1)
+endif()
+
+# HMX acceleration: available on v73+ architectures
+set(HTP_HMX_VERSIONS v73 v75 v79 v81)
+list(FIND HTP_HMX_VERSIONS ${DSP_VERSION} _hmx_idx)
+
+if (_hmx_idx GREATER_EQUAL 0)
+    target_sources(${HTP_LIB} PRIVATE
+        hmx-matmul-ops.c
+        hmx-flash-attn-ops.c
+        hmx-queue.c
+    )
+
+    # -mhmx enables HMX instruction set (needed by files that include hmx-utils.h)
+    set_source_files_properties(
+        hmx-flash-attn-ops.c
+        hmx-matmul-ops.c
+        hmx-queue.c
+        PROPERTIES COMPILE_OPTIONS "-mhmx"
+    )
+
+    target_compile_definitions(${HTP_LIB} PRIVATE HTP_HAS_HMX=1)
+endif()
+
+build_idl(htp_iface.idl ${HTP_LIB})
+
+target_sources(${HTP_LIB} PRIVATE
    matmul-ops.c
    binary-ops.c
    unary-ops.c
@@ -42,39 +79,6 @@ add_library(${HTP_LIB} SHARED
    pad-ops.c
 )

-target_compile_definitions(${HTP_LIB} PRIVATE
-    $<IF:$<BOOL:${HEXAGON_HTP_DEBUG}>,HTP_DEBUG=1,NDEBUG=1>
-    $<IF:$<BOOL:${HEXAGON_HTP_DEBUG}>,FARF_HIGH=1,>
-    FP32_QUANTIZE_GROUP_SIZE=${GGML_HEXAGON_FP32_QUANTIZE_GROUP_SIZE})
-
-if (GGML_HEXAGON_FA_EXP2_HF)
-    message(STATUS "ggml-htp: HMX_FA_USE_EXP2_HF=1 (use FP16 exp2 polynomial in FA softmax)")
-    target_compile_definitions(${HTP_LIB} PRIVATE HMX_FA_USE_EXP2_HF=1)
-endif()
-
-# HMX acceleration: available on v73+ architectures
-set(HTP_HMX_VERSIONS v73 v75 v79 v81)
-list(FIND HTP_HMX_VERSIONS ${DSP_VERSION} _hmx_idx)
-
-if (_hmx_idx GREATER_EQUAL 0)
-    target_sources(${HTP_LIB} PRIVATE
-        hmx-queue.c
-        hmx-matmul-ops.c
-        hmx-flash-attn-ops.c
-    )
-
-    # -mhmx enables HMX instruction set (needed by files that include hmx-utils.h)
-    set_source_files_properties(
-        hmx-matmul-ops.c
-        hmx-flash-attn-ops.c
-        PROPERTIES COMPILE_OPTIONS "-mhmx"
-    )
-
-    target_compile_definitions(${HTP_LIB} PRIVATE HTP_HAS_HMX=1)
-endif()
-
-build_idl(htp_iface.idl ${HTP_LIB})
-
 set_target_properties(${HTP_LIB} PROPERTIES EXPORT_COMPILE_COMMANDS ON)

 install(TARGETS ${HTP_LIB})
@@ -276,6 +276,7 @@ int op_argsort(struct htp_ops_context * octx) {
    octx->src0_spad.data = octx->ctx->vtcm_base;
    octx->src0_spad.size = total_spad_size;
    octx->src0_spad.size_per_thread = spad_per_thread;
+    octx->src0_spad.src  = NULL;

    FARF(HIGH, "argsort: %ux%ux%ux%u -> %ux%ux%ux%u (0x%x, 0x%x)",
         octx->src[0]->ne[0], octx->src[0]->ne[1], octx->src[0]->ne[2], octx->src[0]->ne[3],
@@ -262,6 +262,8 @@ int op_concat(struct htp_ops_context * octx) {

        octx->src0_spad.data = octx->ctx->vtcm_base;
        octx->src1_spad.data = octx->src0_spad.data + octx->src0_spad.size;
+        octx->src0_spad.src  = NULL;
+        octx->src1_spad.src  = NULL;

        if (type_size == 4) {
            worker_func = concat_2d_f32_transposed;
@@ -11,6 +11,7 @@
 #include "hex-dma.h"
 #include "hvx-utils.h"
 #include "hvx-dump.h"
+#include "hvx-flash-attn.h"

 #define GGML_COMMON_DECL_C
 #include "ggml-common.h"
@@ -22,6 +23,16 @@
 // Must be multiple of 32
 #define FLASH_ATTN_BLOCK_SIZE (32 * 2)

+#if __HVX_ARCH__ < 79
+#define HVX_OP_ADD_F32(a, b) Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_VsfVsf(a, b))
+#define HVX_OP_SUB_F32(a, b) Q6_Vsf_equals_Vqf32(Q6_Vqf32_vsub_VsfVsf(a, b))
+#define HVX_OP_MUL_F32(a, b) Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(a, b))
+#else
+#define HVX_OP_ADD_F32(a, b) Q6_Vsf_vadd_VsfVsf(a, b)
+#define HVX_OP_SUB_F32(a, b) Q6_Vsf_vsub_VsfVsf(a, b)
+#define HVX_OP_MUL_F32(a, b) Q6_Vsf_vmpy_VsfVsf(a, b)
+#endif
+
 // This is a bit of a hack because the compiler is strugling to properly inline
 // the default hvx_vec_f32_to_f16 with output into the local array.
 static __attribute__((noinline)) void hvx_vec_f32_to_f16_a(void *ptr, HVX_Vector v0, HVX_Vector v1)
@@ -54,8 +65,8 @@ static inline void hvx_dot_f16_f16_aa(float * restrict r, const void * restrict
        rsum_p = hvx_vec_mpyacc_f32_f16(rsum_p, x_hf, y_hf);
    }

-    HVX_Vector rsum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_VsfVsf(Q6_V_lo_W(rsum_p), Q6_V_hi_W(rsum_p)));
-    rsum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(hvx_vec_splat_f32(s), hvx_vec_reduce_sum_f32(rsum)));
+    HVX_Vector rsum = HVX_OP_ADD_F32(Q6_V_lo_W(rsum_p), Q6_V_hi_W(rsum_p));
+    rsum = HVX_OP_MUL_F32(hvx_vec_splat_f32(s), hvx_vec_reduce_sum_f32(rsum));
    hvx_vec_store_u(r, 4, rsum);
 }

@@ -105,10 +116,10 @@ static inline HVX_Vector hvx_dot_f16_f16_aa_rx4(const void * restrict y,
        rsum3_p = hvx_vec_mpyacc_f32_f16(rsum3_p, x3_hf, y_hf);
    }

-    HVX_Vector rsum0 = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_VsfVsf(Q6_V_lo_W(rsum0_p), Q6_V_hi_W(rsum0_p)));
-    HVX_Vector rsum1 = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_VsfVsf(Q6_V_lo_W(rsum1_p), Q6_V_hi_W(rsum1_p)));
-    HVX_Vector rsum2 = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_VsfVsf(Q6_V_lo_W(rsum2_p), Q6_V_hi_W(rsum2_p)));
-    HVX_Vector rsum3 = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_VsfVsf(Q6_V_lo_W(rsum3_p), Q6_V_hi_W(rsum3_p)));
+    HVX_Vector rsum0 = HVX_OP_ADD_F32(Q6_V_lo_W(rsum0_p), Q6_V_hi_W(rsum0_p));
+    HVX_Vector rsum1 = HVX_OP_ADD_F32(Q6_V_lo_W(rsum1_p), Q6_V_hi_W(rsum1_p));
+    HVX_Vector rsum2 = HVX_OP_ADD_F32(Q6_V_lo_W(rsum2_p), Q6_V_hi_W(rsum2_p));
+    HVX_Vector rsum3 = HVX_OP_ADD_F32(Q6_V_lo_W(rsum3_p), Q6_V_hi_W(rsum3_p));

    HVX_Vector_x4 rsum0123 = { .v = { rsum0, rsum1, rsum2, rsum3 } };
    return hvx_vec_reduce_sum_f32x4(rsum0123);
@@ -123,7 +134,7 @@ static inline HVX_Vector hvx_dot_f16_f16_aa_rx32(const void * restrict y,
    const size_t nvec = n / VLEN_FP16; // num full fp16 hvx vectors
    const size_t nloe = n % VLEN_FP16; // leftover elements

-    HVX_Vector   sums;  // initialize at j = 0
+    HVX_Vector   sums = Q6_V_vzero();
    const size_t stride_x_4 = stride_x * 4;
    for (uint32_t j = 0; j < VLEN_FP32; j += 4) {
        HVX_Vector     sums_x4 = hvx_dot_f16_f16_aa_rx4(y, x, stride_x, nvec, nloe);
@@ -132,8 +143,7 @@ static inline HVX_Vector hvx_dot_f16_f16_aa_rx32(const void * restrict y,
        x += stride_x_4;
    }

-    sums = Q6_Vqf32_vmpy_VsfVsf(hvx_vec_splat_f32(s), sums);
-    return Q6_Vsf_equals_Vqf32(sums);
+    return HVX_OP_MUL_F32(hvx_vec_splat_f32(s), sums);
 }

 // MAD: y (F32) += x (F16) * s (F16)
@@ -236,6 +246,7 @@ struct htp_fa_context {
    uint32_t n_head_log2;
    float m0;
    float m1;
+    float slopes[512];

    uint32_t n_blocks;

@@ -268,11 +279,10 @@ static inline void hvx_scale_vec_f32_aa(uint8_t * restrict dst, const uint8_t *
    uint32_t i = 0;
    #pragma unroll(4)
    for (; i < nvec; ++i) {
-        vdst[i] = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(vsrc[i], vs));
+        vdst[i] = HVX_OP_MUL_F32(vsrc[i], vs);
    }
    if (nloe) {
-        HVX_Vector v = Q6_Vqf32_vmpy_VsfVsf(vsrc[i], vs);
-        hvx_vec_store_a(&vdst[i], nloe * sizeof(float), Q6_Vsf_equals_Vqf32(v));
+        hvx_vec_store_a(&vdst[i], nloe * sizeof(float), HVX_OP_MUL_F32(vsrc[i], vs));
    }
 }

@@ -404,7 +414,7 @@ static void flash_attn_ext_f16_thread(unsigned int nth, unsigned int ith, void *
        }

        const uint32_t h = iq2; // head index
-        const float slope = (factx->max_bias > 0.0f) ? (h < factx->n_head_log2 ? powf(factx->m0, h + 1) : powf(factx->m1, 2*(h - factx->n_head_log2) + 1)) : 1.0f;
+        const float slope = factx->slopes[h];

        HVX_Vector S_vec = hvx_vec_splat_f32(0.0f);
        HVX_Vector M_vec = hvx_vec_splat_f32(-INFINITY);
@@ -438,25 +448,44 @@ static void flash_attn_ext_f16_thread(unsigned int nth, unsigned int ith, void *
            // Process in sub-blocks of 32 (VLEN_FP32)
            HVX_Vector sb_scores[FLASH_ATTN_BLOCK_SIZE / VLEN_FP32];
            HVX_Vector v_max = hvx_vec_splat_f32(-INFINITY);
-            for (uint32_t iv = 0; ic + VLEN_FP32 <= current_block_size; ic += VLEN_FP32, ++iv) {
+            for (uint32_t iv = 0; ic < current_block_size; ic += VLEN_FP32, ++iv) {
                // 1. Compute scores
                HVX_Vector scores = hvx_dot_f16_f16_aa_rx32(q_ptr_vtcm, k_base + ic * factx->size_k_row_padded, factx->size_k_row_padded, DK, factx->scale);

