cont : rename common_schema to common_chat_schema

cont : restore (min|max)Length to imply string type
cont : make "{}" mean any object for json_object as well
2026-10-27 06:57:35 -05:00 · 2026-09-12 13:23:12 -05:00 · 2026-09-12 13:23:12 -05:00 · 2026-09-12 13:23:12 -05:00 · 2026-09-12 13:23:12 -05:00 · 2026-09-12 13:23:12 -05:00
244 changed files with 7923 additions and 13827 deletions
@@ -1,5 +1,5 @@
-ARG OPENVINO_VERSION_MAJOR=2026.4
-ARG OPENVINO_VERSION_FULL=2026.4.0.22959.99c81491cc3
+ARG OPENVINO_VERSION_MAJOR=2026.3.1
+ARG OPENVINO_VERSION_FULL=2026.3.1.22476.56d9685302d
 ARG UBUNTU_VERSION=24.04

 # Intel GPU driver versions. https://github.com/intel/compute-runtime/releases
@@ -10,9 +10,9 @@ ARG COMPUTE_RUNTIME_VERSION_FULL=26.31.39395.13-0
 ARG IGDGMM_VERSION=22.10.0

 # Intel NPU driver versions. https://github.com/intel/linux-npu-driver/releases
-ARG NPU_DRIVER_VERSION=v1.38.0
-ARG NPU_DRIVER_FULL=v1.38.0.20260910-34487311128
-ARG LIBZE1_VERSION=1.32.0-1~24.04~ppa1
+ARG NPU_DRIVER_VERSION=v1.35.0
+ARG NPU_DRIVER_FULL=v1.35.0.20260722-29947505341
+ARG LIBZE1_VERSION=1.28.2-1~24.04~ppa1

 # Optional proxy build arguments
 ARG http_proxy=
@@ -173,7 +173,7 @@ RUN --mount=type=cache,target=/var/cache/intel-npu,sharing=locked \
    fi; \
    DEB=/var/cache/intel-npu/libze1_${LIBZE1_VERSION}_amd64.deb; \
    if [ ! -f "$DEB" ]; then \
-        wget -q -O "$DEB" https://snapshot.ppa.launchpadcontent.net/kobuk-team/intel-graphics/ubuntu/20260830T100000Z/pool/main/l/level-zero-loader/libze1_${LIBZE1_VERSION}_amd64.deb; \
+        wget -q -O "$DEB" https://snapshot.ppa.launchpadcontent.net/kobuk-team/intel-graphics/ubuntu/20260606T100000Z/pool/main/l/level-zero-loader/libze1_${LIBZE1_VERSION}_amd64.deb; \
    fi; \
    mkdir /tmp/npu/ && cd /tmp/npu/ && tar -xf "$TGZ" && cp "$DEB" .; \
    apt-get update; \
@@ -1,5 +1,5 @@
 {
-  "Exclude": ["^\\.gitmodules$", "stb_image\\.h", "examples/test-cmake/build/", "examples/test-cmake/build-subdir/"],
+  "Exclude": ["^\\.gitmodules$", "stb_image\\.h"],
  "Disable": {
    "IndentSize": true
  }
@@ -14,7 +14,7 @@ runs:
      run: |
        BUILD_NUMBER="$(git rev-list --count HEAD)"
        SHORT_HASH="$(git rev-parse --short=7 HEAD)"
-        if [[ "${{ env.BRANCH_NAME }}" == "master" || "${{ env.BRANCH_NAME }}" == "b${BUILD_NUMBER}" ]]; then
+        if [[ "${{ env.BRANCH_NAME }}" == "master" ]]; then
          echo "name=b${BUILD_NUMBER}" >> $GITHUB_OUTPUT
        else
          SAFE_NAME=$(echo "${{ env.BRANCH_NAME }}" | tr '/' '-')
@@ -100,36 +100,36 @@ runs:
          echo "CUDA_PATH=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.1" | Out-File -FilePath $env:GITHUB_ENV -Append -Encoding utf8
          echo "CUDA_PATH_V13_1=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.1" | Out-File -FilePath $env:GITHUB_ENV -Append -Encoding utf8

-    - name: Install Cuda Toolkit 13.4 for x64
-      if: ${{ inputs.cuda_version == '13.4' && inputs.cuda_arch == 'x64' }}
+    - name: Install Cuda Toolkit 13.3
+      if: ${{ inputs.cuda_version == '13.3' }}
      shell: pwsh
      run: |
-          mkdir -p "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.4"
+          mkdir -p "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.3"
          choco install unzip -y
-          curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_crt/windows-x86_64/cuda_crt-windows-x86_64-13.4.59-archive.zip"
-          curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_cudart/windows-x86_64/cuda_cudart-windows-x86_64-13.4.49-archive.zip"
-          curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_nvcc/windows-x86_64/cuda_nvcc-windows-x86_64-13.4.59-archive.zip"
-          curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_nvrtc/windows-x86_64/cuda_nvrtc-windows-x86_64-13.4.59-archive.zip"
-          curl -O "https://developer.download.nvidia.com/compute/cuda/redist/libcublas/windows-x86_64/libcublas-windows-x86_64-13.7.0.27-archive.zip"
-          curl -O "https://developer.download.nvidia.com/compute/cuda/redist/libnvvm/windows-x86_64/libnvvm-windows-x86_64-13.4.59-archive.zip"
-          curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_nvtx/windows-x86_64/cuda_nvtx-windows-x86_64-13.4.49-archive.zip"
-          curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_profiler_api/windows-x86_64/cuda_profiler_api-windows-x86_64-13.4.49-archive.zip"
-          curl -O "https://developer.download.nvidia.com/compute/cuda/redist/visual_studio_integration/windows-x86_64/visual_studio_integration-windows-x86_64-13.4.49-archive.zip"
-          curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cccl/windows-x86_64/cccl-windows-x86_64-13.3.4.2.1-archive.zip"
-          unzip '*.zip' -d "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.4"
-          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.4\cuda_crt-windows-x86_64-13.4.59-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.4" /E /I /H /Y
-          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.4\cuda_cudart-windows-x86_64-13.4.49-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.4" /E /I /H /Y
-          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.4\cuda_nvcc-windows-x86_64-13.4.59-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.4" /E /I /H /Y
-          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.4\cuda_nvrtc-windows-x86_64-13.4.59-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.4" /E /I /H /Y
-          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.4\libcublas-windows-x86_64-13.7.0.27-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.4" /E /I /H /Y
-          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.4\libnvvm-windows-x86_64-13.4.59-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.4" /E /I /H /Y
-          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.4\cuda_nvtx-windows-x86_64-13.4.49-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.4" /E /I /H /Y
-          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.4\cuda_profiler_api-windows-x86_64-13.4.49-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.4" /E /I /H /Y
-          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.4\visual_studio_integration-windows-x86_64-13.4.49-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.4" /E /I /H /Y
-          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.4\cccl-windows-x86_64-13.3.4.2.1-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.4" /E /I /H /Y
-          echo "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.4\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
-          echo "CUDA_PATH=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.4" | Out-File -FilePath $env:GITHUB_ENV -Append -Encoding utf8
-          echo "CUDA_PATH_V13_4=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.4" | Out-File -FilePath $env:GITHUB_ENV -Append -Encoding utf8
+          curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_crt/windows-x86_64/cuda_crt-windows-x86_64-13.3.33-archive.zip"
+          curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_cudart/windows-x86_64/cuda_cudart-windows-x86_64-13.3.29-archive.zip"
+          curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_nvcc/windows-x86_64/cuda_nvcc-windows-x86_64-13.3.33-archive.zip"
+          curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_nvrtc/windows-x86_64/cuda_nvrtc-windows-x86_64-13.3.33-archive.zip"
+          curl -O "https://developer.download.nvidia.com/compute/cuda/redist/libcublas/windows-x86_64/libcublas-windows-x86_64-13.5.1.27-archive.zip"
+          curl -O "https://developer.download.nvidia.com/compute/cuda/redist/libnvvm/windows-x86_64/libnvvm-windows-x86_64-13.3.33-archive.zip"
+          curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_nvtx/windows-x86_64/cuda_nvtx-windows-x86_64-13.3.29-archive.zip"
+          curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_profiler_api/windows-x86_64/cuda_profiler_api-windows-x86_64-13.3.27-archive.zip"
+          curl -O "https://developer.download.nvidia.com/compute/cuda/redist/visual_studio_integration/windows-x86_64/visual_studio_integration-windows-x86_64-13.3.27-archive.zip"
+          curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cccl/windows-x86_64/cccl-windows-x86_64-13.3.3.3.1-archive.zip"
+          unzip '*.zip' -d "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.3"
+          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.3\cuda_crt-windows-x86_64-13.3.33-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.3" /E /I /H /Y
+          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.3\cuda_cudart-windows-x86_64-13.3.29-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.3" /E /I /H /Y
+          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.3\cuda_nvcc-windows-x86_64-13.3.33-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.3" /E /I /H /Y
+          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.3\cuda_nvrtc-windows-x86_64-13.3.33-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.3" /E /I /H /Y
+          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.3\libcublas-windows-x86_64-13.5.1.27-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.3" /E /I /H /Y
+          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.3\libnvvm-windows-x86_64-13.3.33-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.3" /E /I /H /Y
+          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.3\cuda_nvtx-windows-x86_64-13.3.29-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.3" /E /I /H /Y
+          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.3\cuda_profiler_api-windows-x86_64-13.3.27-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.3" /E /I /H /Y
+          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.3\visual_studio_integration-windows-x86_64-13.3.27-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.3" /E /I /H /Y
+          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.3\cccl-windows-x86_64-13.3.3.3.1-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.3" /E /I /H /Y
+          echo "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.3\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
+          echo "CUDA_PATH=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.3" | Out-File -FilePath $env:GITHUB_ENV -Append -Encoding utf8
+          echo "CUDA_PATH_V13_3=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.3" | Out-File -FilePath $env:GITHUB_ENV -Append -Encoding utf8

    - name: Install Cuda Toolkit 13.4 for ARM64
      if: ${{ inputs.cuda_version == '13.4' && inputs.cuda_arch == 'arm64' }}
@@ -49,7 +49,7 @@ jobs:
          distribution: zulu

      - name: Setup Android SDK
-        uses: android-actions/setup-android@be39fa834029ff78f1a44aa3bb0819b8fc2bd8fd # v4.0.4
+        uses: android-actions/setup-android@40fd30fb8d7440372e1316f5d1809ec01dcd3699 # v4.0.1
        with:
          log-accepted-android-sdk-licenses: false

@@ -123,7 +123,7 @@ jobs:
          distribution: temurin

      - name: Setup Android SDK
-        uses: android-actions/setup-android@be39fa834029ff78f1a44aa3bb0819b8fc2bd8fd # v4.0.4
+        uses: android-actions/setup-android@40fd30fb8d7440372e1316f5d1809ec01dcd3699 # v4.0.1
        with:
          log-accepted-android-sdk-licenses: false

@@ -41,8 +41,8 @@ jobs:

    env:
      # Sync versions in build-openvino.yml, build-self-hosted.yml, release.yml, build-cache.yml, .devops/openvino.Dockerfile
-      OPENVINO_VERSION_MAJOR: "2026.4"
-      OPENVINO_VERSION_FULL: "2026.4.0.22959.99c81491cc3"
+      OPENVINO_VERSION_MAJOR: "2026.3.1"
+      OPENVINO_VERSION_FULL: "2026.3.1.22476.56d9685302d"

    steps:
      - name: Clone
@@ -69,8 +69,8 @@ jobs:

    env:
      # Sync versions in build.yml, build-self-hosted.yml, release.yml, build-cache.yml, .devops/openvino.Dockerfile
-      OPENVINO_VERSION_MAJOR: "2026.4"
-      OPENVINO_VERSION_FULL: "2026.4.0.22959.99c81491cc3"
+      OPENVINO_VERSION_MAJOR: "2026.3.1"
+      OPENVINO_VERSION_FULL: "2026.3.1.22476.56d9685302d"

    steps:
      - name: Clone
@@ -177,8 +177,7 @@ jobs:
        id: cmake_build
        run: |
          cmake -B build -S . \
-            -DGGML_MUSA=ON \
-            -DMUSA_ARCHITECTURES=21
+            -DGGML_MUSA=ON
          time cmake --build build --config Release -j $(nproc)

      - name: ccache-buckets-save
@@ -34,7 +34,7 @@ jobs:
          - cuda: '12.4'
            arch: x64
            defines: '-DGGML_CUDA_CUB_3DOT2=ON'
-          - cuda: '13.4'
+          - cuda: '13.3'
            arch: x64
            defines: ''
          - cuda: '13.4'
@@ -34,14 +34,9 @@ env:
  LLAMA_ARG_LOG_TIMESTAMPS: 1

 jobs:
-  ubuntu-24-s390x:
-    name: ubuntu-24-s390x (VXE ${{ matrix.vxe }})
-    runs-on: ubuntu-24.04-s390x

-    strategy:
-      fail-fast: false
-      matrix:
-        vxe: ["ON", "OFF"]  # `-DGGML_VXE=ON/OFF`
+  ubuntu-24-s390x:
+    runs-on: ubuntu-24.04-s390x

    steps:
      - name: Clone
@@ -82,8 +77,7 @@ jobs:
        run: |
          cmake -B build \
            -DLLAMA_FATAL_WARNINGS=ON \
-            -DGGML_RPC=ON \
-            -DGGML_VXE=${{ matrix.vxe }}
+            -DGGML_RPC=ON
          time cmake --build build --config Release -j $(nproc)

      - name: Test
@@ -41,8 +41,8 @@ jobs:

    env:
      # Sync versions in build-openvino.yml, build-self-hosted.yml, release.yml, build-cache.yml, .devops/openvino.Dockerfile
-      OPENVINO_VERSION_MAJOR: "2026.4"
-      OPENVINO_VERSION_FULL: "2026.4.0.22959.99c81491cc3"
+      OPENVINO_VERSION_MAJOR: "2026.3.1"
+      OPENVINO_VERSION_FULL: "2026.3.1.22476.56d9685302d"

    steps:
      - name: Clone
@@ -96,8 +96,8 @@ jobs:

    env:
      # Sync versions in build-openvino.yml, build-self-hosted.yml, release.yml, build-cache.yml, .devops/openvino.Dockerfile
-      OPENVINO_VERSION_MAJOR: "2026.4"
-      OPENVINO_VERSION_FULL: "2026.4.0.22959.99c81491cc3"
+      OPENVINO_VERSION_MAJOR: "2026.3.1"
+      OPENVINO_VERSION_FULL: "2026.3.1.22476.56d9685302d"

    steps:
      - name: Clone
@@ -7,7 +7,6 @@ on:
      - master
    paths: [
      '.github/workflows/build-self-hosted.yml',
-      'ci/run.sh',
      '**/CMakeLists.txt',
      '**/.cmake',
      '**/*.h',
@@ -28,7 +27,6 @@ on:
    types: [opened, synchronize, reopened]
    paths: [
      '.github/workflows/build-self-hosted.yml',
-      'ci/run.sh',
      '**/CMakeLists.txt',
      '**/.cmake',
      '**/*.h',
@@ -124,7 +122,6 @@ jobs:
          GG_BUILD_ROCM=1 GG_BUILD_AMDGPU_TARGETS=gfx1151 bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp

  gpu-vulkan-nvidia-cm:
-    # runs-on: "hf-jobs-t4-small:ubuntu26_04"
    runs-on: [self-hosted, Linux, NVIDIA]

    steps:
@@ -132,44 +129,13 @@ jobs:
        id: checkout
        uses: actions/checkout@v6

-      # - name: Install dependencies
-      #   run: |
-      #     sudo apt update
-      #     sudo apt install -y build-essential cmake libxcb-xinput0 libxcb-xinerama0 libxcb-cursor-dev libvulkan-dev glslc spirv-headers vulkan-tools mesa-vulkan-drivers libglvnd0 libgl1 libglx0 libegl1 libgles2 libssl-dev time unzip wget python3 python3-venv python3-pip
-
-      # - name: ccache
-      #   uses: ggml-org/ccache-action@v1.2.24
-      #   with:
-      #     restore: false
-      #     save: false
-
-      # - name: ccache-buckets-restore
-      #   uses: ./.github/actions/ccache-buckets
-      #   with:
-      #     key: self-hosted-vulkan-nvidia-cm
-      #     folder: llama.cpp
-      #     hf_bucket: ggml-org/cache
-
      - name: Test
        id: ggml-ci
        run: |
          vulkaninfo --summary
          GG_BUILD_VULKAN=1 GGML_VK_DISABLE_COOPMAT2=1 bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp

-      # - name: ccache-buckets-save
-      #   if: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
-      #   uses: ./.github/actions/ccache-buckets
-      #   env:
-      #     HF_TOKEN: ${{ secrets.HF_TOKEN_CACHE_OUTPUT }}
-      #   with:
-      #     key: self-hosted-vulkan-nvidia-cm
-      #     folder: llama.cpp
-      #     evict-old-files: 1d
-      #     hf_bucket: ggml-org/cache
-      #     save: true
-
  gpu-vulkan-nvidia-cm2:
-    # runs-on: "hf-jobs-t4-small:ubuntu26_04"
    runs-on: [self-hosted, Linux, NVIDIA, COOPMAT2]

    steps:
@@ -177,68 +143,20 @@ jobs:
        id: checkout
        uses: actions/checkout@v6