                // 2. Softcap
                if (factx->logit_softcap != 0.0f) {
                    scores = hvx_vec_tanh_f32(scores);
-                    scores = Q6_Vqf32_vmpy_VsfVsf(scores, logit_cap);
-                    scores = Q6_Vsf_equals_Vqf32(scores);
+                    scores = HVX_OP_MUL_F32(scores, logit_cap);
                }

                // 3. Mask
                if (mask) {
                    const __fp16 * mp = m_base + ic;
                    HVX_Vector m_vals_f16 = *(const HVX_UVector *) mp;
-                    HVX_VectorPair m_vals_f32_pair = Q6_Wqf32_vmpy_VhfVhf(Q6_Vh_vshuff_Vh(m_vals_f16), slope_vec);
-                    HVX_Vector add_val = Q6_V_lo_W(m_vals_f32_pair);
-                    scores = Q6_Vqf32_vadd_Vqf32Vsf(add_val, scores);
-                    scores = Q6_Vsf_equals_Vqf32(scores);
+
+                    // Multiplying -INFINITY (0xFC00) by a slope in VhfVhf instructions can incorrectly produce NaN on v79.
+                    // Clamp -INFINITY to the max negative fp16 finite value (-65504.0f).
+                    HVX_Vector vinf = Q6_Vh_vsplat_R(0xFC00);
+                    HVX_Vector vmin = Q6_Vh_vsplat_R(0xFBFF);
+                    HVX_VectorPred is_inf = Q6_Q_vcmp_eq_VhVh(m_vals_f16, vinf);
+                    m_vals_f16 = Q6_V_vmux_QVV(is_inf, vmin, m_vals_f16);
+
+                    #if __HVX_ARCH__ >= 79
+                        HVX_VectorPair m_vals_f32_pair = Q6_Wsf_vmpy_VhfVhf(Q6_Vh_vshuff_Vh(m_vals_f16), slope_vec);
+                        HVX_Vector add_val = Q6_V_lo_W(m_vals_f32_pair);
+                        scores = Q6_Vsf_vadd_VsfVsf(add_val, scores);
+                    #else
+                        HVX_VectorPair m_vals_f32_pair = Q6_Wqf32_vmpy_VhfVhf(Q6_Vh_vshuff_Vh(m_vals_f16), slope_vec);
+                        HVX_Vector add_val = Q6_V_lo_W(m_vals_f32_pair);
+                        scores = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(add_val, scores));
+                    #endif
+                }
+
+                // Mask out invalid lanes for leftover handling
+                uint32_t valid_lanes = current_block_size - ic;
+                if (valid_lanes < VLEN_FP32) {
+                    HVX_VectorPred valid_pred = Q6_Q_vsetq_R(valid_lanes * 4); // 4 bytes per fp32 lane
+                    scores = Q6_V_vmux_QVV(valid_pred, scores, hvx_vec_splat_f32(-INFINITY));
                }

                sb_scores[iv] = scores;
@@ -466,78 +495,55 @@ static void flash_attn_ext_f16_thread(unsigned int nth, unsigned int ith, void *
            {
                // 4. Online Softmax Update
                HVX_Vector M_new_vec = Q6_Vsf_vmax_VsfVsf(v_max, M_vec);
-                HVX_Vector diff_vec  = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vsub_VsfVsf(M_vec, M_new_vec));
+                HVX_Vector diff_vec  = HVX_OP_SUB_F32(M_vec, M_new_vec);
                HVX_Vector ms_vec    = hvx_vec_exp_f32(diff_vec);
                M_vec = M_new_vec;

                hvx_scale_vec_f32_aa((uint8_t *) VKQ32, (const uint8_t *) VKQ32, DV, ms_vec);

                HVX_Vector p_sum_vec = hvx_vec_splat_f32(0.0f);
-                for (uint32_t ic2 = 0, iv = 0; ic2 + VLEN_FP32 <= current_block_size; ic2 += VLEN_FP32, ++iv) {
+                for (uint32_t ic2 = 0, iv = 0; ic2 < current_block_size; ic2 += VLEN_FP32, ++iv) {
                    HVX_Vector scores = sb_scores[iv];
-                    HVX_Vector scores_shifted = Q6_Vqf32_vsub_VsfVsf(scores, M_vec);
-                    HVX_Vector P = hvx_vec_exp_f32(Q6_Vsf_equals_Vqf32(scores_shifted));
+                    HVX_Vector scores_shifted = HVX_OP_SUB_F32(scores, M_vec);
+                    HVX_Vector P = hvx_vec_exp_f32(scores_shifted);

-                    p_sum_vec = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_VsfVsf(p_sum_vec, P));
+                    p_sum_vec = HVX_OP_ADD_F32(p_sum_vec, P);

                    // 5. Accumulate V
                    __fp16 __attribute__((aligned(VLEN))) p_arr[VLEN_FP16];
                    hvx_vec_f32_to_f16_a(p_arr, P, hvx_vec_splat_f32(0));

+                    float __attribute__((aligned(128))) P_arr[VLEN_FP32];
+                    hvx_vec_store_a(P_arr, 128, P);
+
                    for (uint32_t j = 0; j < VLEN_FP32; j += 2) {
-                        const uint32_t  cur_ic = ic2 + j;
-                        const uint8_t * v_ptr  = v_base + cur_ic * factx->size_v_row_padded;
+                        const uint32_t cur_ic = ic2 + j;
+                        if (cur_ic >= current_block_size) {
+                            break;
+                        }
+
+                        if (cur_ic + 1 == current_block_size) {
+                            // Odd leftover, process single row
+                            if (P_arr[j] != 0.0f) {
+                                const uint8_t * v_ptr = v_base + cur_ic * factx->size_v_row_padded;
+                                hvx_mad_f32_f16_aa(VKQ32, v_ptr, (p_arr + j), DV);
+                            }
+                            break;
+                        }
+
+                        // Avoid NaN * 0.0 = NaN for uninitialized V cache rows.
+                        // Check the f32 values to safely avoid strict aliasing violations.
+                        if (P_arr[j] == 0.0f && P_arr[j + 1] == 0.0f) {
+                            continue;
+                        }
+
+                        const uint8_t * v_ptr = v_base + cur_ic * factx->size_v_row_padded;
                        hvx_mad_f32_f16_aa_rx2(VKQ32, v_ptr, v_ptr + factx->size_v_row_padded, (p_arr + j), (p_arr + j + 1), DV);
                    }
                }

                p_sum_vec = hvx_vec_reduce_sum_f32(p_sum_vec);
-                S_vec = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_VsfVsf(Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(S_vec, ms_vec)), p_sum_vec));
-            }
-
-            if (ic < current_block_size) {
-                // Sync scalars for leftover/next block if needed
-                float M = hvx_vec_get_f32(M_vec);
-                float S = hvx_vec_get_f32(S_vec);
-
-                // Leftover
-                for (; ic < current_block_size; ++ic) {
-                    float s_val;
-                    const uint8_t * k_ptr = k_base + ic * factx->size_k_row_padded;
-                    hvx_dot_f16_f16_aa(&s_val, q_ptr_vtcm, k_ptr, DK, factx->scale);
-                    if (factx->logit_softcap != 0.0f) {
-                        s_val = factx->logit_softcap * tanhf(s_val);
-                    }
-
-                    if (mask) {
-                        const float m_val = m_base[ic];
-                        s_val += slope * m_val;
-                    }
-
-                    const float Mold = M;
-                    __fp16 vs = 1.0f;
-
-                    if (s_val > M) {
-                        M = s_val;
-                        HVX_Vector diff_vec = hvx_vec_splat_f32(Mold - M);
-                        HVX_Vector ms_vec   = hvx_vec_exp_f32(diff_vec);
-                        hvx_scale_vec_f32_aa((uint8_t *) VKQ32, (const uint8_t *) VKQ32, DV, ms_vec);
-
-                        float ms = hvx_vec_get_f32(ms_vec);
-                        S = S * ms + vs;
-                    } else {
-                        HVX_Vector diff_vec = hvx_vec_splat_f32(s_val - M);
-                        vs = hvx_vec_get_f32(hvx_vec_exp_f32(diff_vec));
-                        S += vs;
-                    }
-
-                    const uint8_t * v_ptr = v_base + ic * factx->size_v_row_padded;
-
-                    hvx_mad_f32_f16_aa(VKQ32, v_ptr, &vs, DV);
-                }
-
-                M_vec = hvx_vec_splat_f32(M);
-                S_vec = hvx_vec_splat_f32(S);
+                S_vec = HVX_OP_ADD_F32(HVX_OP_MUL_F32(S_vec, ms_vec), p_sum_vec);
            }

            // Issue DMA for next+1 block (if exists)
@@ -599,8 +605,9 @@ static void flash_attn_ext_f16_thread(unsigned int nth, unsigned int ith, void *
        const int i2 = iq2;
        const int i3 = iq3;

-        // dst is permuted
-        uint8_t * dst_ptr = (uint8_t *) dst->data + (i3*ne2*ne1 + i2 + i1*ne1) * nb1;
+        // dst is permuted: [DV, n_heads, n_tokens, n_seq]
+        // head stride is nb[1], token stride is nb[2], batch stride is nb[3]
+        uint8_t * dst_ptr = (uint8_t *) dst->data + i2 * dst->nb[1] + i1 * dst->nb[2] + i3 * dst->nb[3];

        if (dst->type == HTP_TYPE_F32) {
            hvx_copy_f32_ua(dst_ptr, (uint8_t *) VKQ32, DV);
@@ -623,8 +630,8 @@ int op_flash_attn_ext(struct htp_ops_context * octx) {
    }

 #ifdef HTP_HAS_HMX
-    // HMX path: prefill (neq1 >= 32), head_dim multiple of 32, F16 KV
-    if (k->type == HTP_TYPE_F16 && v->type == HTP_TYPE_F16 && k->ne[0] % 32 == 0 && q->ne[1] >= 32) {
+    // HMX path: head_dim multiple of 64, F16 KV, and no sinks
+    if (k->type == HTP_TYPE_F16 && v->type == HTP_TYPE_F16 && k->ne[0] % 64 == 0 && v->ne[0] % 64 == 0 && octx->src[4] == NULL) {
        int ret = hmx_flash_attn_ext(octx);
        if (ret == HTP_STATUS_OK) {
            return ret;
@@ -684,6 +691,13 @@ int op_flash_attn_ext(struct htp_ops_context * octx) {
    factx.m0 = powf(2.0f, -(max_bias       ) / factx.n_head_log2);
    factx.m1 = powf(2.0f, -(max_bias / 2.0f) / factx.n_head_log2);

+    if (n_head > 512) {
+        return HTP_STATUS_NO_SUPPORT;
+    }
+    for (uint32_t h = 0; h < n_head; ++h) {
+        factx.slopes[h] = (max_bias > 0.0f) ? alibi_slope(h, factx.n_head_log2, factx.m0, factx.m1) : 1.0f;
+    }
+
    // total rows in q
    const uint32_t neq0 = q->ne[0];
    const uint32_t neq1 = q->ne[1];
@@ -17,14 +17,17 @@
 #define GGML_COMMON_DECL_C
 #include "ggml-common.h"
 #include "hex-dma.h"
+#include "hex-fastdiv.h"
 #include "hmx-profile.h"
 #include "hmx-queue.h"
 #include "hmx-utils.h"
 #include "htp-ctx.h"
 #include "htp-ops.h"
 #include "hvx-dump.h"
+#include "hvx-copy.h"
 #include "hvx-reduce.h"
 #include "hvx-utils.h"
+#include "hvx-flash-attn.h"
 #include "vtcm-utils.h"
 #include "worker-pool.h"