-      # - name: Install dependencies
-      #   run: |
-      #     sudo apt update
-      #     sudo apt install -y build-essential cmake libxcb-xinput0 libxcb-xinerama0 libxcb-cursor-dev libvulkan-dev glslc spirv-headers vulkan-tools mesa-vulkan-drivers libglvnd0 libgl1 libglx0 libegl1 libgles2 libssl-dev time unzip wget python3 python3-venv python3-pip
-
-      # - name: ccache
-      #   uses: ggml-org/ccache-action@v1.2.24
-      #   with:
-      #     restore: false
-      #     save: false
-
-      # - name: ccache-buckets-restore
-      #   uses: ./.github/actions/ccache-buckets
-      #   with:
-      #     key: self-hosted-vulkan-nvidia-cm2
-      #     folder: llama.cpp
-      #     hf_bucket: ggml-org/cache
-
      - name: Test
        id: ggml-ci
        run: |
          vulkaninfo --summary
          GG_BUILD_VULKAN=1 bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp

-      # - name: ccache-buckets-save
-      #   if: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
-      #   uses: ./.github/actions/ccache-buckets
-      #   env:
-      #     HF_TOKEN: ${{ secrets.HF_TOKEN_CACHE_OUTPUT }}
-      #   with:
-      #     key: self-hosted-vulkan-nvidia-cm2
-      #     folder: llama.cpp
-      #     evict-old-files: 1d
-      #     hf_bucket: ggml-org/cache
-      #     save: true
-
  gpu-webgpu-nvidia:
-    runs-on: "hf-jobs-t4-small:ubuntu26_04"
+    runs-on: [self-hosted, Linux, NVIDIA, X64]

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

-      - name: Install dependencies
-        run: |
-          sudo apt update
-          sudo apt install -y build-essential cmake libxcb-xinput0 libxcb-xinerama0 libxcb-cursor-dev libvulkan1 mesa-vulkan-drivers libglvnd0 libgl1 libglx0 libegl1 libgles2 libssl-dev time unzip wget python3 python3-venv python3-pip
-
-      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.24
-        with:
-          restore: false
-          save: false
-
-      - name: ccache-buckets-restore
-        uses: ./.github/actions/ccache-buckets
-        with:
-          key: self-hosted-webgpu-nvidia
-          folder: llama.cpp
-          hf_bucket: ggml-org/cache
-
      - name: Dawn Dependency
        id: dawn-depends
        run: |
@@ -260,18 +178,6 @@ jobs:
          GG_BUILD_WEBGPU_DAWN_DIR="$GITHUB_WORKSPACE/dawn/lib64/cmake/Dawn" \
            bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp

-      - name: ccache-buckets-save
-        if: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
-        uses: ./.github/actions/ccache-buckets
-        env:
-          HF_TOKEN: ${{ secrets.HF_TOKEN_CACHE_OUTPUT }}
-        with:
-          key: self-hosted-webgpu-nvidia
-          folder: llama.cpp
-          evict-old-files: 1d
-          hf_bucket: ggml-org/cache
-          save: true
-
  # TODO: provision AMX-compatible machine
  #cpu-amx:
  #  runs-on: [self-hosted, Linux, CPU, AMX]
@@ -412,8 +318,8 @@ jobs:

    env:
      # Sync versions in build.yml, build-self-hosted.yml, release.yml, build-cache.yml, .devops/openvino.Dockerfile
-      OPENVINO_VERSION_MAJOR: "2026.4"
-      OPENVINO_VERSION_FULL: "2026.4.0.22959.99c81491cc3"
+      OPENVINO_VERSION_MAJOR: "2026.3.1"
+      OPENVINO_VERSION_FULL: "2026.3.1.22476.56d9685302d"

    steps:
      - name: Clone
@@ -453,7 +359,7 @@ jobs:
          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-arm64-high-perf-graviton4:
-    runs-on: ah-ubuntu_24_04-c8g_8x
+    runs-on: ah-ubuntu_22_04-c8g_8x

    steps:
      - name: Clone
@@ -496,7 +402,7 @@ jobs:
          bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp

  cpu-arm64-graviton4-kleidiai:
-    runs-on: ah-ubuntu_24_04-c8g_8x
+    runs-on: ah-ubuntu_22_04-c8g_8x

    steps:
      - name: Clone
@@ -19,7 +19,7 @@ on:

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

    steps:
      - name: Checkout
@@ -15,7 +15,7 @@ concurrency:

 jobs:
  model-naming:
-    runs-on: ubuntu-slim
+    runs-on: [self-hosted, fast]
    steps:
      - uses: actions/checkout@v6
      - name: Check model naming conventions
@@ -11,12 +11,10 @@ on:
    paths:
      - .github/workflows/copilot-setup-steps.yml

-cache-mode: none
-
 jobs:
  # The job MUST be called `copilot-setup-steps` or it will not be picked up by Copilot.
  copilot-setup-steps:
-    runs-on: ubuntu-24.04
+    runs-on: ubuntu-latest

    # Set the permissions to the lowest permissions possible needed for your steps.
    # Copilot will be given its own token for its operations.
@@ -33,8 +31,8 @@ jobs:
      - name: ccache
        uses: ggml-org/ccache-action@v1.2.24
        with:
-          restore: false
-          save: false
+          key: copilot-setup-steps
+          evict-old-files: 1d

      - name: Dependencies
        id: depends
@@ -15,7 +15,7 @@ concurrency:

 jobs:
  editorconfig:
-    runs-on: ubuntu-slim
+    runs-on: [self-hosted, fast]
    steps:
      - uses: actions/checkout@v6
      - uses: editorconfig-checker/action-editorconfig-checker@840e866d93b8e032123c23bac69dece044d4d84c # v2.2.0
@@ -9,9 +9,7 @@ on:
      '.github/workflows/fusion.yml',
      'ggml/**',
      'tests/fusion/**',
-      'tests/test-fusion.cpp',
-      'tests/test-llama-archs.cpp',
-      'src/models/**'
+      'tests/test-fusion.cpp'
    ]

  pull_request:
@@ -20,9 +18,7 @@ on:
      '.github/workflows/fusion.yml',
      'ggml/**',
      'tests/fusion/**',
-      'tests/test-fusion.cpp',
-      'tests/test-llama-archs.cpp',
-      'src/models/**'
+      'tests/test-fusion.cpp'
    ]

 concurrency:
@@ -13,16 +13,6 @@ on:
        required: true
        type: boolean
        default: true
-      skip_apiabi_check:
-        description: 'Skip API/ABI compatibility check'
-        required: false
-        type: boolean
-        default: false
-      apiabi_compare_tag:
-        description: 'Tag to compare against for API/ABI check (default: latest release)'
-        required: false
-        type: string
-        default: ''

 env:
  GH_TOKEN: ${{ github.token }}
@@ -43,18 +33,12 @@ jobs:
          ref: ${{ inputs.commit != '' && inputs.commit || github.ref_name }}
          fetch-depth: 0

-      - name: Install API/ABI check tools
-        if: ${{ github.event.inputs.skip_apiabi_check != 'true' }}
-        run: sudo apt-get install -y abi-compliance-checker abigail-tools
-
      - name: Run release checks
        id: checks
        run: bash scripts/make-release-checks.sh ${{ github.event.inputs.dry_run == 'true' && '--dry-run' || '' }}
        env:
          GITHUB_REPOSITORY: ${{ github.repository }}
          RELEASE_BRANCH: ${{ github.ref_name }}
-          SKIP_APIABI_CHECK: ${{ github.event.inputs.skip_apiabi_check }}
-          APIABI_COMPARE_TAG: ${{ github.event.inputs.apiabi_compare_tag }}

      - name: Create release tag
        if: ${{ github.event.inputs.dry_run == 'false' }}
@@ -12,7 +12,7 @@ on:

 jobs:
    pre-tokenizer-hashes:
-        runs-on: ubuntu-slim
+        runs-on: [self-hosted, fast]

        steps:
        - name: Checkout repository
@@ -20,7 +20,7 @@ concurrency:

 jobs:
  python-check-requirements:
-    runs-on: ${{ 'ubuntu-24.04-arm' || 'ubuntu-24.04' }}
+    runs-on: [self-hosted, CPU, fast]
    name: check-requirements
    steps:
      - name: Check out source repository
@@ -21,7 +21,7 @@ concurrency:

 jobs:
  flake8-lint:
-    runs-on: ubuntu-slim
+    runs-on: [self-hosted, fast]
    name: Lint
    steps:
      - name: Check out source repository
@@ -22,7 +22,7 @@ concurrency:

 jobs:
  python-type-check:
-    runs-on: ubuntu-slim
+    runs-on: [self-hosted, fast]
    name: python type-check
    steps:
      - name: Check out source repository
@@ -106,7 +106,6 @@ jobs:
        uses: ggml-org/ccache-action@v1.2.24
        with:
          key: release-${{ matrix.os }}-${{ matrix.arch }}
-          evict-old-files: 1d

      - name: Build
        id: cmake_build
@@ -191,7 +190,6 @@ jobs:
        uses: ggml-org/ccache-action@v1.2.24
        with:
          key: release-${{ matrix.os }}-cpu
-          evict-old-files: 1d

      - name: Build
        id: cmake_build
@@ -277,7 +275,6 @@ jobs:
        uses: ggml-org/ccache-action@v1.2.24
        with:
          key: release-${{ matrix.os }}-vulkan
-          evict-old-files: 1d

      - name: Build
        id: cmake_build
@@ -313,145 +310,6 @@ jobs:
        with:
          key: release-${{ matrix.os }}-vulkan

-  ubuntu-cuda:
-    name: ubuntu-cuda (${{ matrix.label }}, ${{ matrix.build }})
-    needs: [check-release, ui-build]
-    if: ${{ needs.check-release.outputs.should_release == 'true' }}
-
-    strategy:
-      matrix:
-        include:
-          # label = short version used in artifact names / release body
-          # cuda  = full container image tag
-          - build: 'x64'
-            os: ubuntu-24.04
-            cuda: '12.8.2'
-            label: '12.8'
-            defines: '-DGGML_CUDA_CUB_3DOT2=ON'
-          - build: 'x64'
-            os: ubuntu-24.04
-            cuda: '13.3.1'
-            label: '13.3'
-            defines: ''
-          - build: 'arm64'
-            os: ubuntu-24.04-arm
-            cuda: '13.3.1'
-            label: '13.3'
-            defines: ''
-
-    runs-on: ${{ matrix.os }}
-    container: nvidia/cuda:${{ matrix.cuda }}-devel-ubuntu24.04
-
-    permissions:
-      actions: write
-
-    steps:
-      # the container has no git; install it before checkout so that a real git
-      # repository is created (the get-tag-name action and the build both need it)
-      - name: Install git
-        run: |
-          apt-get update
-          apt-get install -y --no-install-recommends git
-
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v6
-        with:
-          fetch-depth: 0
-
-      # checkout runs as the host user; in-container steps run as root, so git
-      # refuses to touch a repo it does not own. Mark the workspace as safe.
-      # use the env var: the github.workspace context holds the HOST path,
-      # GITHUB_WORKSPACE the container path
-      - name: Git safe directory
-        run: git config --global --add safe.directory "$GITHUB_WORKSPACE"
-
-      - name: Download UI build
-        uses: actions/download-artifact@v7
-        with:
-          name: llama-ui.zip
-          path: tools/ui/dist
-
-      - name: Dependencies
-        id: depends
-        # container jobs default to sh (dash); need bash for the [[ ]] below
-        shell: bash
-        run: |
-          apt-get update
-          apt-get install -y --no-install-recommends build-essential cmake ninja-build libssl-dev jq python3-venv
-          # the container ships GCC 13, which does not know the 'sme' march
-          # feature used by the armv9.2 CPU variant of GGML_CPU_ALL_VARIANTS
-          if [[ "${{ matrix.build }}" == "arm64" ]]; then
-            apt-get install -y --no-install-recommends gcc-14 g++-14
-            echo "CC=gcc-14" >> "$GITHUB_ENV"
-            echo "CXX=g++-14" >> "$GITHUB_ENV"
-          fi
-
-      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.24
-        with:
-          key: release-ubuntu-${{ matrix.os }}-cuda-${{ matrix.label }}-${{ matrix.build }}
-          evict-old-files: 1d
-          max-size: "1G"
-
-      - name: Build
-        id: cmake_build
-        # no CMAKE_CUDA_ARCHITECTURES: use the broad default arch set from
-        # ggml/src/ggml-cuda/CMakeLists.txt so the release binary covers many GPUs
-        run: |
-          cmake -B build \
-            -DCMAKE_INSTALL_RPATH='$ORIGIN' \
-            -DCMAKE_BUILD_WITH_INSTALL_RPATH=ON \
-            -DGGML_BACKEND_DL=ON \
-            -DGGML_NATIVE=OFF \
-            -DGGML_CPU_ALL_VARIANTS=ON \
-            -DGGML_CUDA=ON \
-            -DGGML_CUDA_NCCL=OFF \
-            ${{ env.CMAKE_ARGS }} ${{ matrix.defines }}
-          cmake --build build --config Release -j $(nproc)
-
-      - name: Determine tag name
-        id: tag
-        uses: ./.github/actions/get-tag-name
-
-      - name: Pack artifacts
-        id: pack_artifacts
-        run: |
-          cp LICENSE ./build/bin/
-          tar -czvf llama-${{ steps.tag.outputs.name }}-bin-ubuntu-cuda-${{ matrix.label }}-${{ matrix.build }}.tar.gz --transform "s,^\.,llama-${{ steps.tag.outputs.name }}," -C ./build/bin .
-
-      - name: Upload artifacts
-        uses: actions/upload-artifact@v6
-        with:
-          path: llama-${{ steps.tag.outputs.name }}-bin-ubuntu-cuda-${{ matrix.label }}-${{ matrix.build }}.tar.gz
-          name: llama-bin-ubuntu-cuda-${{ matrix.label }}-${{ matrix.build }}.tar.gz
-
-      # ship the CUDA runtime libraries the backend links against, mirroring
-      # the windows-cuda cudart zip - extract next to the binaries ($ORIGIN rpath)
-      - name: Pack CUDA runtime
-        id: pack_cuda_runtime
-        run: |
-          major="${{ matrix.label }}"
-          major="${major%%.*}"
-          mkdir -p ./cudart
-          # cp -L dereferences the SONAME symlinks into plain files, so the
-          # tarball holds exactly 3 files with no versioned duplicates
-          cp -L /usr/local/cuda/lib64/libcudart.so.${major} ./cudart/
-          cp -L /usr/local/cuda/lib64/libcublas.so.${major} ./cudart/
-          cp -L /usr/local/cuda/lib64/libcublasLt.so.${major} ./cudart/
-          tar -czvf cudart-llama-${{ steps.tag.outputs.name }}-bin-ubuntu-cuda-${{ matrix.label }}-${{ matrix.build }}.tar.gz --transform "s,^\.,cudart-llama-${{ steps.tag.outputs.name }}-bin-ubuntu-cuda-${{ matrix.label }}-${{ matrix.build }}," -C ./cudart .
-
-      - name: Upload CUDA runtime
-        uses: actions/upload-artifact@v6
-        with:
-          path: cudart-llama-${{ steps.tag.outputs.name }}-bin-ubuntu-cuda-${{ matrix.label }}-${{ matrix.build }}.tar.gz
-          name: cudart-llama-bin-ubuntu-cuda-${{ matrix.label }}-${{ matrix.build }}.tar.gz
-
-      - name: ccache-clear
-        uses: ./.github/actions/ccache-clear
-        with:
-          key: release-ubuntu-${{ matrix.os }}-cuda-${{ matrix.label }}-${{ matrix.build }}
-
  android-arm64:
    needs: [check-release, ui-build]
    if: ${{ needs.check-release.outputs.should_release == 'true' }}
@@ -484,7 +342,7 @@ jobs:
          distribution: temurin

      - name: Setup Android SDK
-        uses: android-actions/setup-android@be39fa834029ff78f1a44aa3bb0819b8fc2bd8fd # v4.0.4
+        uses: android-actions/setup-android@40fd30fb8d7440372e1316f5d1809ec01dcd3699 # v4.0.1
        with:
          log-accepted-android-sdk-licenses: false

@@ -503,7 +361,6 @@ jobs:
      #  uses: ggml-org/ccache-action@v1.2.24
      #  with:
      #    key: release-android-arm64
-      #    evict-old-files: 1d

      - name: Build
        id: cmake_build
@@ -558,8 +415,8 @@ jobs:

    env:
      # Sync versions in build-openvino.yml, build-self-hosted.yml, release.yml, build-cache.yml, .devops/openvino.Dockerfile
-      OPENVINO_VERSION_MAJOR: "2026.4"
-      OPENVINO_VERSION_FULL: "2026.4.0.22959.99c81491cc3"
+      OPENVINO_VERSION_MAJOR: "2026.3.1"
+      OPENVINO_VERSION_FULL: "2026.3.1.22476.56d9685302d"

    steps:
      - name: Set OpenVINO version output
@@ -582,7 +439,6 @@ jobs:
        uses: ggml-org/ccache-action@v1.2.24
        with:
          key: release-ubuntu-24.04-openvino-release-no-preset-v1
-          evict-old-files: 1d