@@ -46,7 +49,7 @@
 // g_br = hex_align_up(gqa_factor * Br, 32) replaces Br for all Q/O/S/P/D dimensions.
 // Layout: Q + O_ping + O_pong + K_dma*2 + V_dma*2 + K_tile + V_tile + S + P + D + vectors + scales
 // Mask is DMA'd into a VTCM buffer (Br rows per KV block) to avoid DDR reads in softmax.
-static size_t hmx_fa_compute_vtcm_usage(size_t gqa_factor, size_t DK, size_t DV, size_t Br, size_t Bc, size_t n_threads) {
+static size_t hmx_fa_compute_vtcm_usage(size_t gqa_factor, size_t DK, size_t DV, size_t Br, size_t Bc, size_t n_threads, bool use_pipeline) {
    const size_t g_br         = hex_align_up(gqa_factor * Br, HMX_FP16_TILE_N_ROWS);
    const size_t q_tile_size  = hex_align_up(g_br * DK * sizeof(__fp16), 4096);    // Q:  [g_br, DK]
    const size_t o_tile_size  = hex_align_up(g_br * DV * sizeof(__fp16), 4096);    // O:  [g_br, DV] x2 ping-pong
@@ -67,7 +70,7 @@ static size_t hmx_fa_compute_vtcm_usage(size_t gqa_factor, size_t DK, size_t DV,
           + k_dma_size  * 2               // K DMA x2
           + v_dma_size  * 2               // V DMA x2
           + k_tile_size * 1               // K tiles
-           + v_tile_size * 1               // V tiles
+           + v_tile_size * (use_pipeline ? 2 : 1) // V tiles (double-buffered if pipelining)
           + s_tile_size * 2               // S + P
           + d_tile_size * 1               // D (diagonal matrix)
           + col_vec_size * 4              // m_vec, l_vec, s_rowmax, p_rowsum
@@ -144,12 +147,13 @@ static int hmx_fa_find_chunk_size(size_t * Br_out,
    // See .cursor/todos/hmx-flash-attn-bc-search-space.md for the perf trade-off.
    const size_t bc_unit = HMX_FP16_TILE_N_COLS * 2;  // 64
    const size_t fp16    = sizeof(__fp16);
+    const bool   can_pipeline = (kv_len >= FA_MIN_KV_BLOCKS * bc_unit && n_threads >= 2);

    // Approximate per-unit VTCM costs (without per-buffer alignment padding).
    const size_t per_gbr  = (DK + 2 * DV) * fp16 + 4 * fp16;  // Q + O×2 + 4 col vectors
    const size_t per_gbr2 = fp16;                             // D diagonal matrix
    const size_t per_bc =
-        3 * (DK + DV) * fp16 + 2 * n_threads * fp16;          // K_dma×2 + V_dma×2 + K_tile + V_tile + row bufs
+        3 * DK * fp16 + (can_pipeline ? 4 : 3) * DV * fp16 + 2 * n_threads * fp16;          // K/V DMA x2 + tiles + row bufs
    const size_t per_gbr_bc = 2 * fp16;                       // S + P

    const size_t overhead = 256 * 2 + 13 * 4096;
@@ -164,7 +168,6 @@ static int hmx_fa_find_chunk_size(size_t * Br_out,

    // Pipeline constraint: cap Bc so n_kv_blocks >= FA_MIN_KV_BLOCKS.
    // Only relax when kv_len is too short to form enough blocks.
-    const bool   can_pipeline = (kv_len >= FA_MIN_KV_BLOCKS * bc_unit && n_threads >= 2);
    const size_t Bc_limit     = can_pipeline ? hex_align_down(kv_len / FA_MIN_KV_BLOCKS, bc_unit) :
                                               (kv_len >= bc_unit ? hex_align_down(kv_len, bc_unit) : bc_unit);
    // Cost coefficients calibrated from profiling
@@ -200,7 +203,7 @@ static int hmx_fa_find_chunk_size(size_t * Br_out,
        }

        // Exact VTCM verification (alignment padding may push over budget)
-        while (Bc >= bc_unit && hmx_fa_compute_vtcm_usage(gqa_factor, DK, DV, Br, Bc, n_threads) > vtcm_budget) {
+        while (Bc >= bc_unit && hmx_fa_compute_vtcm_usage(gqa_factor, DK, DV, Br, Bc, n_threads, can_pipeline) > vtcm_budget) {
            Bc -= bc_unit;
        }
        if (Bc < bc_unit) {
@@ -303,6 +306,7 @@ struct hmx_fa_context {
    uint32_t     n_kv_heads;  // number of KV heads
    uint32_t     n_heads;     // number of Q heads
    uint32_t     G;           // GQA factor = n_heads / n_kv_heads
+    struct fastdiv_values div_G;
    uint32_t     n_kv_blocks;
    uint32_t     neq1;        // Q token count

@@ -321,7 +325,7 @@ struct hmx_fa_context {
    __fp16 *     vtcm_k_fp16[2];       // K DMA double-buffer [Bc, D]
    __fp16 *     vtcm_v_fp16[2];       // V DMA double-buffer [Bc, D]
    __fp16 *     vtcm_k_tiles;         // K tiles (transposed)
-    __fp16 *     vtcm_v_tiles;         // V tiles (column-major)
+    __fp16 *     vtcm_v_tiles[2];      // V tiles (column-major, double-buffered)
    __fp16 *     vtcm_s_tiles;         // S = QK^T [g_br, Bc]
    __fp16 *     vtcm_p_tiles;         // P = softmax(S) [g_br, Bc]
    __fp16 *     vtcm_d_tiles;         // Diagonal rescale [g_br, g_br]
@@ -402,7 +406,9 @@ static void fa_v_interleave_thread(unsigned int n, unsigned int i, void * data)
        return;
    }

-    hmx_interleave_cols_to_tiles(factx->vtcm_v_tiles, factx->vtcm_v_fp16[args->buf_idx], total_rows, (int) factx->DV,
+    __fp16 * v_tiles_dest = factx->use_pipeline ? factx->vtcm_v_tiles[args->buf_idx] : factx->vtcm_v_tiles[0];
+
+    hmx_interleave_cols_to_tiles(v_tiles_dest, factx->vtcm_v_fp16[args->buf_idx], total_rows, (int) factx->DV,
                             (int) args->src_stride, (int) args->n_col_tiles, start, end);
 }

@@ -464,10 +470,10 @@ static void fa_q_load_thread(unsigned int n, unsigned int i, void * data) {
    for (size_t r = start; r < end; r += 2) {
        const bool next_row_valid = (r + 1) < n_rows_g;

-        const size_t q_idx0 = (r + 0) / G;
-        const size_t h_idx0 = (r + 0) % G;
-        const size_t q_idx1 = (r + 1) / G;
-        const size_t h_idx1 = (r + 1) % G;
+        const size_t q_idx0 = fastdiv(r + 0, &factx->div_G);
+        const size_t h_idx0 = fastmodulo(r + 0, G, &factx->div_G);
+        const size_t q_idx1 = fastdiv(r + 1, &factx->div_G);
+        const size_t h_idx1 = fastmodulo(r + 1, G, &factx->div_G);

        const uint8_t * q_ptr0 = (const uint8_t *) q->data + (q_start + q_idx0) * q->nb[1] +
                                                  (kv_head * G + h_idx0) * q->nb[2] + ib3 * q->nb[3];
@@ -567,8 +573,8 @@ static void fa_o_store_thread(unsigned int n, unsigned int i, void * data) {
    const uint32_t            ib3        = args->ib3;

    for (size_t r = start; r < end; ++r) {
-        const size_t q_idx = r / G;
-        const size_t h_idx = r % G;
+        const size_t q_idx = fastdiv(r, &factx->div_G);
+        const size_t h_idx = fastmodulo(r, G, &factx->div_G);

        // FIX(dst-indexing): ggml_flash_attn_ext() creates dst as permute(0,2,1,3) ->
        // [DV, n_heads, n_tokens, n_seq], so head stride is nb[1] and token stride is nb[2].
@@ -780,11 +786,11 @@ static void fa_softmax_thread(unsigned int n, unsigned int i, void * data) {
                    if (args->mask_vtcm) {
                        // Read mask from VTCM buffer (DMA'd per KV block).
                        // GQA dedup (scheme B): skip load when qi unchanged.
-                        const size_t qi0 = (r + 0) / G;
+                        const size_t qi0 = fastdiv(r + 0, &factx->div_G);
                        v_mask0 = *(const HVX_UVector *) (args->mask_vtcm + qi0 * args->mask_vtcm_row_stride + c);
                        v_mask1 = v_neg_inf;
                        if (r + 1 < (int) n_rows_g) {
-                            const size_t qi1 = (r + 1) / G;
+                            const size_t qi1 = fastdiv(r + 1, &factx->div_G);
                            if (qi1 == qi0) {
                                v_mask1 = v_mask0;  // scheme B: reuse — same mask row
                            } else {
@@ -794,8 +800,8 @@ static void fa_softmax_thread(unsigned int n, unsigned int i, void * data) {
                    } else {
                        // Fallback: read mask directly from DDR (when mask->ne[2] > 1).
                        const struct htp_tensor * mask   = args->mask;
-                        const size_t              q_idx0 = args->q_start + ((r + 0) / G);
-                        const size_t              h_idx0 = args->kv_head * G + (r + 0) % G;
+                        const size_t              q_idx0 = args->q_start + fastdiv(r + 0, &factx->div_G);
+                        const size_t              h_idx0 = args->kv_head * G + fastmodulo(r + 0, G, &factx->div_G);
                        const uint32_t            im2_0  = h_idx0 % mask->ne[2];
                        const uint32_t            im3_0  = args->ib3 % mask->ne[3];

@@ -805,12 +811,12 @@ static void fa_softmax_thread(unsigned int n, unsigned int i, void * data) {
                        v_mask1 = v_neg_inf;

                        if (r + 1 < (int) n_rows_g) {
-                            const size_t q_idx1 = args->q_start + ((r + 1) / G);
+                            const size_t q_idx1 = args->q_start + fastdiv(r + 1, &factx->div_G);
                            if (q_idx1 == q_idx0) {
                                // scheme B: same mask row in DDR path
                                v_mask1 = v_mask0;
                            } else {
-                                const size_t   h_idx1 = args->kv_head * G + (r + 1) % G;
+                                const size_t   h_idx1 = args->kv_head * G + fastmodulo(r + 1, G, &factx->div_G);
                                const uint32_t im2_1  = h_idx1 % mask->ne[2];
                                const uint32_t im3_1  = args->ib3 % mask->ne[3];
                                const __fp16 * m1_ptr = (const __fp16 *) ((const uint8_t *) mask->data + q_idx1 * mask->nb[1] +
@@ -1191,14 +1197,13 @@ static void hmx_fa_o_norm_worker(void * data) {
 // Row r in the GQA-merged block maps to Q head h = kv_head * G + r % G.
 // slope(h) = m0^(h+1) when h < n_head_log2, else m1^(2*(h-n_head_log2)+1).
 // When max_bias == 0, all slopes are 1.0 (no ALiBi).
-static __attribute__((noinline)) void fa_compute_slopes(fa_softmax_args_t * sargs,
+static __attribute__((noinline)) void fa_compute_slopes(
                              const struct hmx_fa_context * factx,
                              uint32_t                      kv_head,
                              size_t                        n_rows_g) {
+    __fp16 * slopes = factx->vtcm_slopes;
    if (factx->max_bias == 0.0f) {
-        for (size_t r = 0; r < n_rows_g; ++r) {
-            sargs->slopes[r] = 1.0f;
-        }
+        hvx_splat_f16_a(slopes, 1.0f, n_rows_g);
        return;
    }

@@ -1207,10 +1212,32 @@ static __attribute__((noinline)) void fa_compute_slopes(fa_softmax_args_t * sarg
    const float    m0          = factx->m0;
    const float    m1          = factx->m1;

-    for (size_t r = 0; r < n_rows_g; ++r) {
-        const uint32_t h = kv_head * G + r % G;
-        sargs->slopes[r] = (h < n_head_log2) ? powf(m0, h + 1) : powf(m1, 2 * (h - n_head_log2) + 1);
+    __fp16 temp_slopes[512] __attribute__((aligned(128)));
+    if (G <= 32) {
+        // Fast path: Compute G unique slope values in vector registers
+        HVX_Vector v_val = hvx_alibi_slopes(kv_head, G, n_head_log2, m0, m1);
+
+        __fp16 temp_slopes_aligned[64] __attribute__((aligned(128)));
+        hvx_vmem(temp_slopes_aligned) = hvx_vec_f32_to_f16(v_val, Q6_V_vzero());
+
+        for (uint32_t i = 0; i < G; ++i) {
+            temp_slopes[i] = temp_slopes_aligned[i];
+        }
+    } else {
+        // Fallback path: G > 32 (rare configurations)
+        for (uint32_t i = 0; i < G; ++i) {
+            temp_slopes[i] = (__fp16)alibi_slope(kv_head * G + i, n_head_log2, m0, m1);
+        }
    }
+
+    // Allocate stack buffer to avoid scalar writes to VTCM (which generates L2 misses)
+    __fp16 local_slopes[n_rows_g] __attribute__((aligned(128)));
+    for (size_t r = 0; r < n_rows_g; ++r) {
+        local_slopes[r] = temp_slopes[fastmodulo(r, G, &factx->div_G)];
+    }
+
+    // Copy to VTCM slopes using HVX block copy (both are aligned to 128 bytes)
+    hvx_copy_f16_aa((uint8_t *)slopes, (const uint8_t *)local_slopes, n_rows_g);
 }

 // ============================================================================
@@ -1248,28 +1275,28 @@ int hmx_flash_attn_ext(struct htp_ops_context * octx) {
    if (DK % 32 != 0 || DV % 32 != 0) {
        return HTP_STATUS_NO_SUPPORT;
    }
-    if (neq1 < 32) {
-        return HTP_STATUS_NO_SUPPORT;
-    }

    // GQA factor
    const uint32_t n_kv_heads = k->ne[2];
    const uint32_t G          = neq2 / n_kv_heads;

    // Thread count for multi-thread HVX phases
-    const uint32_t n_threads = octx->n_threads;
+    const uint32_t n_threads_init = octx->n_threads;

    // Compute dynamic block sizes (GQA-aware, accounting for per-thread row bufs)
    size_t       Br, Bc;
    const size_t vtcm_budget = ctx->vtcm_size;
-    if (hmx_fa_find_chunk_size(&Br, &Bc, G, DK, DV, neq1, nek1, vtcm_budget, n_threads) != 0) {
+    if (hmx_fa_find_chunk_size(&Br, &Bc, G, DK, DV, neq1, nek1, vtcm_budget, n_threads_init) != 0) {
        return HTP_STATUS_VTCM_TOO_SMALL;
    }

    const size_t g_br = hex_align_up(G * Br, HMX_FP16_TILE_N_ROWS);

    const uint32_t n_kv_blocks  = (nek1 + Bc - 1) / Bc;
-    const bool     use_pipeline = (n_kv_blocks >= FA_MIN_KV_BLOCKS && n_threads >= 2);
+    const bool     use_pipeline = (n_kv_blocks >= FA_MIN_KV_BLOCKS && n_threads_init >= 2);
+
+    // Bypass thread pool dispatch for small prompts/non-pipelined prefill by setting n_threads = 1
+    const uint32_t n_threads = use_pipeline ? n_threads_init : 1;

    FARF(HIGH, "hmx-fa: neq1=%u nek1=%u DK=%u DV=%u G=%u Br=%zu Bc=%zu g_br=%zu n_kv_blocks=%u pipeline=%d vtcm=%zu",
         neq1, nek1, DK, DV, G, Br, Bc, g_br, n_kv_blocks, use_pipeline, vtcm_budget);
@@ -1285,6 +1312,7 @@ int hmx_flash_attn_ext(struct htp_ops_context * octx) {
    factx.n_kv_heads     = n_kv_heads;
    factx.n_heads        = neq2;
    factx.G              = G;
+    factx.div_G          = init_fastdiv_values(G);
    factx.neq1           = neq1;
    factx.Br             = (uint32_t) Br;
    factx.Bc             = (uint32_t) Bc;
@@ -1357,7 +1385,12 @@ int hmx_flash_attn_ext(struct htp_ops_context * octx) {
    factx.vtcm_v_fp16[0]      = (__fp16 *) vtcm_seq_alloc(&vtcm_cur, v_dma_bytes);
    factx.vtcm_v_fp16[1]      = (__fp16 *) vtcm_seq_alloc(&vtcm_cur, v_dma_bytes);
    factx.vtcm_k_tiles        = (__fp16 *) vtcm_seq_alloc(&vtcm_cur, k_tile_bytes);
-    factx.vtcm_v_tiles        = (__fp16 *) vtcm_seq_alloc(&vtcm_cur, v_tile_bytes);
+    factx.vtcm_v_tiles[0]     = (__fp16 *) vtcm_seq_alloc(&vtcm_cur, v_tile_bytes);
+    if (use_pipeline) {
+        factx.vtcm_v_tiles[1] = (__fp16 *) vtcm_seq_alloc(&vtcm_cur, v_tile_bytes);
+    } else {
+        factx.vtcm_v_tiles[1] = NULL;
+    }
    factx.vtcm_s_tiles        = (__fp16 *) vtcm_seq_alloc(&vtcm_cur, s_tile_bytes);
    factx.vtcm_p_tiles        = (__fp16 *) vtcm_seq_alloc(&vtcm_cur, s_tile_bytes);
    factx.vtcm_d_tiles        = (__fp16 *) vtcm_seq_alloc(&vtcm_cur, d_tile_bytes);
@@ -1460,6 +1493,8 @@ int hmx_flash_attn_ext(struct htp_ops_context * octx) {
                // ---- KV block loop with DMA double-buffering ----
                size_t buf_idx = 0;

+                fa_compute_slopes(&factx, kv_head, n_rows_g);
+
                // Prefetch first KV block
                if (factx.n_kv_blocks > 0) {
                    const uint32_t kv_rows0 = hex_smin(Bc, nek1);
@@ -1538,7 +1573,7 @@ int hmx_flash_attn_ext(struct htp_ops_context * octx) {
                            ou_job.o_curr           = o_tile_curr;
                            ou_job.o_prev           = o_tile_prev;
                            ou_job.p_tiles          = factx.vtcm_p_tiles;
-                            ou_job.v_tiles          = factx.vtcm_v_tiles;
+                            ou_job.v_tiles          = factx.vtcm_v_tiles[1 - buf_idx];
                            ou_job.d_tiles          = factx.vtcm_d_tiles;
                            ou_job.hmx_scales       = factx.vtcm_hmx_scales_id;
                            ou_job.n_row_tiles      = n_row_tiles;
@@ -1553,11 +1588,6 @@ int hmx_flash_attn_ext(struct htp_ops_context * octx) {
                        fa_phase_k_interleave(&factx, kv_rows, k_src_stride, buf_idx);
                        TIMER_STOP(k_interleave);

-                        if (kv_blk > 0) {
-                            hmx_queue_pop(hmx_q);
-                            hex_swap_ptr((void **) &o_tile_curr, (void **) &o_tile_prev);
-                        }
-
                        // ---- Phase 2: qk_dot(blk) on HMX ‖ V_int(blk) + DMA prefetch on HVX ----
                        qk_job.q_tiles        = factx.vtcm_q_tiles;
                        qk_job.k_tiles        = factx.vtcm_k_tiles;
@@ -1577,6 +1607,13 @@ int hmx_flash_attn_ext(struct htp_ops_context * octx) {
                        fa_phase_v_interleave(&factx, kv_rows, v_src_stride, buf_idx, n_tiles_per_bc);
                        TIMER_STOP(v_interleave);

+                        // Pop and swap previous block's output update (deferred HMX pop)
+                        if (kv_blk > 0) {
+                            hmx_queue_pop(hmx_q);
+                            hex_swap_ptr((void **) &o_tile_curr, (void **) &o_tile_prev);
+                        }
+
+                        // Pop current block's dot product job
                        hmx_queue_pop(hmx_q);
                        TIMER_STOP(qk_dot);

@@ -1604,7 +1641,6 @@ int hmx_flash_attn_ext(struct htp_ops_context * octx) {
                        sargs.mask_vtcm            = has_mask_dma ? (const __fp16 *) factx.vtcm_mask_buf : NULL;
                        sargs.mask_vtcm_row_stride = factx.mask_buf_row_stride;
                        sargs.slopes               = factx.vtcm_slopes;
-                        fa_compute_slopes(&sargs, &factx, kv_head, n_rows_g);

                        TIMER_START(softmax);
                        fa_phase_softmax_and_build_d(&factx, &sargs, n_row_tiles, n_row_tiles_g_br);
@@ -1620,7 +1656,7 @@ int hmx_flash_attn_ext(struct htp_ops_context * octx) {
                        ou_job.o_curr           = o_tile_curr;
                        ou_job.o_prev           = o_tile_prev;
                        ou_job.p_tiles          = factx.vtcm_p_tiles;
-                        ou_job.v_tiles          = factx.vtcm_v_tiles;
+                        ou_job.v_tiles          = factx.vtcm_v_tiles[1 - buf_idx];
                        ou_job.d_tiles          = factx.vtcm_d_tiles;
                        ou_job.hmx_scales       = factx.vtcm_hmx_scales_id;
                        ou_job.n_row_tiles      = n_row_tiles;
@@ -1715,7 +1751,6 @@ int hmx_flash_attn_ext(struct htp_ops_context * octx) {
                        sargs.mask_vtcm            = has_mask_dma ? (const __fp16 *) factx.vtcm_mask_buf : NULL;
                        sargs.mask_vtcm_row_stride = factx.mask_buf_row_stride;
                        sargs.slopes               = factx.vtcm_slopes;
-                        fa_compute_slopes(&sargs, &factx, kv_head, n_rows_g);