      - name: Dependencies
        run: |
@@ -673,8 +529,8 @@ jobs:

    env:
      # Sync versions in build-openvino.yml, build-self-hosted.yml, release.yml, build-cache.yml, .devops/openvino.Dockerfile
-      OPENVINO_VERSION_MAJOR: "2026.4"
-      OPENVINO_VERSION_FULL: "2026.4.0.22959.99c81491cc3"
+      OPENVINO_VERSION_MAJOR: "2026.3.1"
+      OPENVINO_VERSION_FULL: "2026.3.1.22476.56d9685302d"

    steps:
      - name: Set OpenVINO version output
@@ -826,7 +682,6 @@ jobs:
        uses: ggml-org/ccache-action@v1.2.24
        with:
          key: release-windows-2025-vs2026-${{ matrix.arch }}-cpu
-          evict-old-files: 1d

      - name: Build
        shell: cmd
@@ -1071,7 +926,6 @@ jobs:
      #  uses: ggml-org/ccache-action@v1.2.24
      #  with:
      #    key: release-windows-2025-${{ matrix.arch }}-${{ matrix.backend }}
-      #    evict-old-files: 1d

      - name: Install OpenCL Headers and Libs
        id: install_opencl
@@ -1133,7 +987,7 @@ jobs:
          - cuda: '12.4'
            arch: x64
            defines: '-DGGML_CUDA_CUB_3DOT2=ON'
-          - cuda: '13.4'
+          - cuda: '13.3'
            arch: x64
            defines: ''
          - cuda: '13.4'
@@ -1160,7 +1014,6 @@ jobs:
        uses: ggml-org/ccache-action@v1.2.24
        with:
          key: release-windows-2022-${{ matrix.arch }}-cuda-${{ matrix.cuda }}
-          evict-old-files: 1d

      - name: Build
        id: cmake_build
@@ -1257,7 +1110,6 @@ jobs:
        uses: ggml-org/ccache-action@v1.2.24
        with:
          key: release-windows-2022-x64-sycl
-          evict-old-files: 1d

      - name: Build
        id: cmake_build
@@ -1376,7 +1228,6 @@ jobs:
        uses: ggml-org/ccache-action@v1.2.24
        with:
          key: release-ubuntu-24.04-sycl-${{ matrix.build }}
-          evict-old-files: 1d

      - name: Build
        id: cmake_build
@@ -1429,7 +1280,7 @@ jobs:
      matrix:
        include:
          - ROCM_VERSION: "10.0.0"
-            gpu_targets: "gfx908;gfx90a;gfx942;gfx950;gfx1010;gfx1011;gfx1012;gfx1030;gfx1031;gfx1032;gfx1033;gfx1034;gfx1035;gfx1036;gfx1100;gfx1101;gfx1102;gfx1103;gfx1150;gfx1151;gfx1152;gfx1200;gfx1201"
+            gpu_targets: "gfx908;gfx90a;gfx942;gfx950;gfx1010;gfx1011;gfx1012;gfx1030;gfx1031;gfx1032;gfx1033;gfx1034;gfx1035;gfx1036;gfx1100;gfx1101;gfx1102;gfx1150;gfx1151;gfx1152;gfx1200;gfx1201"
            build: 'x64'

    steps:
@@ -1721,7 +1572,6 @@ jobs:
      - ubuntu-24-rocm
      - ubuntu-cpu
      - ubuntu-vulkan
-      - ubuntu-cuda
      - ubuntu-24-openvino
      - ubuntu-24-sycl
      - android-arm64
@@ -1853,9 +1703,6 @@ jobs:
            - [Ubuntu s390x (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-ubuntu-s390x.tar.gz)
            - [Ubuntu x64 (Vulkan)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-ubuntu-vulkan-x64.tar.gz)
            - [Ubuntu arm64 (Vulkan)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-ubuntu-vulkan-arm64.tar.gz)
-            - [Ubuntu x64 (CUDA 12)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-ubuntu-cuda-12.8-x64.tar.gz) - [CUDA 12.8 libraries](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/cudart-llama-${{ steps.tag.outputs.name }}-bin-ubuntu-cuda-12.8-x64.tar.gz)
-            - [Ubuntu x64 (CUDA 13)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-ubuntu-cuda-13.3-x64.tar.gz) - [CUDA 13.3 libraries](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/cudart-llama-${{ steps.tag.outputs.name }}-bin-ubuntu-cuda-13.3-x64.tar.gz)
-            - [Ubuntu arm64 (CUDA 13)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-ubuntu-cuda-13.3-arm64.tar.gz) - [CUDA 13.3 libraries](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/cudart-llama-${{ steps.tag.outputs.name }}-bin-ubuntu-cuda-13.3-arm64.tar.gz)
            - [Ubuntu x64 (ROCm 10.0)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-ubuntu-rocm-10.0-x64.tar.gz)
            - [Ubuntu x64 (OpenVINO)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-ubuntu-openvino-${{ needs.ubuntu-24-openvino.outputs.openvino_version }}-x64.tar.gz)
            - [Ubuntu x64 (SYCL FP32)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-ubuntu-sycl-fp32-x64.tar.gz)
@@ -1869,7 +1716,7 @@ jobs:
            - [Windows arm64 (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-cpu-arm64.zip)
            - [Windows arm64 (OpenCL Adreno)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-opencl-adreno-arm64.zip)
            - [Windows x64 (CUDA 12)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-cuda-12.4-x64.zip) - [CUDA 12.4 DLLs](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/cudart-llama-bin-win-cuda-12.4-x64.zip)
-            - [Windows x64 (CUDA 13)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-cuda-13.4-x64.zip) - [CUDA 13.4 DLLs](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/cudart-llama-bin-win-cuda-13.4-x64.zip)
+            - [Windows x64 (CUDA 13)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-cuda-13.3-x64.zip) - [CUDA 13.3 DLLs](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/cudart-llama-bin-win-cuda-13.3-x64.zip)
            - [Windows arm64 (CUDA 13)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-cuda-13.4-arm64.zip) - [CUDA 13.4 DLLs](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/cudart-llama-bin-win-cuda-13.4-arm64.zip)
            - [Windows x64 (Vulkan)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-vulkan-x64.zip)
            - [Windows x64 (OpenVINO)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-openvino-${{ needs.windows-openvino.outputs.openvino_version }}-x64.zip)
@@ -116,6 +116,7 @@ jobs:
        run: |
          source .venv/bin/activate
          cd tools/server/tests
+          export ${{ matrix.extra_args }}
          PYTEST_WORKERS=1 ./tests.sh

      - name: Slow tests
@@ -124,4 +125,5 @@ jobs:
        run: |
          source .venv/bin/activate
          cd tools/server/tests
+          export ${{ matrix.extra_args }}
          PYTEST_WORKERS=1 SLOW_TESTS=1 ./tests.sh
@@ -192,7 +192,7 @@ jobs:
          PYTEST_WORKERS=1 ./tests.sh

  server-kleidiai:
-    runs-on: ah-ubuntu_24_04-c8g_8x
+    runs-on: ah-ubuntu_22_04-c8g_8x

    steps:
      - name: Clone
@@ -232,7 +232,7 @@ jobs:
      - name: Build
        id: cmake_build
        run: |
-          cmake -B build -DGGML_SCHED_NO_REALLOC=ON -DGGML_CPU_KLEIDIAI=ON -DLLAMA_FATAL_WARNINGS=ON
+          cmake -B build -DGGML_SCHED_NO_REALLOC=ON -DGGML_CPU_KLEIDIAI=ON
          cmake --build build --config Release -j $(nproc) --target llama-server

      - name: Python setup
@@ -16,7 +16,7 @@ on:

 jobs:
    update-ops-docs:
-        runs-on: ubuntu-slim
+        runs-on: [self-hosted, fast, ARM64]

        steps:
        - name: Checkout repository
@@ -6,7 +6,6 @@ General:
 - PR and commit titles format: `<module> : <title>`. Lookup recents for examples
 - Don't try to build or run the code unless you are explicitly asked to do so
 - Use the `gh` CLI tool when querying PRs, issues, or other GitHub resources
- When [MODEL] is needed, first try to get it from the `PI_MODEL_NAME` env var before asking the user

 Coding:
 - When in doubt, always refer to the CONTRIBUTING.md file of the project
@@ -21,9 +20,8 @@ Pull requests (PRs):
 - Don't explicitly wrap lines in the PR description (each paragraph and bullet is a single line)
 - When creating a pull request, look for the repository's PR template and follow it
 - For the AI usage disclosure section, write "YES. pi:llama.cpp/[MODEL]"
- If `PI_MODEL_NAME` env var is not set, ask the user to tell you what model was used and write it in place of [MODEL]
+- Ask the user to tell you what model was used and write it in place of [MODEL]
 - Always create the pull requests in draft mode
- Never reply to review comments or post comments on issues/PRs without explicit permission from the user

 Commits:
 - On every commit that you make, include a "Assisted-by: pi:llama.cpp/[MODEL]" tag
@@ -5,7 +5,7 @@ include(CheckIncludeFileCXX)
 ### llama.cpp version
 set(LLAMA_VERSION_MAJOR 0)
 set(LLAMA_VERSION_MINOR 4)
-set(LLAMA_VERSION_PATCH 1)
+set(LLAMA_VERSION_PATCH 0)
 set(LLAMA_VERSION_BASE "${LLAMA_VERSION_MAJOR}.${LLAMA_VERSION_MINOR}.${LLAMA_VERSION_PATCH}")

 # whether this is a development/nightly build
@@ -197,16 +197,6 @@ llama_option_depr(WARNING     LLAMA_CURL)
 include("cmake/license.cmake")
 license_add_file("llama.cpp" "LICENSE")

-#
-# compile options
-#
-
-# clang stores the modification time of the precompiled header sources inside the
-# header and rejects it when they differ, so the timestamp is left out of it
-add_compile_options(
-    "$<$<COMPILE_LANG_AND_ID:C,Clang,IntelLLVM>:SHELL:-Xclang -fno-pch-timestamp>"
-    "$<$<COMPILE_LANG_AND_ID:CXX,Clang,IntelLLVM>:SHELL:-Xclang -fno-pch-timestamp>")
-
 #
 # 3rd-party
 #
@@ -97,6 +97,7 @@
 /src/models/                            @CISC
 /tests/                                 @ggerganov
 /tests/test-chat.*                      @pwilkin
+/tests/test-llama-archs.cpp             @JohannesGaessler
 /tools/batched-bench/                   @ggerganov
 /tools/cli/                             @ngxson
 /tools/completion/                      @ggerganov
@@ -20,8 +20,8 @@ If AI is used to generate any portion of the code, contributors must adhere to t

 1. Explicitly disclose the manner in which AI was employed.
 2. Check for an existing PR addressing the same change; if one exists, comment there to work with its author instead of opening a duplicate.
-3. Perform a comprehensive manual review prior to submitting the pull request. A proper code review usually takes something like one hour per 200-400 LOC and you should be spending **at least that much time on code review alone**.
-4. Be prepared to explain every line of code you submit when asked about it by a maintainer.
+3. Perform a comprehensive manual review prior to submitting the pull request.
+4. Be prepared to explain every line of code they submitted when asked about it by a maintainer.
 5. It is strictly prohibited to use AI to write your posts for you (bug reports, feature requests, pull request descriptions, Github discussions, responding to humans, ...).

 For more info, please refer to the [AGENTS.md](AGENTS.md) file.
@@ -16,7 +16,7 @@ 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 "${PROJECT_SOURCE_DIR}/licenses/LICENSE-*")
+file(GLOB EXTRA_LICENSES "${CMAKE_SOURCE_DIR}/licenses/LICENSE-*")

 foreach(FILE_PATH ${EXTRA_LICENSES})
    get_filename_component(FILE_NAME "${FILE_PATH}" NAME)
@@ -58,6 +58,8 @@ if [ -n "${GG_BUILD_ROCM}" ] && [ -n "${GITHUB_RUN_ID}" ]; then
 fi

 rm -f $OUT/*.log
+rm -f $OUT/*.exit
+rm -f $OUT/*.md

 sd=`dirname $0`
 cd $sd/../
@@ -209,6 +211,10 @@ function gg_wget {
    cd $cwd
 }

+function gg_printf {
+    printf -- "$@" >> $OUT/README.md
+}
+
 function gg_run {
    ci=$1

@@ -217,10 +223,13 @@ function gg_run {

    gg_run_$ci | tee $OUT/$ci.log
    cur=$?
+    echo "$cur" > $OUT/$ci.exit

    set +x
    set +o pipefail

+    gg_sum_$ci
+
    ret=$((ret | cur))
 }

@@ -246,6 +255,17 @@ function gg_run_ctest_debug {
    set +e
 }

+function gg_sum_ctest_debug {
+    gg_printf '### %s\n\n' "${ci}"
+
+    gg_printf 'Runs ctest in debug mode\n'
+    gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)"
+    gg_printf '```\n'
+    gg_printf '%s\n' "$(cat $OUT/${ci}-ctest.log)"
+    gg_printf '```\n'
+    gg_printf '\n'
+}
+
 # ctest_release

 function gg_run_ctest_release {
@@ -270,6 +290,16 @@ function gg_run_ctest_release {
    set +e
 }

+function gg_sum_ctest_release {
+    gg_printf '### %s\n\n' "${ci}"
+
+    gg_printf 'Runs ctest in release mode\n'
+    gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)"
+    gg_printf '```\n'
+    gg_printf '%s\n' "$(cat $OUT/${ci}-ctest.log)"
+    gg_printf '```\n'
+}
+
 # test_llama_archs_tensor_split

 function gg_run_test_llama_archs_tensor_split {
@@ -294,6 +324,16 @@ function gg_run_test_llama_archs_tensor_split {
    set +e
 }

+function gg_sum_test_llama_archs_tensor_split {
+    gg_printf '### %s\n\n' "${ci}"
+
+    gg_printf 'Runs test-llama-archs with 1 to 4 devices\n'
+    gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)"
+    gg_printf '```\n'
+    gg_printf '%s\n' "$(cat $OUT/${ci}.log)"
+    gg_printf '```\n'
+}
+
 # test_llama_archs_models

 function gg_run_test_llama_archs_models {
@@ -313,6 +353,16 @@ function gg_run_test_llama_archs_models {
    set +e
 }

+function gg_sum_test_llama_archs_models {
+    gg_printf '### %s\n\n' "${ci}"
+
+    gg_printf 'Generates the dummy models used by the model-dependent tests\n'
+    gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)"
+    gg_printf '```\n'
+    gg_printf '%s\n' "$(cat $OUT/${ci}.log)"
+    gg_printf '```\n'
+}
+
 # test_scripts

 function gg_run_test_scripts {
@@ -326,6 +376,17 @@ function gg_run_test_scripts {
    set +e
 }

+function gg_sum_test_scripts {
+    gg_printf '### %s\n\n' "${ci}"
+
+    gg_printf 'Runs test scripts\n'
+    gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)"
+    gg_printf '```\n'
+    gg_printf '%s\n' "$(cat $OUT/${ci}-scripts.log)"
+    gg_printf '```\n'
+    gg_printf '\n'
+}
+
 function gg_get_model {
    #local gguf_0="$MNT/models/qwen3/0.6B/ggml-model-f16.gguf"
    local gguf_0="$MNT/models/qwen3/0.6B/ggml-model-q4_0.gguf"
@@ -369,6 +430,26 @@ function gg_run_ctest_with_model_release {
    cd ..
 }

+function gg_sum_ctest_with_model_debug {
+    gg_printf '### %s\n\n' "${ci}"
+
+    gg_printf 'Runs ctest with model files in debug mode\n'
+    gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)"
+    gg_printf '```\n'
+    gg_printf '%s\n' "$(cat $OUT/${ci}-ctest.log)"
+    gg_printf '```\n'
+}
+
+function gg_sum_ctest_with_model_release {
+    gg_printf '### %s\n\n' "${ci}"
+
+    gg_printf 'Runs ctest with model files in release mode\n'
+    gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)"
+    gg_printf '```\n'
+    gg_printf '%s\n' "$(cat $OUT/${ci}-ctest.log)"
+    gg_printf '```\n'
+}
+
 # qwen3_0_6b

 function gg_run_qwen3_0_6b {
@@ -473,24 +554,50 @@ function gg_run_qwen3_0_6b {
        return 0
    }