                        TIMER_START(softmax);
                        fa_phase_softmax_and_build_d(&factx, &sargs, n_row_tiles, n_row_tiles_g_br);
@@ -1735,7 +1770,7 @@ int hmx_flash_attn_ext(struct htp_ops_context * octx) {
                            const size_t DV_tiles           = (size_t) (DV / 32);
                            const __fp16 * restrict d_base  = factx.vtcm_d_tiles;
                            const __fp16 * restrict p_base  = factx.vtcm_p_tiles;
-                            const __fp16 * restrict v_base  = factx.vtcm_v_tiles;
+                            const __fp16 * restrict v_base  = factx.vtcm_v_tiles[0];
                            const __fp16 * restrict op_base = o_tile_prev;
                            __fp16 * restrict oc_base       = o_tile_curr;
                            __builtin_assume(n_row_tiles > 0);
@@ -0,0 +1,6 @@
+// HMX operations compiled as a single translation unit.
+// This allows interprocedural optimizations within HMX ops without requiring global HTP LTO.
+
+#include "hmx-queue.c"
+#include "hmx-matmul-ops.c"
+#include "hmx-flash-attn-ops.c"
@@ -52,14 +52,32 @@ int hmx_matmul_f16_f32(struct htp_context *ctx,
 // Batch semantics match ggml_mul_mat(): src0 broadcasts to src1 in dims 2/3.
 int hmx_matmul_f16_f32_batched(struct htp_context *ctx, const hmx_matmul_f16_f32_batched_params_t *params);

-// HMX matrix multiplication — quantised weights (Q4_0/Q8_0/IQ4_NL/MXFP4)
-int hmx_matmul_q_f32(struct htp_context *ctx,
+// HMX matrix multiplication — all supported weight types (F16/F32/Q4_0/Q4_1/Q8_0/IQ4_NL/MXFP4)
+int hmx_matmul_2d_f32(struct htp_context *ctx,
                                      float *restrict dst,
                                      const float *activation,
                                      const uint8_t *permuted_weight,
                                      int m, int k, int n,
+                                      int act_stride,
+                                      int weight_stride,
                                      int weight_type);

+struct mmid_row_mapping;
+
+int hmx_matmul_id_2d_f32(struct htp_context *ctx,
+                                         float *restrict dst,
+                                         const float *activation,
+                                         const uint8_t *permuted_weight,
+                                         int m, int k, int n,
+                                         int ne11,
+                                         size_t act_nb1, size_t act_nb2,
+                                         size_t dst_nb1, size_t dst_nb2,
+                                         int weight_stride,
+                                         int weight_type,
+                                         const struct mmid_row_mapping *matrix_rows,
+                                         int cur_a,
+                                         int mapping_stride);
+
 // HMX flash attention
 int hmx_flash_attn_ext(struct htp_ops_context * octx);

@@ -79,6 +79,10 @@ struct htp_context {

    uint64_t               max_vmem;

+    // Persistent DDR scratchpad for MUL_MAT_ID mappings
+    void *                 ddr_spad_base;
+    size_t                 ddr_spad_size;
+
    struct htp_ops_context octx;

 #ifdef HTP_HAS_HMX
@@ -20,6 +20,7 @@ enum htp_data_type {
    HTP_TYPE_F32    = 0,
    HTP_TYPE_F16    = 1,
    HTP_TYPE_Q4_0   = 2,
+    HTP_TYPE_Q4_1   = 3,
    HTP_TYPE_Q8_0   = 8,
    HTP_TYPE_IQ4_NL = 20,
    HTP_TYPE_I32    = 26,
@@ -28,6 +29,7 @@ enum htp_data_type {

    // types used internally for repack, dyn.quant, etc
    HTP_TYPE_Q4_0x4x2 = 200,
+    HTP_TYPE_Q4_1x4x2,
    HTP_TYPE_Q8_0x4x2,
    HTP_TYPE_MXFP4x4x2,

@@ -56,6 +58,7 @@ enum htp_op_code {
    HTP_OP_MUL_MAT,
    HTP_OP_MUL_MAT_ID,
    HTP_OP_RMS_NORM,
+    HTP_OP_RMS_NORM_MUL,
    HTP_OP_UNARY_SILU,
    HTP_OP_UNARY_GELU,
    HTP_OP_UNARY_SIGMOID,
@@ -0,0 +1,47 @@
+#ifndef HVX_FLASH_ATTN_H
+#define HVX_FLASH_ATTN_H
+
+#include <math.h>
+#include "hvx-utils.h"
+
+// Scalar helper to compute a single ALiBi slope.
+static inline float alibi_slope(uint32_t h, uint32_t n_head_log2, float m0, float m1) {
+    return (h < n_head_log2) ? powf(m0, h + 1) : powf(m1, 2 * (h - n_head_log2) + 1);
+}
+
+// Vectorized helper to compute 32 ALiBi slopes starting from (kv_head * G).
+static inline HVX_Vector hvx_alibi_slopes(
+    uint32_t kv_head,
+    uint32_t G,
+    uint32_t n_head_log2,
+    float m0,
+    float m1
+) {
+    static const float ramp_32[32] __attribute__((aligned(128))) = {
+        0.0f, 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f,
+        8.0f, 9.0f, 10.0f, 11.0f, 12.0f, 13.0f, 14.0f, 15.0f,
+        16.0f, 17.0f, 18.0f, 19.0f, 20.0f, 21.0f, 22.0f, 23.0f,
+        24.0f, 25.0f, 26.0f, 27.0f, 28.0f, 29.0f, 30.0f, 31.0f
+    };
+    HVX_Vector v_ramp = hvx_vmem(ramp_32);
+    HVX_Vector v_h_base = hvx_vec_splat_f32((float)(kv_head * G));
+    HVX_Vector v_h = hvx_vec_add_f32_f32(v_h_base, v_ramp);
+
+    // Compute exponent_m0: h + 1
+    HVX_Vector v_exp_m0 = hvx_vec_add_f32_f32(v_h, hvx_vec_splat_f32(1.0f));
+
+    // Compute exponent_m1: 2 * (h - n_head_log2) + 1
+    HVX_Vector v_n_head_log2 = hvx_vec_splat_f32((float)n_head_log2);
+    HVX_Vector v_h_minus = hvx_vec_sub_f32_f32(v_h, v_n_head_log2);
+    HVX_Vector v_exp_m1 = hvx_vec_add_f32_f32(hvx_vec_mul_f32_f32(hvx_vec_splat_f32(2.0f), v_h_minus), hvx_vec_splat_f32(1.0f));
+
+    // Compute powers
+    HVX_Vector v_pow_m0 = hvx_vec_pow_const_base_f32(m0, v_exp_m0);
+    HVX_Vector v_pow_m1 = hvx_vec_pow_const_base_f32(m1, v_exp_m1);
+
+    // Select based on h < n_head_log2
+    HVX_VectorPred p_cond = Q6_Q_vcmp_gt_VsfVsf(v_n_head_log2, v_h); // v_n_head_log2 > v_h <=> h < n_head_log2
+    return Q6_V_vmux_QVV(p_cond, v_pow_m0, v_pow_m1);
+}
+
+#endif /* HVX_FLASH_ATTN_H */
@@ -0,0 +1,65 @@
+#ifndef HVX_LOG_H
+#define HVX_LOG_H
+
+#include "hvx-base.h"
+
+// Approximates ln(x) element-wise for float vectors.
+// x must contain positive float elements.
+// Uses Abramowitz & Stegun polynomial approximation 4.1.44 for ln(1+y) over [0, 1].
+static inline HVX_Vector hvx_vec_log_f32(HVX_Vector x) {
+    // x = m * 2^e, where m in [1, 2)
+    HVX_Vector biased_e = Q6_Vuw_vlsr_VuwR(x, 23);
+    HVX_Vector e_int = Q6_Vw_vsub_VwVw(biased_e, Q6_V_vsplat_R(127));
+    HVX_Vector e_float = Q6_Vsf_equals_Vw(e_int);
+
+    // Extract mantissa and set exponent to 127 (which represents float value in [1.0, 2.0))
+    HVX_Vector mant_mask = Q6_V_vsplat_R(0x007FFFFF);
+    HVX_Vector exp_127 = Q6_V_vsplat_R(0x3F800000);
+    HVX_Vector m = Q6_V_vor_VV(Q6_V_vand_VV(x, mant_mask), exp_127);
+
+    // y = m - 1.0f, y in [0, 1)
+    HVX_Vector y = hvx_vec_sub_f32_f32(m, hvx_vec_splat_f32(1.0f));
+
+    // Abramowitz & Stegun 4.1.44 polynomial approximation of ln(1+y)
+    HVX_Vector c;
+    HVX_Vector res;
+
+    c   = hvx_vec_splat_f32(-0.0064535442f);
+    res = hvx_vec_mul_f32_f32(y, c);
+
+    c   = hvx_vec_splat_f32(0.0360884937f);
+    res = hvx_vec_add_f32_f32(res, c);
+    res = hvx_vec_mul_f32_f32(y, res);
+
+    c   = hvx_vec_splat_f32(-0.0953293897f);
+    res = hvx_vec_add_f32_f32(res, c);
+    res = hvx_vec_mul_f32_f32(y, res);
+
+    c   = hvx_vec_splat_f32(0.1676540711f);
+    res = hvx_vec_add_f32_f32(res, c);
+    res = hvx_vec_mul_f32_f32(y, res);
+
+    c   = hvx_vec_splat_f32(-0.2407338084f);
+    res = hvx_vec_add_f32_f32(res, c);
+    res = hvx_vec_mul_f32_f32(y, res);
+
+    c   = hvx_vec_splat_f32(0.3317990258f);
+    res = hvx_vec_add_f32_f32(res, c);
+    res = hvx_vec_mul_f32_f32(y, res);
+
+    c   = hvx_vec_splat_f32(-0.4998741238f);
+    res = hvx_vec_add_f32_f32(res, c);
+    res = hvx_vec_mul_f32_f32(y, res);
+
+    c   = hvx_vec_splat_f32(0.9999964239f);
+    res = hvx_vec_add_f32_f32(res, c);
+    res = hvx_vec_mul_f32_f32(y, res);
+
+    // ln(x) = e * ln(2) + ln(1+y)
+    HVX_Vector ln2 = hvx_vec_splat_f32(0.69314718056f);
+    HVX_Vector term_e = hvx_vec_mul_f32_f32(e_float, ln2);
+
+    return hvx_vec_add_f32_f32(term_e, res);
+}
+
+#endif /* HVX_LOG_H */
@@ -0,0 +1,42 @@
+#ifndef HVX_POW_H
+#define HVX_POW_H
+
+#include <math.h>
+#include "hvx-base.h"
+#include "hvx-exp.h"
+#include "hvx-log.h"
+
+// Approximates base^exponent element-wise for float vectors.
+// base must be a positive constant. exponent is an HVX f32 vector.
+// Uses base^x = exp(x * ln(base)).
+static inline HVX_Vector hvx_vec_pow_const_base_f32(float base, HVX_Vector exponent) {
+    float ln_base = logf(base);
+    HVX_Vector ln_base_v = hvx_vec_splat_f32(ln_base);
+    HVX_Vector x = hvx_vec_mul_f32_f32(exponent, ln_base_v);
+
+    static const float kInf    = INFINITY;
+    static const float kMaxExp = 88.7228f;
+
+    const HVX_Vector max_exp = hvx_vec_splat_f32(kMaxExp);
+    const HVX_Vector inf     = hvx_vec_splat_f32(kInf);
+
+    return hvx_vec_exp_f32_guard(x, max_exp, inf);
+}
+
+// Approximates base^exponent element-wise for float vectors.
+// base and exponent are HVX f32 vectors. base elements must be positive.
+// Uses base^exponent = exp(exponent * ln(base)).
+static inline HVX_Vector hvx_vec_pow_f32(HVX_Vector base, HVX_Vector exponent) {
+    HVX_Vector ln_base = hvx_vec_log_f32(base);
+    HVX_Vector x = hvx_vec_mul_f32_f32(exponent, ln_base);
+
+    static const float kInf    = INFINITY;
+    static const float kMaxExp = 88.7228f;
+
+    const HVX_Vector max_exp = hvx_vec_splat_f32(kMaxExp);
+    const HVX_Vector inf     = hvx_vec_splat_f32(kInf);
+
+    return hvx_vec_exp_f32_guard(x, max_exp, inf);
+}
+
+#endif /* HVX_POW_H */
@@ -17,5 +17,7 @@
 #include "hvx-floor.h"
 #include "hvx-sin-cos.h"
 #include "hvx-base.h"
+#include "hvx-pow.h"
+#include "hvx-log.h"