-    check_ppl "f16"  "$(cat $OUT/${ci}-tg-f16.log  | grep "^\[1\]")"
+    check_ppl "f16"  "$(cat $OUT/${ci}-tg-f16.log  | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
    if [ -z ${GG_BUILD_NO_BF16} ]; then
-        check_ppl "bf16" "$(cat $OUT/${ci}-tg-bf16.log | grep "^\[1\]")"
+        check_ppl "bf16" "$(cat $OUT/${ci}-tg-bf16.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
    fi
-    check_ppl "q8_0" "$(cat $OUT/${ci}-tg-q8_0.log | grep "^\[1\]")"
-    check_ppl "q4_0" "$(cat $OUT/${ci}-tg-q4_0.log | grep "^\[1\]")"
-    check_ppl "q4_1" "$(cat $OUT/${ci}-tg-q4_1.log | grep "^\[1\]")"
-    check_ppl "q5_0" "$(cat $OUT/${ci}-tg-q5_0.log | grep "^\[1\]")"
-    check_ppl "q5_1" "$(cat $OUT/${ci}-tg-q5_1.log | grep "^\[1\]")"
-   #check_ppl "q2_k" "$(cat $OUT/${ci}-tg-q2_k.log | grep "^\[1\]")" # note: ppl > 20.0 for this quant and model
-    check_ppl "q3_k" "$(cat $OUT/${ci}-tg-q3_k.log | grep "^\[1\]")"
-    check_ppl "q4_k" "$(cat $OUT/${ci}-tg-q4_k.log | grep "^\[1\]")"
-    check_ppl "q5_k" "$(cat $OUT/${ci}-tg-q5_k.log | grep "^\[1\]")"
-    check_ppl "q6_k" "$(cat $OUT/${ci}-tg-q6_k.log | grep "^\[1\]")"
+    check_ppl "q8_0" "$(cat $OUT/${ci}-tg-q8_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
+    check_ppl "q4_0" "$(cat $OUT/${ci}-tg-q4_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
+    check_ppl "q4_1" "$(cat $OUT/${ci}-tg-q4_1.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
+    check_ppl "q5_0" "$(cat $OUT/${ci}-tg-q5_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
+    check_ppl "q5_1" "$(cat $OUT/${ci}-tg-q5_1.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
+   #check_ppl "q2_k" "$(cat $OUT/${ci}-tg-q2_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log # note: ppl > 20.0 for this quant and model
+    check_ppl "q3_k" "$(cat $OUT/${ci}-tg-q3_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
+    check_ppl "q4_k" "$(cat $OUT/${ci}-tg-q4_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
+    check_ppl "q5_k" "$(cat $OUT/${ci}-tg-q5_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
+    check_ppl "q6_k" "$(cat $OUT/${ci}-tg-q6_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
+
+    cat $OUT/${ci}-imatrix.log | grep "Final" >> $OUT/${ci}-imatrix-sum.log

    set +e
 }

+function gg_sum_qwen3_0_6b {
+    gg_printf '### %s\n\n' "${ci}"
+
+    gg_printf 'Qwen3 0.6B:\n'
+    gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)"
+    gg_printf '- perplexity:\n%s\n' "$(cat $OUT/${ci}-ppl.log)"
+    gg_printf '- imatrix:\n```\n%s\n```\n' "$(cat $OUT/${ci}-imatrix-sum.log)"
+    gg_printf '- f16:\n```\n%s\n```\n'  "$(cat $OUT/${ci}-tg-f16.log)"
+    if [ -z ${GG_BUILD_NO_BF16} ]; then
+        gg_printf '- bf16:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-bf16.log)"
+    fi
+    gg_printf '- q8_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q8_0.log)"
+    gg_printf '- q4_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_0.log)"
+    gg_printf '- q4_1:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_1.log)"
+    gg_printf '- q5_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_0.log)"
+    gg_printf '- q5_1:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_1.log)"
+    gg_printf '- q2_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q2_k.log)"
+    gg_printf '- q3_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q3_k.log)"
+    gg_printf '- q4_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_k.log)"
+    gg_printf '- q5_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_k.log)"
+    gg_printf '- q6_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q6_k.log)"
+    gg_printf '- save-load-state: \n```\n%s\n```\n' "$(cat $OUT/${ci}-save-load-state.log)"
+}
+
 # bge-small

 function gg_run_embd_bge_small {
@@ -532,6 +639,15 @@ function gg_run_embd_bge_small {
    set +e
 }

+function gg_sum_embd_bge_small {
+    gg_printf '### %s\n\n' "${ci}"
+
+    gg_printf 'BGE Small (BERT):\n'
+    gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)"
+    gg_printf '- f16: \n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-f16.log)"
+    gg_printf '- q8_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q8_0.log)"
+}
+
 # rerank_tiny

 function gg_run_rerank_tiny {
@@ -588,58 +704,66 @@ function gg_run_rerank_tiny {
    set +e
 }

+function gg_sum_rerank_tiny {
+    gg_printf '### %s\n\n' "${ci}"
+
+    gg_printf 'Rerank Tiny (Jina):\n'
+    gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)"
+    gg_printf '- f16: \n```\n%s\n```\n' "$(cat $OUT/${ci}-rk-f16.log)"
+}
+
 function gg_check_build_requirements {
    if ! command -v git &> /dev/null; then
-        echo 'git not found, please install'
+        gg_printf 'git not found, please install\n'
        exit 1
    fi

    if ! command -v git-lfs &> /dev/null; then
-        echo 'git-lfs not found, please install'
+        gg_printf 'git-lfs not found, please install\n'
        exit 1
    fi

    if ! git config --get filter.lfs.clean &> /dev/null; then
-        echo 'git-lfs not initialized, please run `git lfs install`'
+        gg_printf 'git-lfs not initialized, please run `git lfs install`\n'
        exit 1
    fi

    if ! command -v wget &> /dev/null; then
-        echo 'wget not found, please install'
+        gg_printf 'wget not found, please install\n'
        exit 1
    fi

    if ! command -v python3 &> /dev/null; then
-        echo 'python3 not found, please install'
+        gg_printf 'python3 not found, please install\n'
        exit 1
    fi

    if ! command -v pip3 &> /dev/null; then
-        echo 'pip3 not found, please install'
+        gg_printf 'pip3 not found, please install\n'
        exit 1
    fi

    if ! python3 -m ensurepip --help &> /dev/null; then
-        echo 'ensurepip not found, please install python3-venv package'
+        gg_printf 'ensurepip not found, please install python3-venv package\n'
        exit 1
    fi

    if ! command -v cmake &> /dev/null; then
-        echo 'cmake not found, please install'
+        gg_printf 'cmake not found, please install\n'
        exit 1
    fi

    if ! command -v ccache &> /dev/null; then
-        echo 'ccache not found, please consider installing for faster builds'
+        gg_printf 'ccache not found, please consider installing for faster builds\n'
    fi

    if ! command -v ctest &> /dev/null; then
-        echo 'ctest not found, please install'
+        gg_printf 'ctest not found, please install\n'
        exit 1
    fi

    if ! command -v unzip &> /dev/null; then
-        echo 'unzip not found, please install'
+        gg_printf 'unzip not found, please install\n'
        exit 1
    fi
 }
@@ -651,11 +775,7 @@ function gg_run_test_backend_ops {

    set -e

-    local n_jobs=$(nproc)
-    if [ "${n_jobs}" -gt 2 ]; then
-        n_jobs=2
-    fi
-    local args_extra="-j ${n_jobs}"
+    local args_extra="-j $(nproc)"

    # TODO: fix multi-threaded for ROCm
    #       https://github.com/ggml-org/llama.cpp/actions/runs/34576278519/job/103297889044?pr=28740#step:3:4865
@@ -669,21 +789,26 @@ function gg_run_test_backend_ops {
        args_extra=""
    fi

-    # TODO: OpenVINO GPU plugin crashes (CL_OUT_OF_RESOURCES) with 2 concurrent workers on GPU.
-    if [ ! -z "${GG_BUILD_OPENVINO}" ] && [ "${GGML_OPENVINO_DEVICE:-}" = "GPU" ]; then
-        args_extra=""
-    fi
-
-    # TODO: reduce the test-backend-ops timeout to 1800s
    if [ ! -z ${GG_BUILD_HIGH_PERF} ]; then
-        (time timeout 3600 ./bin/test-backend-ops ${args_extra} -b CPU) 2>&1 | tee -a $OUT/${ci}-test-backend-ops.log
+        (time ./bin/test-backend-ops ${args_extra} -b CPU) 2>&1 | tee -a $OUT/${ci}-test-backend-ops.log
    else
-        (time timeout 3600 ./bin/test-backend-ops ${args_extra}       ) 2>&1 | tee -a $OUT/${ci}-test-backend-ops.log
+        (time ./bin/test-backend-ops ${args_extra}       ) 2>&1 | tee -a $OUT/${ci}-test-backend-ops.log
    fi

    set +e
 }

+function gg_sum_test_backend_ops {
+    gg_printf '### %s\n\n' "${ci}"
+
+    gg_printf 'Runs test-backend-ops\n'
+    gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)"
+    gg_printf '```\n'
+    gg_printf '%s\n' "$(cat $OUT/${ci}-test-backend-ops.log)"
+    gg_printf '```\n'
+    gg_printf '\n'
+}
+
 ## main

 export LLAMA_ARG_LOG_PREFIX=1
@@ -731,4 +856,6 @@ if [ -z ${GG_BUILD_LOW_PERF} ]; then
    test $ret -eq 0 && gg_run ctest_with_model_release
 fi

+cat $OUT/README.md
+
 exit $ret
@@ -136,6 +136,8 @@ set_target_properties(${TARGET} PROPERTIES
 target_include_directories(${TARGET} PUBLIC .)
 target_link_libraries     (${TARGET} PUBLIC vendor::nlohmann vendor::sheredom)
 target_compile_features   (${TARGET} PUBLIC cxx_std_17)
+target_precompile_headers (${TARGET} PRIVATE common.h)
+target_precompile_headers (${TARGET} PRIVATE chat.h)

 if (LLAMA_SUBPROCESS)
    target_compile_definitions(${TARGET} PUBLIC LLAMA_SUBPROCESS)
@@ -3875,7 +3875,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
        {"--no-log-jsonl"},
        "Log as JSONL (one JSON object per line) to stdout, this also disables colored logging (default: disabled)",
        [](common_params &, bool value) {
-            common_log_set_jsonl(value);
+            common_log_set_jsonl(common_log_main(), value);
        }
    ).set_env("LLAMA_ARG_LOG_JSONL"));
    add_opt(common_arg(
@@ -1586,11 +1586,6 @@ common_context_seq_rm_type common_context_can_seq_rm(llama_context * ctx) {
        return COMMON_CONTEXT_SEQ_RM_TYPE_NO;
    }

-    if (llama_n_rs_seq(ctx) > 0) {
-        COM_TRC("%s", "the context supports bounded partial sequence removal\n");
-        return COMMON_CONTEXT_SEQ_RM_TYPE_RS;
-    }
-
    common_context_seq_rm_type res = COMMON_CONTEXT_SEQ_RM_TYPE_PART;

    llama_memory_clear(mem, true);
@@ -1607,6 +1602,12 @@ common_context_seq_rm_type common_context_can_seq_rm(llama_context * ctx) {
        goto done;
    }

+    if (llama_n_rs_seq(ctx) > 0) {
+        COM_TRC("%s", "the context supports bounded partial sequence removal\n");
+        res = COMMON_CONTEXT_SEQ_RM_TYPE_RS;
+        goto done;
+    }
+
    // try to remove the last tokens
    if (!llama_memory_seq_rm(mem, 0, 1, -1)) {
        COM_TRC("%s", "the context does not support partial sequence removal\n");
@@ -1,6 +1,5 @@
 #include "fit.h"

-#include "json.h"
 #include "log.h"

 #include "../src/llama-ext.h"
@@ -192,9 +191,9 @@ static void common_params_fit_impl(
    uint32_t hp_nct = 0; // hparams.n_ctx_train
    uint32_t hp_nex = 0; // hparams.n_expert

-    // size the context for all sequences, but keep minimums and alignment per KV stream
-    const uint32_t n_seq_max  = std::max<uint32_t>(1, cparams->n_seq_max);
-    const uint32_t n_streams  = cparams->kv_unified ? 1 : n_seq_max;
+    // with non-unified kv, we need to take into account n_streams
+    // for example, if memory can hold more than model's trained context size, we must extend the n_ctx to hold enough n_streams
+    const uint32_t n_streams  = cparams->kv_unified ? 1 : std::max<uint32_t>(1, cparams->n_seq_max);
    const bool     n_ctx_auto = cparams->n_ctx == 0;

    dmds_t   dmds_extra;       // memory of the extra model, laid out on the devices of the main model
@@ -264,15 +263,15 @@ static void common_params_fit_impl(
    dmds_t dmds_full = common_get_device_memory_data_impl(path_model, mparams, cparams, devs, hp_ngl, hp_nct, hp_nex, log_level);

    // saturate instead of overflowing, this also preserves the UINT32_MAX sentinel of n_ctx_min:
-    const uint32_t n_ctx_max       = (uint32_t) std::min<uint64_t>(uint64_t(hp_nct)    * n_seq_max, UINT32_MAX);
+    const uint32_t n_ctx_max       = (uint32_t) std::min<uint64_t>(uint64_t(hp_nct)    * n_streams, UINT32_MAX);
    const uint32_t n_ctx_min_total = (uint32_t) std::min<uint64_t>(uint64_t(n_ctx_min) * n_streams, UINT32_MAX);

    // llama_context would use only hp_nct in total for n_ctx == 0, resolve the context before measuring anything else:
    if (n_ctx_auto) {
        cparams->n_ctx = n_ctx_max;
-        if (n_seq_max > 1) {
-            LOG_TRC("%s: context size unset -> using %" PRIu32 " for %" PRIu32 " sequences:\n",
-                __func__, n_ctx_max, n_seq_max);
+        if (n_streams > 1) {
+            LOG_TRC("%s: context size unset and KV cache not unified -> using %" PRIu32 " for %" PRIu32 " sequences:\n",
+                __func__, n_ctx_max, n_streams);
            dmds_full = common_get_device_memory_data_impl(path_model, mparams, cparams, devs, hp_ngl, hp_nct, hp_nex, log_level);
        }
    }
@@ -916,9 +915,6 @@ void common_memory_breakdown_print(const struct llama_context * ctx) {

    std::vector<std::array<std::string, 9>> table_data;
    table_data.reserve(devices.size());
-
-    // same data as the table below, for --log-jsonl consumers
-    common_json rows = common_json::array();
    const std::string template_header = "%s: | %s | %s   %s    %s   %s   %s   %s    %s |\n";
    const std::string template_gpu    = "%s: | %s | %s = %s + (%s = %s + %s + %s) + %s |\n";
    const std::string template_other  = "%s: | %s | %s   %s    %s = %s + %s + %s    %s |\n";
@@ -993,19 +989,6 @@ void common_memory_breakdown_print(const struct llama_context * ctx) {
            std::to_string(mb.context / MiB),
            std::to_string(mb.compute / MiB),
            std::to_string(unaccounted / static_cast<int64_t>(MiB))});
-
-        rows.push_back({
-            {"kind",        "device"},
-            {"name",        name},
-            {"description", desc},
-            {"total",       total / MiB},
-            {"free",        free / MiB},
-            {"self",        self / MiB},
-            {"model",       mb.model / MiB},
-            {"context",     mb.context / MiB},
-            {"compute",     mb.compute / MiB},
-            {"unaccounted", unaccounted / static_cast<int64_t>(MiB)},
-        });
    }

    // print memory breakdown for host:
@@ -1021,15 +1004,6 @@ void common_memory_breakdown_print(const struct llama_context * ctx) {
            std::to_string(mb_host.context / MiB),
            std::to_string(mb_host.compute / MiB),
            ""}); // unaccounted
-
-        rows.push_back({
-            {"kind",    "host"},
-            {"name",    "Host"},
-            {"self",    self / MiB},
-            {"model",   mb_host.model / MiB},
-            {"context", mb_host.context / MiB},
-            {"compute", mb_host.compute / MiB},
-        });
    }

    // print memory breakdown for all remaining buffer types:
@@ -1051,16 +1025,6 @@ void common_memory_breakdown_print(const struct llama_context * ctx) {
            std::to_string(mb.context / MiB),
            std::to_string(mb.compute / MiB),
            ""}); // unaccounted
-
-        rows.push_back({
-            {"kind",    "buffer_type"},
-            {"name",    name},
-            {"self",    self / MiB},
-            {"model",   mb.model / MiB},
-            {"context", mb.context / MiB},
-            {"compute", mb.compute / MiB},
-        });
-
        seen_buffer_types.insert(buft);
    }

@@ -1078,11 +1042,6 @@ void common_memory_breakdown_print(const struct llama_context * ctx) {
            __func__, td[1].c_str(), td[2].c_str(), td[3].c_str(), td[4].c_str(), td[5].c_str(),
            td[6].c_str(), td[7].c_str(), td[8].c_str());
    }
-
-    LOG_JSON("fit_memory_breakdown", common_json({
-        {"unit", "MiB"},
-        {"rows", rows},
-    }));
 }

 void common_fit_print(
@@ -842,12 +842,6 @@ value member_expression::execute_impl(context & ctx) {
        } else {
            property = this->property->execute(ctx);
        }
-    } else if (is_stmt<integer_literal>(this->property)) {
-        // syntax: obj.index
-        property = mk_val<value_int>(cast_stmt<integer_literal>(this->property)->val);
-        if (property->as_int() < 0) {
-            throw std::runtime_error("Static member property cannot be negative");
-        }
    } else {
        // syntax: obj.prop
        if (!is_stmt<identifier>(this->property)) {
@@ -37,16 +37,6 @@ void common_log_set_verbosity_thold(int verbosity) {
    common_log_verbosity_thold = verbosity;
 }

-static bool common_log_jsonl = false;
-
-bool common_log_get_jsonl(void) {
-    return common_log_jsonl;
-}
-
-void common_log_set_jsonl(bool jsonl) {
-    common_log_jsonl = jsonl;
-}
-
 static int64_t t_us() {
    return std::chrono::duration_cast<std::chrono::microseconds>(std::chrono::system_clock::now().time_since_epoch()).count();
 }
@@ -97,7 +87,6 @@ struct common_log_entry {
    bool is_end       { false }; // signals the worker thread to stop
    bool prefix       { false };
    bool jsonl        { false };
-    bool is_json      { false }; // msg already holds a serialized JSON object

    common_log_entry(size_t size = 256) : msg(size) { }

@@ -118,12 +107,6 @@ struct common_log_entry {
        }

        if (jsonl) {
-            if (is_json) {
-                fprintf(fcur, "%s\n", msg.data());
-                fflush(fcur);
-                return;
-            }
-
            common_json obj = {
                {"type",  "log"},
                {"time",  timestamp},
@@ -173,6 +156,7 @@ struct common_log {
        file       = nullptr;
        prefix     = false;
        timestamps = false;
+        jsonl      = false;
        running    = false;
        t_start    = t_us();

@@ -200,6 +184,7 @@ private:

    bool prefix;
    bool timestamps;
+    bool jsonl;
    bool running;

    int64_t t_start;
@@ -288,8 +273,7 @@ public:
        entry.is_end    = false;
        entry.level     = level;
        entry.prefix    = prefix;
-        entry.jsonl     = common_log_jsonl;
-        entry.is_json   = false;
+        entry.jsonl     = jsonl;
        entry.timestamp = 0;
        if (timestamps) {
            entry.timestamp = t_us() - t_start;
@@ -299,42 +283,6 @@ public:
        cv_new.notify_one();
    }

-    void add_json(const char * type, const common_json & obj) {
-        const common_json full = {
-            {"type", type},
-            {"data", obj},
-        };
-
-        const std::string text = full.dump_safe();
-
-        std::unique_lock<std::mutex> lock(mtx);
-
-        // block if the queue is full
-        cv_full.wait(lock, [this]() { return !running || !is_full(); });
-
-        if (!running) {
-            // discard messages while the worker thread is paused
-            return;
-        }
-
-        auto & entry = queue[tail];
-
-        if (entry.msg.size() < text.size() + 1) {
-            entry.msg.resize(text.size() + 1);
-        }
-        memcpy(entry.msg.data(), text.c_str(), text.size() + 1);
-
-        entry.is_end    = false;
-        entry.level     = GGML_LOG_LEVEL_NONE;
-        entry.prefix    = false;
-        entry.jsonl     = true;
-        entry.is_json   = true;
-        entry.timestamp = 0;
-
-        tail = (tail + 1) % queue.size();
-        cv_new.notify_one();
-    }
-
    void resume() {
        std::lock_guard<std::mutex> lock(mtx);

@@ -440,6 +388,12 @@ public:

        this->timestamps = timestamps;
    }
+
+    void set_jsonl(bool jsonl) {
+        std::lock_guard<std::mutex> lock(mtx);
+
+        this->jsonl = jsonl;
+    }
 };

 //
@@ -486,14 +440,6 @@ void common_log_add(struct common_log * log, enum ggml_log_level level, const ch
    va_end(args);
 }

-void common_log_add_json(struct common_log * log, const char * type, const common_json & obj) {
-    if (!common_log_jsonl) {
-        return;
-    }
-
-    log->add_json(type, obj);
-}
-
 void common_log_set_file(struct common_log * log, const char * file) {
    log->set_file(file);
 }
@@ -521,6 +467,10 @@ void common_log_set_timestamps(struct common_log * log, bool timestamps) {
    log->set_timestamps(timestamps);
 }

+void common_log_set_jsonl(struct common_log * log, bool jsonl) {
+    log->set_jsonl(jsonl);
+}
+
 void common_log_flush(struct common_log * log) {
    log->pause();
    log->resume();
@@ -43,10 +43,6 @@ int  common_log_get_verbosity_thold(void);

 void common_log_set_verbosity_thold(int verbosity); // not thread-safe

-bool common_log_get_jsonl(void);
-
-void common_log_set_jsonl(bool jsonl); // not thread-safe
-
 int common_log_get_verbosity(enum ggml_log_level level);

 void common_log_default_callback(enum ggml_log_level level, const char * text, void * user_data);
@@ -95,6 +91,7 @@ void common_log_set_file      (struct common_log * log, const char * file); // n
 void common_log_set_colors    (struct common_log * log, log_colors colors); // not thread-safe
 void common_log_set_prefix    (struct common_log * log, bool prefix);       // whether to output prefix to each log
 void common_log_set_timestamps(struct common_log * log, bool timestamps);   // whether to output timestamps in the prefix
+void common_log_set_jsonl     (struct common_log * log, bool jsonl);        // print each log as a JSON object on one line, not thread-safe
 void common_log_flush         (struct common_log * log);                    // flush all pending log messages

 // helper macros for logging
@@ -130,17 +127,3 @@ void common_log_flush         (struct common_log * log);                    // f
 #define LOG_WRNV(verbosity, ...) LOG_TMPL(GGML_LOG_LEVEL_WARN,  verbosity, __VA_ARGS__)
 #define LOG_ERRV(verbosity, ...) LOG_TMPL(GGML_LOG_LEVEL_ERROR, verbosity, __VA_ARGS__)
 #define LOG_CNTV(verbosity, ...) LOG_TMPL(GGML_LOG_LEVEL_CONT,  verbosity, __VA_ARGS__)
-
-class common_json; // defined in common/json.h
-
-// helper allows different types of json output
-// no-op if --log-jsonl is not set
-void common_log_add_json(struct common_log * log, const char * type, const common_json & data);
-
-// will only print if --log-jsonl is set
-#define LOG_JSON(type, data) \
-    do { \
-        if (common_log_get_jsonl()) { \
-            common_log_add_json(common_log_main(), type, data); \
-        } \
-    } while (0)
@@ -104,12 +104,6 @@ common_chat_params common_chat_params_init_deepseek_v3_2(const common_chat_templ
    const std::string GEN_PROMPT   = "<｜Assistant｜>";
    const std::string TC_SEPARATOR = "\n\n";

-    // lets the server find user turns in the prompt and place context checkpoints there
-    data.message_delimiters = {
-        { COMMON_CHAT_ROLE_ASSISTANT, GEN_PROMPT },
-        { COMMON_CHAT_ROLE_USER, "<｜User｜>" },
-    };
-
    data.prompt = common_chat_template_direct_apply_impl(
        tmpl, inputs, adjusted_messages, std::nullopt, additional_context);
    data.generation_prompt = common_chat_template_generation_prompt_impl(
@@ -23,9 +23,8 @@ common_chat_params common_chat_params_init_qwen3_coder(const common_chat_templat
    if (supports_reasoning) {
        data.thinking_start_tag = "<think>";
        // Support both </think> and <tool_call> as reasoning end sequences.
-        // The newline variant comes first so it is included in the forced message
        // <function= is omitted, as it is a workaround for Qwen3-Coder which is not a thinking model
-        data.thinking_end_tags = { "\n</think>", "</think>", "<tool_call>" };
+        data.thinking_end_tags = { "</think>", "<tool_call>" };
        data.preserved_tokens.insert(data.preserved_tokens.end(), { "<think>", "</think>" });
    }

@@ -105,34 +104,9 @@ common_chat_params common_chat_params_init_qwen3_coder(const common_chat_templat

                    auto arg_open = p.tool_arg_open("<parameter=" + p.tool_arg_name(p.literal(param.name)) + ">\n");

-                    auto types = param.schema->value_types();
-
-                    auto arg_value = p.eps();
-                    if (!types.has(common_chat_schema::TYPE_STRING)) {
-                        arg_value = p.tool_arg_json_value(p.schema(p.json(), rule_name + "-schema", doc, *param.schema)) + arg_close;
-                    } else if (types.is_only(common_chat_schema::TYPE_STRING)) {
-                        arg_value = arg_string;
-                    } else {
-                        // The string alternative accepts any text, so the grammar only keeps the raw string
-                        // rule. The parser still tries the JSON alternatives first to type the value.
-                        auto json_value = p.choice();
-                        if (types.has(common_chat_schema::TYPE_OBJECT)) {
-                            json_value |= p.json_object();
-                        }
-                        if (types.has(common_chat_schema::TYPE_ARRAY)) {
-                            json_value |= p.json_array();
-                        }
-                        if (types.has(common_chat_schema::TYPE_NUMBER) || types.has(common_chat_schema::TYPE_INTEGER)) {
-                            json_value |= p.json_number();
-                        }
-                        if (types.has(common_chat_schema::TYPE_BOOLEAN)) {
-                            json_value |= p.json_bool();
-                        }
-                        if (types.has(common_chat_schema::TYPE_NULL)) {
-                            json_value |= p.json_null();
-                        }
-                        arg_value = p.gbnf(p.atomic(p.tool_arg_json_value(json_value) + arg_close) | arg_string, "xml-arg-string");
-                    }
+                    auto arg_value = param.schema->may_be_string() ?
+                        arg_string :
+                        p.tool_arg_json_value(p.schema(p.json(), rule_name + "-schema", doc, *param.schema)) + arg_close;

                    auto arg_rule = p.rule(rule_name, p.tool_arg(arg_open + arg_value));

@@ -123,7 +123,6 @@ TEXT_MODEL_MAP: dict[str, str] = {
    "HunYuanDenseV1ForCausalLM": "hunyuan",
    "HunYuanMoEV1ForCausalLM": "hunyuan",
    "HunYuanVLForConditionalGeneration": "hunyuan",
-    "HrmTextForCausalLM": "hrm_text",
    "HYV3ForCausalLM": "hunyuan",
    "HYV4ForCausalLM": "hy_v4",
    "IQuestCoderForCausalLM": "llama",
@@ -169,7 +168,6 @@ TEXT_MODEL_MAP: dict[str, str] = {
    "Mamba2ForCausalLM": "mamba",
    "MambaForCausalLM": "mamba",
    "MambaLMHeadModel": "mamba",
-    "MapleForCausalLM": "maple",
    "MellumForCausalLM": "mellum",
    "MiMoV2FlashForCausalLM": "mimo",
    "MiMoV2ForCausalLM": "mimo",
@@ -1633,9 +1633,6 @@ class TextModel(ModelBase):
        if chkhsh == "9e454714343b69b99b71795c1d27a68c2a1d15dab111f4d353109f966af29da7":
            # ref: https://huggingface.co/LiquidAI/LFM2.5-8B-A1B
            res = "lfm2"
-        if chkhsh == "846deafc5b0fa786186fa4ae6c7b49903cf2f1d1895bdb80b9120d60be135252":
-            # ref: https://huggingface.co/danish-foundation-models/DFM-Mimir
-            res = "gemma4"
        if chkhsh == "0a766d034107bc736a3f2dc4968fd62e54a3570f1454443e0c5a4cc6bd7941ed":
            # ref: https://huggingface.co/XHToken/Spark-X2.5-1.7B
            res = "spark2_5"
@@ -1861,9 +1858,6 @@ class TextModel(ModelBase):
        if chkhsh == "972da7b59cec44d1f0a490a86c96df53859e486e481563e5dddac155013d87ac":
            # ref: https://huggingface.co/poolside/Laguna-XS.2
            res = "laguna"
-        if chkhsh == "653660222fb704f61cbf2b618a8ae6502b7f8b20c980f9a5de07ed78e13319cd":
-            # ref: https://huggingface.co/ufakai/ufakzeka-1
-            res = "ufakzeka"

        if res is None:
            logger.warning("\n")
@@ -1,79 +0,0 @@
-from __future__ import annotations
-
-import re
-
-from typing import Iterable, TYPE_CHECKING
-
-if TYPE_CHECKING:
-    from torch import Tensor
-
-from .base import ModelBase, TextModel, gguf
-
-
-@ModelBase.register("HrmTextForCausalLM")
-@ModelBase.example("danish-foundation-models/DFM-Mimir")
-class HrmTextModel(TextModel):
-    model_arch = gguf.MODEL_ARCH.HRM_TEXT
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-
-        # training-style configs store the per-stack count in num_hidden_layers,
-        # transformers-style configs keep it in num_layers_per_stack
-        self.layers_per_stack = self.hparams.get("num_layers_per_stack") or self.hparams["num_hidden_layers"]
-        self.h_cycles = self.hparams["H_cycles"]
-        self.l_cycles = self.hparams["L_cycles"]
-
-        # block_count is the expanded cache-slot count; the file only holds
-        # 2 * layers_per_stack physical blocks
-        self.block_count = self.layers_per_stack * self.h_cycles * (self.l_cycles + 1)
-        self.tensor_map = gguf.get_tensor_name_map(self.model_arch, 2 * self.layers_per_stack)
-
-    def set_vocab(self):
-        self._set_vocab_gpt2()
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-
-        head_dim = self.hparams.get("head_dim") or self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
-        self.gguf_writer.add_rope_dimension_count(head_dim)
-        self.gguf_writer.add_embedding_scale(self.hparams["embedding_scale"])
-        self.gguf_writer.add_hrm_layers_per_stack(self.layers_per_stack)
-        self.gguf_writer.add_hrm_h_cycles(self.h_cycles)
-        self.gguf_writer.add_hrm_l_cycles(self.l_cycles)
-        self.gguf_writer.add_hrm_prefix_lm(bool(self.hparams.get("prefix_lm", False)))
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        if name == "model.embed_tokens.weight":
-            yield self.format_tensor_name(gguf.MODEL_TENSOR.TOKEN_EMBD), data_torch
-            return
-        if name == "lm_head.weight":
-            yield self.format_tensor_name(gguf.MODEL_TENSOR.OUTPUT), data_torch
-            return
-        if name == "model.z_L_init":
-            yield self.format_tensor_name(gguf.MODEL_TENSOR.HRM_Z_L_INIT, suffix=""), data_torch
-            return
-
-        match = re.fullmatch(r"model\.([LH])_module\.layers\.(\d+)\.(.+)", name)
-        if match is None:
-            raise ValueError(f"can not map tensor: {name}")
-
-        stack, layer_s, tensor_name = match.groups()
-        # the L stack occupies blocks [0, layers_per_stack), the H stack follows it
-        layer_idx = int(layer_s) + (self.layers_per_stack if stack == "H" else 0)
-
-        if tensor_name == "attn.gqkv_proj.weight":
-            gate, q, k, v = data_torch.chunk(4, dim=0)
-            yield self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_GATE, layer_idx), gate.contiguous()
-            yield self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_Q, layer_idx), q.contiguous()
-            yield self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_K, layer_idx), k.contiguous()
-            yield self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_V, layer_idx), v.contiguous()
-        elif tensor_name == "mlp.gate_up_proj.weight":
-            gate, up = data_torch.chunk(2, dim=0)
-            yield self.format_tensor_name(gguf.MODEL_TENSOR.FFN_GATE, layer_idx), gate.contiguous()
-            yield self.format_tensor_name(gguf.MODEL_TENSOR.FFN_UP, layer_idx), up.contiguous()
-        else:
-            if tensor_name.startswith("attn."):
-                tensor_name = "self_attn." + tensor_name[len("attn."):]
-            tensor_name = "model.layers.{bid}." + tensor_name
-            yield from super().modify_tensors(data_torch, tensor_name.format(bid=layer_idx), layer_idx)
@@ -1,87 +0,0 @@
-from __future__ import annotations
-
-from typing import Iterable, TYPE_CHECKING, cast
-
-import torch
-
-if TYPE_CHECKING:
-    from torch import Tensor
-
-from .base import LazyTorchTensor, ModelBase, TextModel, gguf
-
-
-@ModelBase.register("MapleForCausalLM")
-@ModelBase.example("deepgrove/maple-preview")
-class MapleModel(TextModel):
-    model_arch = gguf.MODEL_ARCH.MAPLE
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        hparams = self.hparams
-
-        assert hparams["hidden_act"] == "silu"
-        assert hparams.get("num_shared_experts", 0) == 0
-        assert hparams.get("norm_topk_prob", True)
-        assert hparams.get("nope_on_global_attention", False)
-
-        head_dim = hparams.get("head_dim", hparams["hidden_size"] // hparams["num_attention_heads"])
-        partial_rotary_factor = self.rope_parameters.get("partial_rotary_factor", 1.0)
-
-        self.gguf_writer.add_vocab_size(hparams["vocab_size"])
-        self.gguf_writer.add_rope_dimension_count(int(head_dim * partial_rotary_factor))
-        self.gguf_writer.add_sliding_window(hparams["sliding_window"])
-        self.gguf_writer.add_sliding_window_pattern([layer_type == "sliding_attention" for layer_type in hparams["layer_types"]])
-        self.gguf_writer.add_expert_feed_forward_length(hparams["moe_intermediate_size"])
-        # the reference clamps the MoE SwiGLU gate/up at 7.0 (modeling_maple.py)
-        self.gguf_writer.add_swiglu_clamp_exp([7.0] * self.block_count)
-
-    _experts: list[dict[str, Tensor]] | None = None
-
-    @staticmethod
-    def _stack_experts(tensors: list[Tensor]) -> Tensor:
-        shape = (len(tensors), *tensors[0].shape)
-        dtype = tensors[0].dtype
-        meta = LazyTorchTensor.meta_with_dtype_and_shape(dtype, shape)
-
-        # tensors goes through args, not the closure, so that `func` matches
-        # LazyBase's single-argument shape
-        def stack(ts: list[Tensor]) -> Tensor:
-            result = torch.empty(shape, dtype=dtype)
-            for expert_id, tensor in enumerate(ts):
-                result[expert_id].copy_(LazyTorchTensor.to_eager(tensor))
-            ts.clear()
-            return result
-
-        return cast(torch.Tensor, LazyTorchTensor(meta=meta, args=(tensors,), func=stack))
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        if "mlp.experts" in name:
-            n_experts = self.hparams["num_experts"]
-            assert bid is not None
-
-            if self._experts is None:
-                self._experts = [{} for _ in range(self.block_count)]
-
-            self._experts[bid][name] = data_torch
-
-            if len(self._experts[bid]) >= n_experts * 3:
-                for weight_name in ("down_proj", "gate_proj", "up_proj"):
-                    tensors = []
-
-                    for expert_id in range(n_experts):
-                        expert_name = f"model.layers.{bid}.mlp.experts.{expert_id}.{weight_name}.weight"
-                        tensors.append(self._experts[bid].pop(expert_name))
-
-                    merged_name = f"model.layers.{bid}.mlp.experts.{weight_name}.weight"
-                    yield from super().modify_tensors(self._stack_experts(tensors), merged_name, bid)
-            return
-
-        yield from super().modify_tensors(data_torch, name, bid)
-
-    def prepare_tensors(self):
-        super().prepare_tensors()
-
-        if self._experts is not None:
-            experts = [name for layer in self._experts for name in layer]
-            if experts:
-                raise ValueError(f"Unprocessed experts: {experts}")
@@ -163,7 +163,6 @@ models = [
    {"name": "granite-embed-multi-311m", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/ibm-granite/granite-embedding-311m-multilingual-r2", },
    {"name": "mellum2",          "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/JetBrains/Mellum2-12B-A2.5B-Base"},
    {"name": "laguna",           "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/poolside/Laguna-XS.2", },
-    {"name": "ufakzeka",         "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/ufakai/ufakzeka-1", },
 ]