 #endif /* HVX_UTILS_H */
@@ -12,6 +12,7 @@
 #include <HAP_mem.h>
 #include <HAP_power.h>
 #include <HAP_ps.h>
+#include <HAP_dcvs.h>
 #include <qurt.h>
 #include <qurt_thread.h>
 #include <qurt_memory.h>
@@ -63,8 +64,7 @@ AEEResult htp_iface_open(const char * uri, remote_handle64 * handle) {

        request.type                              = HAP_power_set_DCVS_v3;
        request.dcvs_v3.set_dcvs_enable           = TRUE;
-        request.dcvs_v3.dcvs_enable               = TRUE;
-        request.dcvs_v3.dcvs_option               = HAP_DCVS_V2_PERFORMANCE_MODE;
+        request.dcvs_v3.dcvs_enable               = FALSE;
        request.dcvs_v3.set_bus_params            = TRUE;
        request.dcvs_v3.bus_params.min_corner     = HAP_DCVS_VCORNER_MAX;
        request.dcvs_v3.bus_params.max_corner     = HAP_DCVS_VCORNER_MAX;
@@ -75,6 +75,10 @@ AEEResult htp_iface_open(const char * uri, remote_handle64 * handle) {
        request.dcvs_v3.core_params.target_corner = HAP_DCVS_VCORNER_MAX;
        request.dcvs_v3.set_sleep_disable         = TRUE;
        request.dcvs_v3.sleep_disable             = TRUE;
+
+#if (__HEXAGON_ARCH__ >= 79)
+        HAP_set_dcvs_v3_protected_bus_corners(&request, 1);
+#endif
        if ((err = HAP_power_set((void *) ctx, &request)) != 0) {
            return err;
        }
@@ -103,7 +107,7 @@ AEEResult htp_iface_open(const char * uri, remote_handle64 * handle) {
        FARF(ALWAYS, "Setting HMX clock\n");
        err = HAP_power_set((void *) ctx, &request);
        if (err != AEE_SUCCESS) {
-            FARF(ERROR, "Error setting HMX clock.");
+            FARF(ERROR, "ggml-hex: error setting HMX clock.");
            return err;
        }
    }
@@ -117,7 +121,7 @@ AEEResult htp_iface_open(const char * uri, remote_handle64 * handle) {
        FARF(ALWAYS, "Powering HMX on\n");
        err = HAP_power_set((void *) ctx, &request);
        if (err != AEE_SUCCESS) {
-            FARF(ERROR, "Error powering on HMX.");
+            FARF(ERROR, "ggml-hex: error powering on HMX.");
            return err;
        }
    }
@@ -423,10 +427,18 @@ AEEResult htp_iface_start(remote_handle64 handle, uint32 sess_id, uint64 dsp_que
        ctx->dma[i] = dma_queue_create(256); // queue depth
    }

+    ctx->ddr_spad_size = 512 * 1024; // 512 KB
+    ctx->ddr_spad_base = memalign(128, ctx->ddr_spad_size);
+
    // init worker pool
    err = worker_pool_init(&ctx->worker_pool, n_hvx);
    if (err != AEE_SUCCESS) {
        FARF(ERROR, "Unable to create worker pool");
+        if (ctx->ddr_spad_base) {
+            free(ctx->ddr_spad_base);
+            ctx->ddr_spad_base = NULL;
+            ctx->ddr_spad_size = 0;
+        }
        return err;
    }

@@ -474,6 +486,12 @@ AEEResult htp_iface_stop(remote_handle64 handle) {

    vtcm_free(ctx);

+    if (ctx->ddr_spad_base) {
+        free(ctx->ddr_spad_base);
+        ctx->ddr_spad_base = NULL;
+        ctx->ddr_spad_size = 0;
+    }
+
    return AEE_SUCCESS;
 }

@@ -537,6 +555,7 @@ static int execute_op(struct htp_ops_context * octx) {

        case HTP_OP_NORM:
        case HTP_OP_RMS_NORM:
+        case HTP_OP_RMS_NORM_MUL:
        case HTP_OP_SCALE:
        case HTP_OP_SQR:
        case HTP_OP_SQRT:
@@ -853,6 +872,11 @@ static void htp_packet_callback(dspqueue_t queue, int error, void * context) {
        for (uint32_t i=0; i < n_ops; i++) {
            struct profile_data prof;

+            if (i == (n_ops-1)) {
+                // wake up the host before starting the last op
+                dspqueue_write_early_wakeup_noblock(queue, 0, 0);
+            }
+
            profile_start(ctx->profiler, &prof);

            proc_op_req(octx, tens, i, &ops[i]);
@@ -869,8 +893,6 @@ static void htp_packet_callback(dspqueue_t queue, int error, void * context) {
            }
        }

-        // dspqueue_write_early_wakeup_noblock(ctx->queue, 10, 0);
-
        struct htp_opbatch_rsp rsp;
        rsp.id        = req.id;
        rsp.status    = HTP_STATUS_OK;
@@ -511,6 +511,8 @@ int op_pad(struct htp_ops_context * octx) {
        octx->dst_spad.size  = n_threads * octx->dst_spad.size_per_thread;
        octx->src0_spad.data = octx->ctx->vtcm_base;
        octx->dst_spad.data  = octx->src0_spad.data + octx->src0_spad.size;
+        octx->src0_spad.src  = NULL;
+        octx->dst_spad.src   = NULL;
    }

    struct htp_pad_context pctx = {
@@ -23,21 +23,26 @@ struct htp_unary_context {

    // Precomputed values
    const uint8_t *           data_src0;
+    const uint8_t *           data_src1;            // weight/scale tensor for RMS_NORM_MUL
    uint8_t *                 data_dst;

    size_t                    src0_data_row_size;   // actual data bytes per row
+    size_t                    src1_data_row_size;
    size_t                    dst_data_row_size;    // actual data bytes per row

    size_t                    src0_row_size_aligned;
+    size_t                    src1_row_size_aligned;
    size_t                    dst_row_size_aligned;

    size_t                    src0_spad_half_size;
+    size_t                    src1_spad_half_size;
    size_t                    dst_spad_half_size;

    uint32_t                  block;
    uint32_t                  src0_nrows;
    uint32_t                  src0_nrows_per_thread;
    uint32_t                  nc;
+    bool                      broadcast_weight;
 };

 // Convert flat row index to DDR byte offset using the tensor's actual strides.
@@ -158,6 +163,71 @@ static void hvx_fast_rms_norm_f32(const uint8_t * restrict src,
    }
 }

+static void hvx_fast_rms_norm_mul_f32(const uint8_t * restrict src,
+                                      const uint8_t * restrict weight,
+                                      uint8_t * restrict dst,
+                                      const int num_elems,
+                                      float     epsilon) {
+    const HVX_Vector * restrict v_src    = (const HVX_Vector *) src;
+    const HVX_Vector * restrict v_weight = (const HVX_Vector *) weight;
+    HVX_Vector * restrict v_dst          = (HVX_Vector *) dst;
+
+    const int nvec = num_elems / VLEN_FP32;    // number of full vectors
+    const int nloe = num_elems % VLEN_FP32;    // leftover elements
+
+    // Compute sum of squares for full vectors
+    HVX_Vector sum_v = Q6_V_vsplat_R(0x00000000);
+    HVX_Vector epsilon_v = hvx_vec_splat_f32(epsilon);
+
+    #pragma unroll(4)
+    for (int i = 0; i < nvec; i++) {
+        HVX_Vector v1 = v_src[i];
+        HVX_Vector v2 = Q6_Vqf32_vmpy_VsfVsf(v1, v1);
+        sum_v = Q6_Vqf32_vadd_Vqf32Vqf32(sum_v, v2);
+    }
+
+    // Handle tail elements using vectorized ops with masking
+    if (nloe > 0) {
+        HVX_VectorPred bmask = Q6_Q_vsetq_R(nloe * 4);
+        HVX_Vector v1 = Q6_V_vand_QV(bmask, v_src[nvec]);
+        HVX_Vector v2 = Q6_Vqf32_vmpy_VsfVsf(v1, v1);
+        sum_v = Q6_Vqf32_vadd_Vqf32Vqf32(sum_v, v2);
+    }
+
+    // Reduce HVX sum
+    sum_v = hvx_vec_reduce_sum_f32(Q6_Vsf_equals_Vqf32(sum_v));
+
+    HVX_Vector t_v            = hvx_vec_splat_f32((float) num_elems);
+    HVX_Vector denom_v        = hvx_vec_inverse_f32(t_v);
+    HVX_Vector mean_v         = Q6_Vqf32_vmpy_VsfVsf(sum_v, denom_v);
+    HVX_Vector mean_epsilon_v = Q6_Vqf32_vadd_Vqf32Vsf(mean_v, epsilon_v);
+
+    // Scale and multiply
+    HVX_Vector scale_v = hvx_vec_rsqrt_f32(Q6_Vsf_equals_Vqf32(mean_epsilon_v));
+
+    #pragma unroll(4)
+    for (int i = 0; i < nvec; i++) {
+        HVX_Vector v1 = v_src[i];
+        HVX_Vector v2 = Q6_Vqf32_vmpy_VsfVsf(v1, scale_v);
+        HVX_Vector v3 = Q6_Vsf_equals_Vqf32(v2);
+        HVX_Vector result = Q6_Vqf32_vmpy_VsfVsf(v3, v_weight[i]);
+        v_dst[i] = Q6_Vsf_equals_Vqf32(result);
+    }
+
+    // Handle tail elements using vectorized ops with masking
+    if (nloe > 0) {
+        HVX_VectorPred bmask = Q6_Q_vsetq_R(nloe * 4);
+        HVX_Vector v1 = Q6_V_vand_QV(bmask, v_src[nvec]);
+        HVX_Vector v2 = Q6_Vqf32_vmpy_VsfVsf(v1, scale_v);
+        HVX_Vector v3 = Q6_Vsf_equals_Vqf32(v2);
+        HVX_Vector result = Q6_Vqf32_vmpy_VsfVsf(v3, v_weight[nvec]);
+        HVX_Vector res_v = Q6_Vsf_equals_Vqf32(result);
+
+        // Store with masking to avoid overwriting memory beyond the tensor
+        hvx_vec_store_a(&v_dst[nvec], nloe * 4, res_v);
+    }
+}
+
 static void hvx_fast_norm_f32(const uint8_t * restrict src,
                                  uint8_t * restrict dst,
                                  uint8_t * restrict pad,
@@ -269,6 +339,27 @@ static void rms_norm_f32(const float * restrict src,
    }
 }