 # some models are known to be broken upstream, so we will skip them as exceptions
@@ -192,10 +191,6 @@ pre_computed_hashes = [
    {"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"},
-    # hrm-text (DFM Mimir) is SPM-style BPE: normalizer maps ' ' -> '▁', merges
-    # over the whole text (fix_mistral_regex inserts a tekken regex that is a
-    # no-op here); the gemma4 pre (escape ws, split on newlines only) matches it.
-    {"name": "gemma4", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/danish-foundation-models/DFM-Mimir", "chkhsh": "846deafc5b0fa786186fa4ae6c7b49903cf2f1d1895bdb80b9120d60be135252"},
    {"name": "spark2_5", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/XHToken/Spark-X2.5-1.7B", "chkhsh": "0a766d034107bc736a3f2dc4968fd62e54a3570f1454443e0c5a4cc6bd7941ed"},
 ]

@@ -12,8 +12,6 @@ The OpenVINO backend is implemented in `ggml/src/ggml-openvino` and provides a t
 - Compiles and caches the model for the target device.
 - Binds GGML tensor memory to OpenVINO inference tensors and runs inference.

-For guidance on contributing to the OpenVINO backend, see the [OpenVINO Backend Contributing Guide](https://github.com/ravi9/llamacpp-ov-dev-guide/blob/main/contributing-llamacpp-ov.md).
-
 ## Contents

 - [Supported Devices](#supported-devices)
@@ -98,7 +96,7 @@ Although, the validated models below were tested with `llama-cli` using the `Q4_
  - **SL** = Stateless (`GGML_OPENVINO_STATEFUL_EXECUTION=0`)
  - **SF** = Stateful (`GGML_OPENVINO_STATEFUL_EXECUTION=1`)
  - Note: The NPU operates in stateless mode only.
- **Validation system:** Intel® Core™ Ultra 5 238V (Lunar Lake) | 32 GB RAM | Ubuntu 24.04 | Intel Graphics Compiler 2.41.5 | Intel OpenCL GPU Driver 26.31.39395.13-0 | Intel NPU Driver 1.38.0.
+- **Validation system:** Intel® Core™ Ultra 5 238V (Lunar Lake) | 32 GB RAM | Ubuntu 24.04 | Intel OpenCL GPU Driver 26.31.39395.13-0 | Intel NPU Driver 1.35.0.
 - See [Known Limitations](#known-limitations) for context on observed failures.

 | Model | CPU (SL / SF) | GPU (SL / SF) | NPU (SL) |
@@ -119,9 +117,9 @@ Although, the validated models below were tested with `llama-cli` using the `Q4_
 | [lmstudio-community/Qwen3.5-9B-Q4_K_M](https://huggingface.co/lmstudio-community/Qwen3.5-9B-GGUF) | ✓ / ✗ | ✓ / ✗ | ✗ |
 |  |  |  |  |
 | [unsloth/gemma-3-4b-it-Q4_K_M](https://huggingface.co/unsloth/gemma-3-4b-it-GGUF) | ✓ / ✓ | ✓ / ✓ | ✓ |
-| [bartowski/google_gemma-4-E2B-it-Q4_K_M](https://huggingface.co/bartowski/google_gemma-4-E2B-it-GGUF) | ✓ / ✓ | ✓ / ✓ | ✗ |
-| [bartowski/google_gemma-4-E4B-it-Q4_K_M](https://huggingface.co/bartowski/google_gemma-4-E4B-it-GGUF) | ✓ / ✓ | ✓ / ✓ | ✓ |
-| [bartowski/gemma-4-12B-it-Q4_K_M](https://huggingface.co/bartowski/gemma-4-12B-it-GGUF) | ✓ / ✓ | ✓ / ✓ | ✗ |
+| [bartowski/google_gemma-4-E2B-it-Q4_K_M](https://huggingface.co/bartowski/google_gemma-4-E2B-it-GGUF) | ✓ / ✗ | ✓ / ✗ | ✗ |
+| [bartowski/google_gemma-4-E4B-it-Q4_K_M](https://huggingface.co/bartowski/google_gemma-4-E4B-it-GGUF) | ✓ / ✗ | ✓ / ✗ | ✓ |
+| [bartowski/gemma-4-12B-it-Q4_K_M](https://huggingface.co/bartowski/gemma-4-12B-it-GGUF) | ✓ / ✗ | ✓ / ✗ | ✓ |
 |  |  |  |  |
 | [bartowski/Phi-3-mini-4k-instruct-Q4_K_M](https://huggingface.co/bartowski/Phi-3-mini-4k-instruct-GGUF) | ✓ / ✓ | ✓ / ✓ | ✓ |
 | [bartowski/Phi-3.5-mini-instruct-Q4_K_M](https://huggingface.co/bartowski/Phi-3.5-mini-instruct-GGUF) | ✓ / ✓ | ✓ / ✓ | ✓ |
@@ -134,9 +132,9 @@ Although, the validated models below were tested with `llama-cli` using the `Q4_
 | [bartowski/DeepSeek-R1-Distill-Llama-8B-Q4_K_M](https://huggingface.co/bartowski/DeepSeek-R1-Distill-Llama-8B-GGUF) | ✓ / ✓ | ✓ / ✓ | ✓ |
 | [bartowski/DeepSeek-R1-Distill-Qwen-7B-Q4_K_M](https://huggingface.co/bartowski/DeepSeek-R1-Distill-Qwen-7B-GGUF) | ✓ / ✓ | ✓ / ✓ | ✓ |
 |  |  |  |  |
-| [ibm-granite/granite-4.0-350m-Q4_K_M](https://huggingface.co/ibm-granite/granite-4.0-350m-GGUF) | ✓ / ✓ | ✓ / ✓ | ✓ |
+| [ibm-granite/granite-4.0-350m-Q4_K_M](https://huggingface.co/ibm-granite/granite-4.0-350m-GGUF) | ✓ / ✓ | ✗ / ✗ | ✓ |
 | [ibm-granite/granite-4.0-micro-Q4_K_M](https://huggingface.co/ibm-granite/granite-4.0-micro-GGUF) | ✓ / ✓ | ✓ / ✓ | ✓ |
-| [ibm-granite/granite-4.0-1b-Q4_K_M](https://huggingface.co/ibm-granite/granite-4.0-1b-GGUF) | ✓ / ✓ | ✓ / ✓ | ✗ |
+| [ibm-granite/granite-4.0-1b-Q4_K_M](https://huggingface.co/ibm-granite/granite-4.0-1b-GGUF) | ✓ / ✓ | ✗ / ✗ | ✗ |
 | [ibm-research/granite-3.2-8b-instruct-Q4_K_M](https://huggingface.co/ibm-research/granite-3.2-8b-instruct-GGUF) | ✓ / ✓ | ✓ / ✓ | ✓ |
 |  |  |  |  |
 | [HuggingFaceTB/smollm2-1.7b-instruct-q4_k_m](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct-GGUF) | ✓ / ✓ | ✓ / ✓ | ✓ |
@@ -244,8 +242,8 @@ chmod +x build-llamacpp-ov.sh
 # ============================================
 set -euo pipefail

-OPENVINO_VERSION_MAJOR="2026.4"
-OPENVINO_VERSION_FULL="2026.4.0.22959.99c81491cc3"
+OPENVINO_VERSION_MAJOR="2026.3.1"
+OPENVINO_VERSION_FULL="2026.3.1.22476.56d9685302d"

 SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
 OPENVINO_INSTALL_DIR="/opt/intel/openvino_${OPENVINO_VERSION_MAJOR}"
@@ -342,7 +340,7 @@ echo "  ./build/ReleaseOV/bin/llama-cli -m model.gguf"
 ```

 > [!NOTE]
-> The script pins OpenVINO `2026.4` via the `OPENVINO_VERSION_MAJOR` / `OPENVINO_VERSION_FULL` variables at the top — edit them to track a different release.
+> The script pins OpenVINO `2026.3.1` via the `OPENVINO_VERSION_MAJOR` / `OPENVINO_VERSION_FULL` variables at the top — edit them to track a different release.

 </details>

@@ -372,8 +370,8 @@ REM ============================================
 REM llama.cpp OpenVINO Build Script (Ninja)
 REM ============================================

-set "OPENVINO_VERSION_MAJOR=2026.4"
-set "OPENVINO_VERSION_FULL=2026.4.0.22959.99c81491cc3"
+set "OPENVINO_VERSION_MAJOR=2026.3.1"
+set "OPENVINO_VERSION_FULL=2026.3.1.22476.56d9685302d"

 set "SCRIPT_DIR=%~dp0"
 set "VCPKG_DIR=C:\vcpkg"
@@ -552,7 +550,7 @@ endlocal
 ```

 > [!NOTE]
-> The script pins OpenVINO `2026.4` via the `OPENVINO_VERSION_MAJOR` / `OPENVINO_VERSION_FULL` variables at the top — edit them to track a different release. From any new shell, source the matching `setupvars` script via the junction — `call "C:\Intel\openvino\setupvars.bat"` from `cmd`, or `& "C:\Intel\openvino\setupvars.ps1"` from PowerShell. If `winget` cannot register Visual Studio Build Tools on first run, install them once manually and re-run the script from an elevated **Developer Command Prompt for VS 2022**.
+> The script pins OpenVINO `2026.3.1` via the `OPENVINO_VERSION_MAJOR` / `OPENVINO_VERSION_FULL` variables at the top — edit them to track a different release. From any new shell, source the matching `setupvars` script via the junction — `call "C:\Intel\openvino\setupvars.bat"` from `cmd`, or `& "C:\Intel\openvino\setupvars.ps1"` from PowerShell. If `winget` cannot register Visual Studio Build Tools on first run, install them once manually and re-run the script from an elevated **Developer Command Prompt for VS 2022**.

 </details>

@@ -721,13 +719,10 @@ Boolean flags follow a uniform convention: set to a **positive integer** (e.g. `
 | `GGML_OPENVINO_STATEFUL_EXECUTION`| Boolean   | `0`        | Enable stateful KV cache for better performance. Recommended on CPU, GPU.                                   |
 | `GGML_OPENVINO_DISABLE_CACHE`     | Boolean   | `0`        | Disable the in-process compiled-model / decoder cache (cache is on by default). Set to `1` to disable.      |
 | `GGML_OPENVINO_DISABLE_KV_SLICE`  | Boolean   | `0`        | Disable the KV-cache input-tensor slicing optimization (slicing is on by default on CPU/GPU). Set to `1` to disable. |
-| `GGML_OPENVINO_DISABLE_KV_STATE_RELAYOUT` | Boolean | `0`     | Disable the stateful KV-state sequence-axis relayout (relayout is on by default). It moves the KV state sequence axis from dim 1 to dim 2, so the GPU plugin can append new tokens in place instead of copying the whole state every token, and the reader side no longer transposes the whole accumulated state. Set to `1` to disable. |
 | `GGML_OPENVINO_MANUAL_GQA_ATTN`   | Boolean   | device-based | Tri-state. When **unset**, manual GQA attention is enabled by default on `GPU` and disabled on other devices. Set to a positive integer to force-enable, or `0` to force-disable. |
 | `GGML_OPENVINO_MEMORY_OPTIMIZE`   | Boolean   | `0`        | Umbrella switch for compile-time memory reductions. Enables `GGML_OPENVINO_REDUCE_COMPILE_MEM` and, on GPU, `GGML_OPENVINO_RELEASE_WEIGHTS` unless those fine-grained variables are explicitly set. |
 | `GGML_OPENVINO_REDUCE_COMPILE_MEM`| Boolean   | inherits from `GGML_OPENVINO_MEMORY_OPTIMIZE` | Reduce compile-time host memory use by streaming weight requantization and avoiding extra weight-node materialization where possible. Set explicitly to override the umbrella switch. |
 | `GGML_OPENVINO_RELEASE_WEIGHTS`   | Boolean   | inherits from `GGML_OPENVINO_MEMORY_OPTIMIZE` on GPU | GPU-only. Release host weight buffers after the compiled model cache can reuse the device/plugin copy. Requires stable graph shapes; dynamic workloads that need recompilation should leave this disabled. |
-| `GGML_OPENVINO_SPILL_DIR`         | String    | `not set`  | Directory for a disk-backed weight buffer. When set, the repacked weight buffer is mapped from an unlinked file on this path instead of anonymous memory, so its pages are reclaimable under memory pressure instead of staying pinned, cutting the load-time host memory peak. Must point at real storage; a tmpfs mount (e.g. `/tmp` on many systems) backs it with RAM and makes the peak worse. |
-| `GGML_OPENVINO_REQUANT_KQUANT`    | String    | `not set`  | Requantize Q6_K/Q5_K weights (and matching MoE expert weights) to a 4-bit target instead of the default Q8_0_C, trading accuracy for less memory traffic. One of `q4_sym128` (Q6_K/Q5_K only), `q4_sym128_all` (Q4_K too, drops its per-group zero point), `q4_asym64_all` (Q6_K/Q5_K/Q4_K, keeps a real zero point at group 64), or `native` (no requantization). |
 | `GGML_OPENVINO_PROFILING`         | Boolean   | `0`        | Enable execution-time profiling.                                                                            |
 | `GGML_OPENVINO_DUMP_CGRAPH`       | Boolean   | `0`        | Dump the GGML compute graph to `cgraph_ov.txt`.                                                             |
 | `GGML_OPENVINO_DUMP_IR`           | Boolean   | `0`        | Serialize OpenVINO IR files with timestamps.                                                                |
@@ -790,15 +790,14 @@ User can use the device management in [docs/multi-gpu.md](https://github.com/ggm