+static void rms_norm_mul_f32(const float * restrict src,
+                             const float * restrict weight,
+                             float * restrict dst,
+                             const uint32_t num_rows,
+                             const uint32_t row_elems,
+                             const size_t   row_size,
+                             const size_t   weight_row_size,
+                             int32_t *      op_params,
+                             bool           broadcast_weight) {
+    float epsilon = 0.f;
+    memcpy(&epsilon, op_params, sizeof(float));
+
+    for (uint32_t ir = 0; ir < num_rows; ir++) {
+        const uint8_t * restrict src_local = (const uint8_t *)src + (ir * row_size);
+        const uint8_t * restrict w_local   = (const uint8_t *)weight + (broadcast_weight ? 0 : ir * weight_row_size);
+        uint8_t * restrict dst_local       = (uint8_t *)dst + (ir * row_size);
+
+        hvx_fast_rms_norm_mul_f32(src_local, w_local, dst_local, row_elems, epsilon);
+    }
+}
+
 static void norm_f32(const float * restrict src,
                         float * restrict dst,
                         uint8_t * restrict spad,
@@ -598,12 +689,20 @@ static void unary_job_f32_per_thread(unsigned int nth, unsigned int ith, void *
    t1 = HAP_perf_get_qtimer_count();

    const uint8_t * restrict data_src = uctx->data_src0;
+    const uint8_t * restrict data_src1 = uctx->data_src1;
    uint8_t * restrict       data_dst = uctx->data_dst;

+    const struct htp_tensor * src1 = (htp_op == HTP_OP_RMS_NORM_MUL) ? octx->src[1] : NULL;
+    const uint32_t nb11 = src1 ? src1->nb[1] : 0;
+    const uint32_t nb12 = src1 ? src1->nb[2] : 0;
+    const uint32_t nb13 = src1 ? src1->nb[3] : 0;
+
    uint8_t * src0_spad_data = octx->src0_spad.data + (ith * octx->src0_spad.size_per_thread);
+    uint8_t * src1_spad_data = octx->src1_spad.data + (ith * octx->src1_spad.size_per_thread);
    uint8_t * dst_spad_data  = octx->dst_spad.data  + (ith * octx->dst_spad.size_per_thread);

    size_t src0_spad_half_size = uctx->src0_spad_half_size;
+    size_t src1_spad_half_size = uctx->src1_spad_half_size;
    size_t dst_spad_half_size  = uctx->dst_spad_half_size;

    // Non-contiguous tensors have gaps at dim-2/3 boundaries that a single-stride
@@ -624,6 +723,12 @@ static void unary_job_f32_per_thread(unsigned int nth, unsigned int ith, void *

    dma_queue * dma_queue = octx->ctx->dma[ith];

+    // If weight is broadcasted, load it once per thread at the beginning of execution
+    if (htp_op == HTP_OP_RMS_NORM_MUL && uctx->broadcast_weight) {
+        dma_queue_push(dma_queue, dma_make_ptr(src1_spad_data, data_src1), uctx->src1_row_size_aligned, 0, uctx->src1_data_row_size, 1);
+        dma_queue_flush(dma_queue);
+    }
+
    for (uint32_t ir = src0_start_row, spad_idx = 0; ir < src0_end_row && spad_idx < 2; spad_idx++) {
        const uint32_t block_size = unary_block_size(ir, src0_end_row, BLOCK, src0_contig, dst_contig, ne01, ne1);

@@ -636,6 +741,14 @@ static void unary_job_f32_per_thread(unsigned int nth, unsigned int ith, void *
        dma_queue_push(dma_queue,
            dma_make_ptr(src0_spad_data + (spad_idx * src0_spad_half_size), data_src + src0_off),
            src0_row_size_aligned, nb01, src0_data_row_size, block_size);
+
+        if (htp_op == HTP_OP_RMS_NORM_MUL && !uctx->broadcast_weight) {
+            const size_t src1_off = unary_row_offset(ir, ne01, ne02, nb11, nb12, nb13);
+            dma_queue_push(dma_queue,
+                dma_make_ptr(src1_spad_data + (spad_idx * src1_spad_half_size), data_src1 + src1_off),
+                uctx->src1_row_size_aligned, nb11, uctx->src1_data_row_size, block_size);
+        }
+
        ir += block_size;
    }

@@ -644,6 +757,10 @@ static void unary_job_f32_per_thread(unsigned int nth, unsigned int ith, void *

        float * dst_spad  = (float *) dma_queue_pop(dma_queue).src;
        float * src0_spad = (float *) dma_queue_pop(dma_queue).dst;
+        float * src1_spad = NULL;
+        if (htp_op == HTP_OP_RMS_NORM_MUL && !uctx->broadcast_weight) {
+            src1_spad = (float *) dma_queue_pop(dma_queue).dst;
+        }

        // Process block in VTCM
        switch (htp_op) {
@@ -653,6 +770,12 @@ static void unary_job_f32_per_thread(unsigned int nth, unsigned int ith, void *
            case HTP_OP_RMS_NORM:
                rms_norm_f32(src0_spad, dst_spad, NULL, block_size, ne0, src0_row_size_aligned, op_params);
                break;
+            case HTP_OP_RMS_NORM_MUL:
+                {
+                    const float * w_ptr = uctx->broadcast_weight ? (const float *) src1_spad_data : src1_spad;
+                    rms_norm_mul_f32(src0_spad, w_ptr, dst_spad, block_size, ne0, src0_row_size_aligned, uctx->src1_row_size_aligned, op_params, uctx->broadcast_weight);
+                }
+                break;
            case HTP_OP_SCALE:
                scale_f32(src0_spad, dst_spad, NULL, block_size, ne0, src0_row_size_aligned, op_params);
                break;
@@ -700,9 +823,16 @@ static void unary_job_f32_per_thread(unsigned int nth, unsigned int ith, void *
            if (pref_ir < src0_end_row) {
                const uint32_t pref_block_size = unary_block_size(pref_ir, src0_end_row, BLOCK, src0_contig, dst_contig, ne01, ne1);
                const size_t src0_pref_off = unary_row_offset(pref_ir, ne01, ne02, nb01, nb02, nb03);
-            dma_queue_push(dma_queue,
-                dma_make_ptr(src0_spad, data_src + src0_pref_off),
-                src0_row_size_aligned, nb01, src0_data_row_size, pref_block_size);
+                dma_queue_push(dma_queue,
+                    dma_make_ptr(src0_spad, data_src + src0_pref_off),
+                    src0_row_size_aligned, nb01, src0_data_row_size, pref_block_size);
+
+                if (htp_op == HTP_OP_RMS_NORM_MUL && !uctx->broadcast_weight) {
+                    const size_t src1_pref_off = unary_row_offset(pref_ir, ne01, ne02, nb11, nb12, nb13);
+                    dma_queue_push(dma_queue,
+                        dma_make_ptr(src1_spad, data_src1 + src1_pref_off),
+                        uctx->src1_row_size_aligned, nb11, uctx->src1_data_row_size, pref_block_size);
+                }
            }
        }
        ir += block_size;
@@ -732,6 +862,9 @@ static int execute_op_unary_f32(struct htp_ops_context * octx) {
        case HTP_OP_RMS_NORM:
            op_type = "rmsnorm-f32";
            break;
+        case HTP_OP_RMS_NORM_MUL:
+            op_type = "rmsnorm-mul-f32";
+            break;
        case HTP_OP_SCALE:
            op_type = "scale-f32";
            break;
@@ -777,12 +910,44 @@ static int execute_op_unary_f32(struct htp_ops_context * octx) {
    const size_t src0_row_size_aligned = hex_round_up(src0_data_row_size, VLEN);
    const size_t dst_row_size_aligned  = hex_round_up(dst_data_row_size,  VLEN);

+    size_t src1_data_row_size = 0;
+    size_t src1_row_size_aligned = 0;
+    bool broadcast_weight = false;
+    const struct htp_tensor * src1 = NULL;
+
+    if (octx->op == HTP_OP_RMS_NORM_MUL) {
+        src1 = octx->src[1];
+        src1_data_row_size = src1->ne[0] * sizeof(float);
+        src1_row_size_aligned = hex_round_up(src1_data_row_size, VLEN);
+        broadcast_weight = (src1->ne[1] * src1->ne[2] * src1->ne[3] == 1);
+    }
+
    // VTCM scratchpads for all tensors
    // N rows per thread, padded to HVX vector size
    // Double buffering requires 2x size per buffer

-    size_t spad_size_per_row   = 2 * (src0_row_size_aligned + dst_row_size_aligned);
-    size_t vtcm_row_per_thread = (octx->ctx->vtcm_size)/ (n_threads * spad_size_per_row);
+    size_t spad_size_per_row = 0;
+    size_t vtcm_row_per_thread = 0;
+
+    if (octx->op == HTP_OP_RMS_NORM_MUL) {
+        if (broadcast_weight) {
+            size_t available_vtcm = octx->ctx->vtcm_size;
+            size_t src1_spad_total = n_threads * src1_row_size_aligned;
+            if (available_vtcm > src1_spad_total) {
+                available_vtcm -= src1_spad_total;
+            } else {
+                available_vtcm = 0;
+            }
+            spad_size_per_row = 2 * (src0_row_size_aligned + dst_row_size_aligned);
+            vtcm_row_per_thread = available_vtcm / (n_threads * spad_size_per_row);
+        } else {
+            spad_size_per_row = 2 * (src0_row_size_aligned + dst_row_size_aligned + src1_row_size_aligned);
+            vtcm_row_per_thread = (octx->ctx->vtcm_size) / (n_threads * spad_size_per_row);
+        }
+    } else {
+        spad_size_per_row   = 2 * (src0_row_size_aligned + dst_row_size_aligned);
+        vtcm_row_per_thread = (octx->ctx->vtcm_size)/ (n_threads * spad_size_per_row);
+    }

    // Make sure the reserved vtcm size is sufficient
    if (vtcm_row_per_thread == 0) {
@@ -797,8 +962,29 @@ static int execute_op_unary_f32(struct htp_ops_context * octx) {
    octx->src0_spad.size = n_threads * octx->src0_spad.size_per_thread;
    octx->dst_spad.size  = n_threads * octx->dst_spad.size_per_thread;

+    if (octx->op == HTP_OP_RMS_NORM_MUL) {
+        if (broadcast_weight) {
+            octx->src1_spad.size_per_thread = src1_row_size_aligned;
+        } else {
+            octx->src1_spad.size_per_thread = src1_row_size_aligned * vtcm_row_per_thread * 2;
+        }
+        octx->src1_spad.size = n_threads * octx->src1_spad.size_per_thread;
+    } else {
+        octx->src1_spad.size = 0;
+        octx->src1_spad.size_per_thread = 0;
+    }
+
    octx->src0_spad.data = octx->ctx->vtcm_base;
-    octx->dst_spad.data  = octx->src0_spad.data + octx->src0_spad.size;
+    if (octx->op == HTP_OP_RMS_NORM_MUL) {
+        octx->src1_spad.data = octx->src0_spad.data + octx->src0_spad.size;
+        octx->dst_spad.data  = octx->src1_spad.data + octx->src1_spad.size;
+    } else {
+        octx->dst_spad.data  = octx->src0_spad.data + octx->src0_spad.size;
+    }
+
+    octx->src0_spad.src = NULL;
+    octx->src1_spad.src = NULL;
+    octx->dst_spad.src  = NULL;