 | Name              | Value            | Function                                                                                                                  |
 |-------------------|------------------|---------------------------------------------------------------------------------------------------------------------------|
-| GGML_SYCL_DEBUG   | 0 (default) or 1 | Enable log function: GGML_SYCL_DEBUG() for common debug. |
-| GGML_SYCL_DEV_DEBUG   | 0 (default) or 1 | Enable log function: GGML_SYCL_DEV_DEBUG() for developmental purposes by replacing GGML_SYCL_DEBUG() in special codes. Restore to GGML_SYCL_DEBUG() before committing code.|
+| GGML_SYCL_DEBUG   | 0 (default) or 1 | Enable log function by macro: GGML_SYCL_DEBUG                                                                             |
 | GGML_SYCL_DEV2DEV_MEMCPY | 0 (default), 1, 2 | Choose the method of dev2dev memory copy.<br>Value: <br>*  0: SYCL API (default), only support dGPUs.<br>* 1: L0 API -- Better performance, only support dGPUs, found to lead to abnormal crash in some case. <br>* 2: Host Forward -- Most stable method for all cases (including iGPU + dGPU*N), but with lower performance (-2% to -5%).<br>SYCL & L0 API are easy to be impacted by Intel GPU driver issue. When you meet the garbled output or crash issues in multiple GPUs case, try with this debug flag to work around or check the issue.|
 | GGML_SYCL_ENABLE_FLASH_ATTN | 1 (default) or 0| Enable Flash-Attention. It can reduce memory usage. The performance impact depends on the LLM.|
 | GGML_SYCL_ENABLE_OPT | 0 or 1 (default)| Enable optimize features for Intel GPUs. (Recommended to 0 for Intel devices older than Gen 10) |
 | GGML_SYCL_ENABLE_GRAPH | 0 (default) or 1 | Enable running computations through SYCL Graphs feature. Disabled by default because SYCL Graph is still on development, no better performance. |
 | GGML_SYCL_ENABLE_HOST_PINNED_MEM | 0 or 1 (default) | Enable host pinned memory to speed up copy data from host to device. When disable it, host memory will common malloc() on CPU. Disable it when use `--load-model mlock`.|
 | GGML_SYCL_HOST_PINNED_MEM_2G | 0 (default) or 1 | Limit the max memory allocation to be no more than 2GB when enable host pinned memory. USM allocations above 2 GiB take the relaxed/large-allocation path, which serializes H2D copies with compute and prevents copy/compute overlap. It will impact the startup time. Need more test. Depend on `GGML_SYCL_ENABLE_HOST_PINNED_MEM=1`.|
-| GGML_SYCL_GET_MEM_API | 0 (default) or 1  | Set to get memory info (free, total) by Level Zero or SYCL API:<br>0 - Level Zero API: support more GPUs, only run on Level Zero running time. When there is an error, fallback to call SYCL API. Depend on GGML_SYCL_SUPPORT_LEVEL_ZERO_API.<br>1 - SYCL API: legacy, support more running time, it can't get the free size of some GPUs (like Arc770). In such case, return the free size as value of total size.|
+| GGML_SYCL_GET_MEM_API | 0 (default) or 1  | Set to get memory info (free, total) by Level Zero or SYCL API:<br>0 - Level Zero API: support more GPUs, only run on Level Zero running time. When there is an error, fallback to call SYCL API. Depend on GGML_SYCL_SUPPORT_LEVEL_ZERO_API.<br>1 - SYCL API: legacy, support more running time, it can't get the free size of some GPUs (like Arc770). In such case, return total size for free size.|
 | GGML_SYCL_USE_LEVEL_ZERO_API | 1 (default) or 0 | Use Level Zero API for device memory allocation instead of SYCL. Reduces system RAM usage on Intel dGPUs by avoiding DMA-buf/TTM host memory staging. Requires GGML_SYCL_SUPPORT_LEVEL_ZERO_API=ON at build time. SYCL backend always runs on Level Zero running time even if it's set as OFF (The SYCL api will be usage for memory allocation).|
 | GGML_SYCL_ENABLE_DNN | 0 or 1 (default)| Enable running computations through oneDNN and always use oneMKL. |
 | GGML_SYCL_FA_ONEDNN | 1 (default) or 0 | Enable the oneDNN fused SDPA (flash-attention) path on supported GPUs. Set to 0 to always use the native SYCL flash-attention kernel. |
@@ -44,7 +44,7 @@ Legend:
 |                     DSV4_HC_COMB | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
 |                     DSV4_HC_POST | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
 |                      DSV4_HC_PRE | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
-|                              DUP | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | 🟡 | 🟡 | ✅ | ✅ | ❌ | ❌ | ❌ |
+|                              DUP | ❌ | ✅ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                              ELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                              EXP | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                            EXPM1 | ❌ | ❌ | ✅ | 🟡 | 🟡 | ❌ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
@@ -5000,14 +5000,14 @@
 "CUDA0","REPEAT_BACK","type=f32,ne=[8,6,4,2],nr=[1,1,1,2],v=1","support","1","yes","CUDA"
 "CUDA0","DUP","type=f32,ne=[10,10,20,1]","support","1","yes","CUDA"
 "CUDA0","DUP","type=f16,ne=[10,10,20,1]","support","1","yes","CUDA"
-"CUDA0","DUP","type=i32,ne=[10,10,20,1]","support","1","yes","CUDA"
-"CUDA0","DUP","type=i16,ne=[10,10,20,1]","support","1","yes","CUDA"
+"CUDA0","DUP","type=i32,ne=[10,10,20,1]","support","0","no","CUDA"
+"CUDA0","DUP","type=i16,ne=[10,10,20,1]","support","0","no","CUDA"
 "CUDA0","DUP","type=f32,ne=[10,10,5,1],permute=[0,2,1,3]","support","1","yes","CUDA"
 "CUDA0","DUP","type=f16,ne=[10,10,5,1],permute=[0,2,1,3]","support","1","yes","CUDA"
 "CUDA0","DUP","type=f32,ne=[10,10,5,1],permute=[1,0,2,3]","support","1","yes","CUDA"
 "CUDA0","DUP","type=f16,ne=[10,10,5,1],permute=[1,0,2,3]","support","1","yes","CUDA"
-"CUDA0","DUP","type=i16,ne=[10,8,3,1],permute=[0,2,1,3]","support","1","yes","CUDA"
-"CUDA0","DUP","type=i16,ne=[10,8,3,1],permute=[1,2,0,3]","support","1","yes","CUDA"
+"CUDA0","DUP","type=i16,ne=[10,8,3,1],permute=[0,2,1,3]","support","0","no","CUDA"
+"CUDA0","DUP","type=i16,ne=[10,8,3,1],permute=[1,2,0,3]","support","0","no","CUDA"
 "CUDA0","SET","type_src=f32,type_dst=f32,ne=[6,5,4,3],dim=1","support","1","yes","CUDA"
 "CUDA0","SET","type_src=f32,type_dst=f32,ne=[6,5,4,3],dim=2","support","1","yes","CUDA"
 "CUDA0","SET","type_src=f32,type_dst=f32,ne=[6,5,4,3],dim=3","support","1","yes","CUDA"
@@ -1,7 +1,5 @@
-if (GGML_CPU)
-    set(TARGET llama-convert-llama2c-to-ggml)
-    add_executable(${TARGET} convert-llama2c-to-ggml.cpp)
-    install(TARGETS ${TARGET} RUNTIME)
-    target_link_libraries(${TARGET} PRIVATE llama-common llama ${CMAKE_THREAD_LIBS_INIT})
-    target_compile_features(${TARGET} PRIVATE cxx_std_17)
-endif()
+set(TARGET llama-convert-llama2c-to-ggml)
+add_executable(${TARGET} convert-llama2c-to-ggml.cpp)
+install(TARGETS ${TARGET} RUNTIME)
+target_link_libraries(${TARGET} PRIVATE llama-common llama ${CMAKE_THREAD_LIBS_INIT})
+target_compile_features(${TARGET} PRIVATE cxx_std_17)
@@ -18,7 +18,7 @@ if(LLAMA_BUILD_TESTS)
        -DDEST=${MODEL_DEST}
        -DNAME=${MODEL_NAME}
        -DHASH=${MODEL_HASH}
-        -P ${PROJECT_SOURCE_DIR}/cmake/download-models.cmake
+        -P ${CMAKE_SOURCE_DIR}/cmake/download-models.cmake
    )
    set_tests_properties(${TEST_TARGET}-download-model PROPERTIES FIXTURES_SETUP ${TEST_TARGET}-download-model)
    add_test(NAME ${TEST_TARGET} COMMAND llama-eval-callback -m "${MODEL_DEST}" --prompt hello --seed 42 -ngl 0)
@@ -1,4 +1,3 @@
 llama-build-install
 install
 build
-build-subdir
@@ -3,20 +3,11 @@ project(llama-simple)

 set(CMAKE_CXX_STANDARD 17)

-option(LLAMA_TEST_USE_SUBDIR "Use add_subdirectory instead of find_package" OFF)
-
-if(LLAMA_TEST_USE_SUBDIR)
-    add_subdirectory(../../ llama.cpp)
-else()
-    find_package(llama 0.1.0 REQUIRED)
-endif()
+find_package(llama 0.1.0 REQUIRED)

 add_executable(test-cmake test-cmake.cpp)
 target_link_libraries(test-cmake PRIVATE llama)
-
-if(DEFINED LLAMA_BUILD_NUMBER)
-    target_compile_definitions(test-cmake PRIVATE
-        LLAMA_BUILD_NUMBER=${LLAMA_BUILD_NUMBER}
-        LLAMA_BUILD_COMMIT="${LLAMA_BUILD_COMMIT}"
-    )
-endif()
+target_compile_definitions(test-cmake PRIVATE
+    LLAMA_BUILD_NUMBER=${LLAMA_BUILD_NUMBER}
+    LLAMA_BUILD_COMMIT="${LLAMA_BUILD_COMMIT}"
+)
@@ -5,18 +5,17 @@ enable troubleshooting issues and exploration. The idea is that this can be used
 after making changes to llama.cpp installation cmake configuration and then
 verify it locally.

-### find_package
-The following will configure, build, and install llama.cpp, and the build a
-project that uses find_package to use the installation.
+### Usage
+The following will configure, build, and install llama.cpp