    FARF(HIGH, "%s: (%ux%ux%ux%u) -> (%ux%ux%ux%u) : src0-spad-size %u src1-spad-size %u dst-spad-size %u\n", op_type,
         src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3], dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3],
@@ -811,19 +997,24 @@ static int execute_op_unary_f32(struct htp_ops_context * octx) {
            .src0_nrows            = src0_nrows,

            .data_src0             = (const uint8_t *)src0->data,
+            .data_src1             = (octx->op == HTP_OP_RMS_NORM_MUL) ? (const uint8_t *)src1->data : NULL,
            .data_dst              = (uint8_t *)dst->data,

            .src0_data_row_size    = src0_data_row_size,
+            .src1_data_row_size    = src1_data_row_size,
            .dst_data_row_size     = dst_data_row_size,

            .src0_row_size_aligned = src0_row_size_aligned,
+            .src1_row_size_aligned = src1_row_size_aligned,
            .dst_row_size_aligned  = dst_row_size_aligned,

            .src0_spad_half_size   = octx->src0_spad.size_per_thread / 2,
+            .src1_spad_half_size   = (octx->op == HTP_OP_RMS_NORM_MUL) ? (octx->src1_spad.size_per_thread / (broadcast_weight ? 1 : 2)) : 0,
            .dst_spad_half_size    = octx->dst_spad.size_per_thread / 2,

            .block                 = (octx->src0_spad.size_per_thread / 2) / src0_row_size_aligned,
            .nc                    = src0->ne[0],
+            .broadcast_weight      = broadcast_weight,
        };

        worker_pool_run_func(octx->ctx->worker_pool, unary_job_f32_per_thread, &uctx, n_threads);
@@ -1,153 +0,0 @@
-#ifndef OP_DESC_H
-#define OP_DESC_H
-
-#define GGML_COMMON_IMPL_CPP
-#include "ggml-backend-impl.h"
-#include "ggml-common.h"
-
-#include <string>
-#include <stdio.h>
-
-struct op_desc {
-    char strides[64 * GGML_MAX_SRC];
-    char dims[64 * GGML_MAX_SRC];
-    char types[16 * GGML_MAX_SRC];
-    char buffs[64 * GGML_MAX_SRC];
-    char names[64 * GGML_MAX_SRC];
-
-    int format_tensor_dims(char * str, const struct ggml_tensor * t) {
-        if (t->ne[2] == 1 && t->ne[3] == 1) {
-            return sprintf(str, "%d:%d", (int) t->ne[0], (int) t->ne[1]);
-        } else {
-            return sprintf(str, "%d:%d:%d:%d", (int) t->ne[0], (int) t->ne[1], (int) t->ne[2], (int) t->ne[3]);
-        }
-    }
-
-    void format_op_dims(char * str, const struct ggml_tensor * t) {
-        char * p = str;
-
-        // append src0 and src1 (if any)
-        if (t->src[0]) {
-            p += format_tensor_dims(p, t->src[0]);
-
-            for (int i = 1; i < GGML_MAX_SRC && t->src[i]; i++) {
-                p += sprintf(p, " x ");
-                p += format_tensor_dims(p, t->src[i]);
-            }
-
-            p += sprintf(p, " -> ");
-        }
-
-        // format self dims separately for better visual alignment
-        char self[64];
-        format_tensor_dims(self, t);
-
-        p += sprintf(p, "%s", self);
-    }
-
-    int format_tensor_strides(char * str, const struct ggml_tensor * t) {
-        const char * c = ggml_is_contiguous(t) ? "" : "!";
-
-        if (t->ne[2] == 1 && t->ne[3] == 1) {
-            return sprintf(str, "%zu:%zu%s", (size_t) t->nb[0], (size_t) t->nb[1], c);
-        } else {
-            return sprintf(str, "%zu:%zu:%zu:%zu%s", (size_t) t->nb[0], (size_t) t->nb[1], (size_t) t->nb[2], (size_t) t->nb[3], c);
-        }
-    }
-
-    void format_op_strides(char * str, const struct ggml_tensor * t) {
-        char * p = str;
-
-        // append src0 and src1 (if any)
-        if (t->src[0]) {
-            p += format_tensor_strides(p, t->src[0]);
-
-            for (int i = 1; i < GGML_MAX_SRC && t->src[i]; i++) {
-                p += sprintf(p, " x ");
-                p += format_tensor_strides(p, t->src[i]);
-            }
-
-            p += sprintf(p, " -> ");
-        }
-
-        // format self dims separately for better visual alignment
-        char self[64];
-        format_tensor_strides(self, t);
-
-        p += sprintf(p, "%s", self);
-    }
-
-    void format_op_types(char * str, const struct ggml_tensor * t) {
-        char * p = str;
-
-        // append src0 and src1 (if any)
-        if (t->src[0]) {
-            p += sprintf(p, "%s", ggml_type_name(t->src[0]->type));
-
-            for (int i = 1; i < GGML_MAX_SRC && t->src[i]; i++) {
-                p += sprintf(p, " x ");
-                p += sprintf(p, "%s", ggml_type_name(t->src[i]->type));
-            }
-
-            p += sprintf(p, " -> ");
-        }
-
-        p += sprintf(p, "%s", ggml_type_name(t->type));
-    }
-
-    const char * tensor_buff_name(const struct ggml_tensor * t) {
-        if (t->buffer) {
-            return ggml_backend_buffer_name(t->buffer);
-        }
-        return "NONE";
-    }
-
-    void format_op_buffs(char * str, const struct ggml_tensor * t) {
-        char * p = str;
-
-        // append src0 and src1 (if any)
-        if (t->src[0]) {
-            p += sprintf(p, "%s", tensor_buff_name(t->src[0]));
-
-            for (int i = 1; i < GGML_MAX_SRC && t->src[i]; i++) {
-                p += sprintf(p, " x ");
-                p += sprintf(p, "%s", tensor_buff_name(t->src[i]));
-            }
-
-            p += sprintf(p, " -> ");
-        }
-
-        p += sprintf(p, "%s", tensor_buff_name(t));
-    }
-
-    void format_op_names(char * str, const struct ggml_tensor * t) {
-        char * p = str;
-
-        // append src0 and src1 (if any)
-        if (t->src[0]) {
-            p += sprintf(p, "%s", t->src[0]->name);
-
-            for (int i = 1; i < GGML_MAX_SRC && t->src[i]; i++) {
-                p += sprintf(p, " x ");
-                p += sprintf(p, "%s", t->src[i]->name);
-            }
-
-            p += sprintf(p, " -> ");
-        }
-
-        p += sprintf(p, "%s", t->name);
-    }
-
-    void format(const ggml_tensor * op) {
-        format_op_dims(dims, op);
-        format_op_strides(strides, op);
-        format_op_types(types, op);
-        format_op_buffs(buffs, op);
-        format_op_names(names, op);
-    }
-
-    op_desc() {}
-    op_desc(const ggml_tensor * op) { format(op); }
-};
-
-#endif // OP_DESC_H
@@ -1732,6 +1732,8 @@ ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_rope(ggml_metal_
 ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_im2col(ggml_metal_library_t lib, const ggml_tensor * op) {
    assert(op->op == GGML_OP_IM2COL);

+    GGML_TENSOR_LOCALS(int64_t, ne0, op->src[0], ne);
+
    GGML_ASSERT(ggml_is_contiguous(op->src[1]));
    GGML_ASSERT(op->src[1]->type == GGML_TYPE_F32);
    GGML_ASSERT(op->type         == GGML_TYPE_F16 || op->type == GGML_TYPE_F32);
@@ -1739,7 +1741,11 @@ ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_im2col(ggml_meta
    char base[256];
    char name[256];

-    snprintf(base, 256, "kernel_im2col_%s", ggml_type_name(op->type));
+    if (ne00*ne01 <= 1024) {
+        snprintf(base, 256, "kernel_im2col_%s", ggml_type_name(op->type));
+    } else {
+        snprintf(base, 256, "kernel_im2col_ext_%s", ggml_type_name(op->type));
+    }
    snprintf(name, 256, "%s", base);

    ggml_metal_pipeline_with_params res = ggml_metal_library_get_pipeline(lib, name);
@@ -1107,7 +1107,7 @@ bool ggml_metal_device_supports_op(ggml_metal_device_t dev, const struct ggml_te
                case GGML_GLU_OP_SWIGLU_OAI:
                case GGML_GLU_OP_GEGLU_ERF:
                case GGML_GLU_OP_GEGLU_QUICK:
-                    return ggml_is_contiguous_1(op->src[0]) && op->src[0]->type == GGML_TYPE_F32;
+                    return ggml_is_contiguous_1(op->src[0]) && (op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16);
               default:
                    return false;
            }
@@ -3635,16 +3635,26 @@ int ggml_metal_op_im2col(ggml_metal_op_t ctx, int idx) {

    auto pipeline = ggml_metal_library_get_pipeline_im2col(lib, op);

-    GGML_ASSERT(KH*KW <= ggml_metal_pipeline_max_theads_per_threadgroup(pipeline));
+    if (KH*KW <= ggml_metal_pipeline_max_theads_per_threadgroup(pipeline)) {
+        const uint64_t ntptg0 = std::min(ggml_metal_pipeline_max_theads_per_threadgroup(pipeline)/(KH*KW), N);

-    const uint64_t ntptg0 = std::min(ggml_metal_pipeline_max_theads_per_threadgroup(pipeline)/(KH*KW), N);
+        ggml_metal_encoder_set_pipeline(enc, pipeline);
+        ggml_metal_encoder_set_bytes   (enc, &args, sizeof(args), 0);
+        ggml_metal_encoder_set_buffer  (enc, ggml_metal_get_buffer_id(op->src[1]), 1);
+        ggml_metal_encoder_set_buffer  (enc, ggml_metal_get_buffer_id(op),         2);

-    ggml_metal_encoder_set_pipeline(enc, pipeline);
-    ggml_metal_encoder_set_bytes   (enc, &args, sizeof(args), 0);
-    ggml_metal_encoder_set_buffer  (enc, ggml_metal_get_buffer_id(op->src[1]), 1);
-    ggml_metal_encoder_set_buffer  (enc, ggml_metal_get_buffer_id(op),         2);
+        ggml_metal_encoder_dispatch_threadgroups(enc, IC, OH, OW, ntptg0, KH, KW);
+    } else {
+        const uint64_t n_threads = std::min(ggml_metal_pipeline_max_theads_per_threadgroup(pipeline), N);
+        const int64_t  quotient  = N / n_threads + (N % n_threads > 0 ? 1 : 0);

-    ggml_metal_encoder_dispatch_threadgroups(enc, IC, OH, OW, ntptg0, KH, KW);
+        ggml_metal_encoder_set_pipeline(enc, pipeline);
+        ggml_metal_encoder_set_bytes   (enc, &args, sizeof(args), 0);
+        ggml_metal_encoder_set_buffer  (enc, ggml_metal_get_buffer_id(op->src[1]), 1);
+        ggml_metal_encoder_set_buffer  (enc, ggml_metal_get_buffer_id(op),         2);
+
+        ggml_metal_encoder_dispatch_threadgroups(enc, quotient * CHW, OH, OW, n_threads, 1, 1);
+    }

    return 1;
 }
--- a/Show More
+++ b/Show More