 Configuring/build/install:
 ```console
 ./build-install.sh
 ```
 The above command will create a directory named `install` in the current directory
-which will have the following files in its lib directory:
+which will have the follwing files in its lib directory:
 ```console
-$ ls install/lib/
+(venv) $ ls install/lib/
 cmake                   libggml.so          libllama-common.so.0      libllama.so.0.1.0  llama.cpp
 libggml-base.so         libggml.so.0        libllama-common.so.0.1.0  libmtmd.so         pkgconfig
 libggml-base.so.0       libggml.so.0.19.0   libllama.so               libmtmd.so.0
@@ -25,7 +24,7 @@ libggml-base.so.0.19.0  libllama-common.so  libllama.so.0             libmtmd.so

 Build/run this project using the installation created above:
 ```console
-$ ./build.sh
+(venv) $ ./build.sh
 -- Configuring done (0.0s)
 -- Generating done (0.0s)
 -- Build files have been written to: /path/to/llama.cpp/examples/test-cmake/build
@@ -35,11 +34,3 @@ $ ./build.sh
 load_backend: loaded CPU backend from /path/to/llama.cpp/examples/test-cmake/install/lib/llama.cpp/libggml-cpu-alderlake.so
 [test-cmake] Backend initialized.
 ```
-
-### add_subdirectory
-The following will use add_subdirectory to include llama.cpp in a cmake project
-and is intended to simulate projects that build llama.cpp in this way.
-
-```console
-$ USE_SUBDIR=ON ./build.sh
-```
@@ -2,17 +2,6 @@

 set -e

-if [ "${USE_SUBDIR:-OFF}" = "ON" ]; then
-    BUILD_DIR="build-subdir"
-    CMAKE_ARGS="-DLLAMA_TEST_USE_SUBDIR=ON -DLLAMA_BUILD_COMMON=ON -DLLAMA_BUILD_TOOLS=ON -DLLAMA_BUILD_SERVER=ON-DLLAMA_BUILD_TESTS=ON"
-    LIB_PATH="${PWD}/${BUILD_DIR}/bin"
-else
-    BUILD_DIR="build"
-    CMAKE_ARGS="-DCMAKE_PREFIX_PATH=${PWD}/install"
-    LIB_PATH="${PWD}/install/lib/llama.cpp"
-fi
-
-cmake --fresh -S . -B "${BUILD_DIR}" ${CMAKE_ARGS}
-cmake --build "${BUILD_DIR}" -j 8
-
-LD_LIBRARY_PATH="${LIB_PATH}:${PWD}/install/lib${LD_LIBRARY_PATH:+:$LD_LIBRARY_PATH}" "./${BUILD_DIR}/test-cmake"
+cmake -S . -B build -DCMAKE_PREFIX_PATH="${PWD}/install"
+cmake --build build
+LD_LIBRARY_PATH="${PWD}/install/lib/llama.cpp:${PWD}/install/lib${LD_LIBRARY_PATH:+:$LD_LIBRARY_PATH}" ./build/test-cmake
@@ -2,12 +2,8 @@
 #include <cstdio>

 int main(void) {
-#ifdef LLAMA_BUILD_NUMBER
    printf("[test-cmake] llama.cpp version: %s, build: %d (%s)\n",
           llama_version(), LLAMA_BUILD_NUMBER, LLAMA_BUILD_COMMIT);
-#else
-    printf("[test-cmake] llama.cpp version: %s\n", llama_version());
-#endif
    printf("[test-cmake] ggml version: %s, commit: %s\n", ggml_version(), ggml_commit());
    printf("[test-cmake] Initializing backend...\n");
    llama_backend_init();
@@ -4,7 +4,7 @@ project("ggml" C CXX ASM)

 ### GGML Version
 set(GGML_VERSION_MAJOR 0)
-set(GGML_VERSION_MINOR 24)
+set(GGML_VERSION_MINOR 23)
 set(GGML_VERSION_PATCH 0)
 set(GGML_VERSION_BASE "${GGML_VERSION_MAJOR}.${GGML_VERSION_MINOR}.${GGML_VERSION_PATCH}")

@@ -6,7 +6,7 @@
 extern "C" {
 #endif

-#define RPC_PROTO_MAJOR_VERSION    7
+#define RPC_PROTO_MAJOR_VERSION    6
 #define RPC_PROTO_MINOR_VERSION    0
 #define RPC_PROTO_PATCH_VERSION    0

@@ -25,7 +25,7 @@ GGML_BACKEND_API bool ggml_backend_is_sycl(ggml_backend_t backend);
 GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_sycl_buffer_type(int device);

 // split tensor buffer that splits matrices by rows across multiple devices
-GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_sycl_split_buffer_type([[maybe_unused]] int main_device, const float * tensor_split);
+GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_sycl_split_buffer_type(const float * tensor_split);

 // Tensor parallelism (--split-mode tensor): comm_init/free/allreduce_tensor
 // trio queried by the meta-backend via ggml_backend_reg_get_proc_address.
@@ -2703,21 +2703,11 @@ extern "C" {
            struct ggml_tensor  * x,
            struct ggml_tensor  * weights);

-    // hc_pre with a per-element gate (Qwen3.8-Flash-Next): gate [n_embd, hc, n_tokens]
-    //   result[i, t] = scale*sum_h x[i, h, t]*sigmoid(gate[i, h, t])
-    //
-    GGML_API struct ggml_tensor * ggml_dsv4_hc_pre_gated(
-            struct ggml_context * ctx,
-            struct ggml_tensor  * x,
-            struct ggml_tensor  * gate,
-            float                 scale);
-
    // hc_post: x [n_embd, n_tokens], residual [n_embd, hc, n_tokens],
    //          post [hc, n_tokens], comb [dst_hc, src_hc, n_tokens]
    //          -> [n_embd, hc, n_tokens]
    //   result[i, dst, t] = x[i, t]*post[dst, t]
    //                       + sum_src residual[i, src, t]*comb[dst, src, t]
-    //   comb == NULL uses the identity: result[i, dst, t] = x[i, t]*post[dst, t] + residual[i, dst, t]
    //
    GGML_API struct ggml_tensor * ggml_dsv4_hc_post(
            struct ggml_context * ctx,
@@ -1630,10 +1630,7 @@ static bool ggml_backend_sched_alloc_splits(ggml_backend_sched_t sched) {
            ggml_backend_synchronize(sched->backends[i]);
        }

-        if (!ggml_gallocr_reserve_n(sched->galloc, &sched->graph, sched->node_backend_ids, sched->leaf_backend_ids)) {
-            GGML_LOG_ERROR("%s: failed to reserve graph buffers\n", __func__);
-            return false;
-        }
+        ggml_gallocr_reserve_n(sched->galloc, &sched->graph, sched->node_backend_ids, sched->leaf_backend_ids);
        if (!ggml_gallocr_alloc_graph(sched->galloc, &sched->graph)) {
            GGML_LOG_ERROR("%s: failed to allocate graph\n", __func__);
            return false;
@@ -675,6 +675,12 @@ function(ggml_add_cpu_backend_variant_impl tag_name)
    target_compile_options(${GGML_CPU_NAME} PRIVATE ${ARCH_FLAGS})
    target_compile_definitions(${GGML_CPU_NAME} PRIVATE ${ARCH_DEFINITIONS})

+    if (CMAKE_C_COMPILER_ID STREQUAL "GNU" AND NOT GGML_SYSTEM_ARCH STREQUAL "x86")
+        message(STATUS "Skipping PCH for ${GGML_CPU_NAME}: GCC PCH is only enabled for x86 (arch: ${GGML_SYSTEM_ARCH})")
+    else()
+        target_precompile_headers(${GGML_CPU_NAME} PRIVATE ggml-impl.h)
+    endif()
+
    if (EMSCRIPTEN)
        set_target_properties(${GGML_CPU_NAME} PROPERTIES COMPILE_FLAGS "-msimd128")
    endif()
@@ -417,7 +417,6 @@ void ggml_vec_dot_mxfp4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const vo
    sumf = vec_hsum_f32x4(v_acc);
    *s = sumf;
 #else
-    UNUSED(nb);
    UNUSED(x);
    UNUSED(y);
    UNUSED(ib);
@@ -70,7 +70,6 @@ void ggml_quantize_mat_q8_0_4x4(const float * GGML_RESTRICT x, void * GGML_RESTR
 #endif
 }

-#if defined(__VXE__) || defined(__VXE2__)
 static inline int16x8_t vxe_dot_acc(const int8x16_t v_x, const int8x16_t v_y, const int16x8_t v_acc) {
    return vec_meadd(v_x, v_y, vec_moadd(v_x, v_y, v_acc));
 }
@@ -85,7 +84,6 @@ static inline int32x4_t vxe_fold(const int16x8_t v_sumi) {
    const int16x8_t v_ones = vec_splats((int16_t)1);
    return vec_add(vec_mule(v_sumi, v_ones), vec_mulo(v_sumi, v_ones));
 }
-#endif

 void ggml_gemv_q4_0_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
    const int qk = QK8_0;
@@ -11259,19 +11259,10 @@ static void ggml_compute_forward_dsv4_hc_pre_f32(
    const int64_t hc       = x->ne[1];
    const int64_t n_tokens = x->ne[2];

-    const float scale = ggml_get_op_params_f32(dst, 0);
-    const bool  gated = ggml_get_op_params_i32(dst, 1) != 0;
-
    GGML_ASSERT(dst->ne[0] == n_embd);
    GGML_ASSERT(dst->ne[1] == n_tokens);
-    if (gated) {
-        GGML_ASSERT(weights->ne[0] == n_embd);
-        GGML_ASSERT(weights->ne[1] == hc);
-        GGML_ASSERT(weights->ne[2] == n_tokens);
-    } else {
-        GGML_ASSERT(weights->ne[0] == hc);
-        GGML_ASSERT(weights->ne[1] == n_tokens);
-    }
+    GGML_ASSERT(weights->ne[0] == hc);
+    GGML_ASSERT(weights->ne[1] == n_tokens);

    GGML_TENSOR_LOCALS(size_t, nbx, x,       nb);
    GGML_TENSOR_LOCALS(size_t, nbw, weights, nb);
@@ -11291,18 +11282,12 @@ static void ggml_compute_forward_dsv4_hc_pre_f32(

        float sum = 0.0f;
        for (int64_t ih = 0; ih < hc; ++ih) {
-            const float xv = *(const float *) ((const char *) x->data + i0*nbx0 + ih*nbx1 + it*nbx2);
-            float wv;
-            if (gated) {
-                const float gv = *(const float *) ((const char *) weights->data + i0*nbw0 + ih*nbw1 + it*nbw2);
-                wv = 1.0f / (1.0f + expf(-gv));
-            } else {
-                wv = *(const float *) ((const char *) weights->data + ih*nbw0 + it*nbw1);
-            }
+            const float xv = *(const float *) ((const char *) x->data       + i0*nbx0 + ih*nbx1 + it*nbx2);
+            const float wv = *(const float *) ((const char *) weights->data + ih*nbw0 + it*nbw1);
            sum += xv * wv;
        }

-        *(float *) ((char *) dst->data + i0*nbd0 + it*nbd1) = scale * sum;
+        *(float *) ((char *) dst->data + i0*nbd0 + it*nbd1) = sum;
    }
 }

@@ -11336,6 +11321,7 @@ static void ggml_compute_forward_dsv4_hc_post_f32(
    GGML_ASSERT(x->type == GGML_TYPE_F32);
    GGML_ASSERT(residual->type == GGML_TYPE_F32);
    GGML_ASSERT(post->type == GGML_TYPE_F32);
+    GGML_ASSERT(comb->type == GGML_TYPE_F32);
    GGML_ASSERT(dst->type == GGML_TYPE_F32);

    const int64_t n_embd   = x->ne[0];
@@ -11349,24 +11335,14 @@ static void ggml_compute_forward_dsv4_hc_post_f32(
    GGML_ASSERT(residual->ne[2] == n_tokens);
    GGML_ASSERT(post->ne[0] == hc);
    GGML_ASSERT(post->ne[1] == n_tokens);
-
-    // comb == NULL: identity mixing, each stream keeps its own residual
-    size_t nbc0 = 0;
-    size_t nbc1 = 0;
-    size_t nbc2 = 0;
-    if (comb) {
-        GGML_ASSERT(comb->type == GGML_TYPE_F32);
-        GGML_ASSERT(comb->ne[0] == hc);
-        GGML_ASSERT(comb->ne[1] == hc);
-        GGML_ASSERT(comb->ne[2] == n_tokens);
-        nbc0 = comb->nb[0];
-        nbc1 = comb->nb[1];
-        nbc2 = comb->nb[2];
-    }
+    GGML_ASSERT(comb->ne[0] == hc);
+    GGML_ASSERT(comb->ne[1] == hc);
+    GGML_ASSERT(comb->ne[2] == n_tokens);

    GGML_TENSOR_LOCALS(size_t, nbx, x,        nb);
    GGML_TENSOR_LOCALS(size_t, nbr, residual, nb);
    GGML_TENSOR_LOCALS(size_t, nbp, post,     nb);
+    GGML_TENSOR_LOCALS(size_t, nbc, comb,     nb);
    GGML_TENSOR_LOCALS(size_t, nbd, dst,      nb);

    const int ith = params->ith;
@@ -11386,14 +11362,10 @@ static void ggml_compute_forward_dsv4_hc_post_f32(
        const float pv = *(const float *) ((const char *) post->data + idst*nbp0 + it*nbp1);

        float sum = xv * pv;
-        if (comb) {
-            for (int64_t isrc = 0; isrc < hc; ++isrc) {
-                const float rv = *(const float *) ((const char *) residual->data + i0*nbr0 + isrc*nbr1 + it*nbr2);
-                const float cv = *(const float *) ((const char *) comb->data     + idst*nbc0 + isrc*nbc1 + it*nbc2);
-                sum += rv * cv;
-            }
-        } else {
-            sum += *(const float *) ((const char *) residual->data + i0*nbr0 + idst*nbr1 + it*nbr2);
+        for (int64_t isrc = 0; isrc < hc; ++isrc) {
+            const float rv = *(const float *) ((const char *) residual->data + i0*nbr0 + isrc*nbr1 + it*nbr2);
+            const float cv = *(const float *) ((const char *) comb->data     + idst*nbc0 + isrc*nbc1 + it*nbc2);
+            sum += rv * cv;
        }

        *(float *) ((char *) dst->data + i0*nbd0 + idst*nbd1 + it*nbd2) = sum;
@@ -5,10 +5,10 @@
 //
 // cache line
 //
-// TODO: rework CACHE_LINE_SIZE so std::hardware_destructive_interference_size
-// can be used consistently between C and C++ TUs; the previous macro form
-// diverged based on include order and undersized the work buffer.
-// ref: https://github.com/ggml-org/llama.cpp/pull/28882
+
+#if defined(__cpp_lib_hardware_interference_size)
+#define CACHE_LINE_SIZE std::hardware_destructive_interference_size
+#else
 #if defined(__POWER9_VECTOR__)
 #define CACHE_LINE_SIZE 128
 #elif defined(__VXE__) || defined(__VXE2__)
@@ -16,8 +16,17 @@
 #else
 #define CACHE_LINE_SIZE 64
 #endif
+#endif

+// -Winterference-size was introduced in GCC 12
+#if defined(__cplusplus) && defined(__GNUC__) && !defined(__clang__) && __GNUC__ >= 12
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Winterference-size"
+#endif
 static const size_t CACHE_LINE_SIZE_F32 = CACHE_LINE_SIZE/sizeof(float);
+#if defined(__cplusplus) && defined(__GNUC__) && !defined(__clang__) && __GNUC__ >= 12
+#pragma GCC diagnostic pop
+#endif

 // Work buffer size for im2col operations in CONV2D
 #define GGML_IM2COL_WORK_SIZE (16 * 1024 * 1024)
@@ -639,7 +639,7 @@ static void permute_transpose_impl(const ggml_tensor * src0,
        }
    } else if (n_src_stride == sizeof(int16_t)) {
        for (int64_t bi = ith; bi < batch; bi += nth) {
-            rvv_transposed_s16_mn_to_nm((int8_t *) ((char *) dst->data + bi * batch_stride), n_dst_stride,
+            rvv_transposed_s32_mn_to_nm((int8_t *) ((char *) dst->data + bi * batch_stride), n_dst_stride,
                                        (int8_t *) ((char *) src0->data + bi * batch_stride), m_src_stride, m, n);
        }
    } else {
@@ -1,6 +1,6 @@
 #include "allreduce.cuh"

-#if !defined(GGML_USE_MUSA)
+#if !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)

 #include "convert.cuh"
 #include "ggml-impl.h"
@@ -11,12 +11,11 @@
 #include <limits>

 // ---------------------------------------------------------------------------
-// AllReduce for tensor-parallel inference across two GPUs (CUDA or
-// ROCm/HIP).
+// CUDA AllReduce for tensor-parallel inference across two GPUs.
 //
-// Provides an in-place sum reduction over matching tensors on two GPUs
-// in the same process.  Used by the tensor-split path alongside NCCL;
-// targets setups without NVLink/xGMI, where data is exchanged between the
+// Provides an in-place sum reduction over matching tensors on two CUDA
+// devices in the same process.  Used by the tensor-split path alongside
+// NCCL; targets setups without NVLink, where data is exchanged between the
 // GPUs by staging it through pinned host memory over PCIe.
 //
 // Two reduction strategies are selected per call by tensor size:
@@ -162,14 +161,11 @@ static __global__ void ggml_cuda_ar_kernel(
        __threadfence_system(); // make our signal visible system-wide

        while (ggml_cuda_ar_signal_get(other_slot) != token) {
-#ifdef GGML_USE_HIP
-            // Equals ~100ns at 2500 MHz (sleeps for n * [1,64] clock cycles)
-            __builtin_amdgcn_s_sleep(4);
-#elif __CUDA_ARCH__ >= GGML_CUDA_CC_VOLTA
+#if __CUDA_ARCH__ >= GGML_CUDA_CC_VOLTA
            __nanosleep(100);
 #else
            NO_DEVICE_CODE;
-#endif // GGML_USE_HIP
+#endif // __CUDA_ARCH__ >= GGML_CUDA_CC_VOLTA
        }
    }

@@ -284,7 +280,7 @@ struct ggml_cuda_ar_host_mapping {
        }
        rc = cudaHostGetDevicePointer(reinterpret_cast<void **>(&dev), host, 0);
        if (rc != cudaSuccess) {
-            CUDA_CHECK(cudaFreeHost(host));
+            cudaFreeHost(host);
            host = nullptr;
            dev  = nullptr;
        }
@@ -293,7 +289,7 @@ struct ggml_cuda_ar_host_mapping {

    void free() {
        if (host) {
-            CUDA_CHECK(cudaFreeHost(host));
+            cudaFreeHost(host);
            host = nullptr;
            dev  = nullptr;
        }
@@ -405,8 +401,7 @@ ggml_cuda_ar_pipeline * ggml_cuda_ar_pipeline_init(const int * devices, size_t n
        return nullptr;
    }

-    // The chunked kernel uses __nanosleep (NVIDIA, sm70+) or
-    // __builtin_amdgcn_s_sleep (AMD).
+    // The chunked kernel uses __nanosleep, which is sm70+ (Volta+).
    for (size_t i = 0; i < n_devices; ++i) {
        const int cc = ggml_cuda_info().devices[devices[i]].cc;
        if (cc < GGML_CUDA_CC_VOLTA) {
@@ -548,7 +543,7 @@ void ggml_cuda_ar_pipeline_free(ggml_cuda_ar_pipeline * p) {
    for (int i = 0; i < p->n_devices; ++i) {
        if (p->streams[i]) {
            ggml_cuda_set_device(p->devices[i]);
-            CUDA_CHECK(cudaStreamSynchronize(p->streams[i]));
+            cudaStreamSynchronize(p->streams[i]);
        }
    }

@@ -557,28 +552,28 @@ void ggml_cuda_ar_pipeline_free(ggml_cuda_ar_pipeline * p) {
        p->host_large[i].free();
        if (p->dev_tmp[i]) {
            ggml_cuda_set_device(p->devices[i]);
-            CUDA_CHECK(cudaFree(p->dev_tmp[i]));
+            cudaFree(p->dev_tmp[i]);
        }
        ggml_cuda_set_device(p->devices[i]);
        for (int s = 0; s < GGML_CUDA_AR_POOL_SIZE; ++s) {
-            if (p->ev_pool[i][s].app) { CUDA_CHECK(cudaEventDestroy(p->ev_pool[i][s].app)); }
+            if (p->ev_pool[i][s].app) { cudaEventDestroy(p->ev_pool[i][s].app); }
            for (int c = 0; c < GGML_CUDA_AR_COPY_MAX_CHUNKS; ++c) {
-                if (p->ev_pool[i][s].cpy[c]) { CUDA_CHECK(cudaEventDestroy(p->ev_pool[i][s].cpy[c])); }
+                if (p->ev_pool[i][s].cpy[c]) { cudaEventDestroy(p->ev_pool[i][s].cpy[c]); }
            }
-            if (p->ev_pool[i][s].h2d) { CUDA_CHECK(cudaEventDestroy(p->ev_pool[i][s].h2d)); }
-            if (p->ev_pool[i][s].ker) { CUDA_CHECK(cudaEventDestroy(p->ev_pool[i][s].ker)); }
+            if (p->ev_pool[i][s].h2d) { cudaEventDestroy(p->ev_pool[i][s].h2d); }
+            if (p->ev_pool[i][s].ker) { cudaEventDestroy(p->ev_pool[i][s].ker); }
        }
        if (p->host_large_read_done[i]) {
            ggml_cuda_set_device(p->devices[i]);
-            CUDA_CHECK(cudaEventDestroy(p->host_large_read_done[i]));
+            cudaEventDestroy(p->host_large_read_done[i]);
        }
        if (p->dev_tmp_kernel_done[i]) {
            ggml_cuda_set_device(p->devices[i]);
-            CUDA_CHECK(cudaEventDestroy(p->dev_tmp_kernel_done[i]));
+            cudaEventDestroy(p->dev_tmp_kernel_done[i]);
        }
        if (p->streams[i]) {
            ggml_cuda_set_device(p->devices[i]);
-            CUDA_CHECK(cudaStreamDestroy(p->streams[i]));
+            cudaStreamDestroy(p->streams[i]);
        }
    }
    p->arrival.free();
@@ -957,14 +952,13 @@ bool ggml_cuda_ar_allreduce(
    return ok;
 }

-#else // defined(GGML_USE_MUSA)
+#else // defined(GGML_USE_HIP) || defined(GGML_USE_MUSA)

-// MUSA lacks the host-mapped pinned-memory APIs (cudaHostAllocPortable
-// / cudaHostAllocMapped / cudaHostGetDevicePointer) and a device-side
-// sleep intrinsic that this implementation relies on, so the internal
-// AllReduce is unavailable there. The dispatcher in ggml-cuda.cu treats
-// a nullptr pipeline as "init failed" and silently falls back to the meta
-// backend's generic AllReduce.
+// HIP and MUSA lack the host-mapped pinned-memory APIs (cudaHostAllocPortable
+// / cudaHostAllocMapped / cudaHostGetDevicePointer) and __nanosleep that this
+// implementation relies on, so the internal AllReduce is a CUDA-only feature.
+// The dispatcher in ggml-cuda.cu treats a nullptr pipeline as "init failed"
+// and silently falls back to the meta backend's generic AllReduce.
 ggml_cuda_ar_pipeline * ggml_cuda_ar_pipeline_init(const int *, size_t) {
    return nullptr;
 }
@@ -974,4 +968,4 @@ bool ggml_cuda_ar_allreduce(ggml_cuda_ar_pipeline *, ggml_backend_t *, ggml_tens
    return false;
 }

-#endif // !defined(GGML_USE_MUSA)
+#endif // !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)
@@ -9,7 +9,7 @@
 struct ggml_cuda_ar_pipeline;

 // Allocate a pipeline for n_devices GPUs.
-// devices[] holds the GPU device IDs in rank order.
+// devices[] holds the CUDA device IDs in rank order.
 // Returns nullptr on allocation failure.
 ggml_cuda_ar_pipeline * ggml_cuda_ar_pipeline_init(
    const int * devices, size_t n_devices);
@@ -329,12 +329,6 @@ static bool fp16_mma_hardware_available(const int cc) {
        (GGML_CUDA_CC_IS_MTHREADS(cc) && cc >= GGML_CUDA_CC_QY2);
 }

-// To be used for feature selection of external libraries, e.g. cuBLAS.
-static bool fast_bf16_hardware_available(const int cc) {
-        return (GGML_CUDA_CC_IS_AMD(cc) && (cc >= GGML_CUDA_CC_RDNA3 || GGML_CUDA_CC_IS_CDNA(cc)))
-            || (GGML_CUDA_CC_IS_NVIDIA(cc) && cc >= GGML_CUDA_CC_AMPERE);
-}
-
 static bool bf16_mma_hardware_available(const int cc) {
    return (GGML_CUDA_CC_IS_NVIDIA(cc) && cc >= GGML_CUDA_CC_AMPERE) ||
        GGML_CUDA_CC_IS_CDNA(cc) || cc >= GGML_CUDA_CC_RDNA3 ||
@@ -589,14 +589,6 @@ void ggml_cuda_cpy(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, gg
            ggml_cpy_scalar_cuda<int32_t, int32_t>
                (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
        }
-    } else if (src0->type == GGML_TYPE_I16 && src1->type == GGML_TYPE_I16) {
-        if (can_be_transposed) {
-            ggml_cpy_scalar_cuda<int16_t, int16_t, true>
-                (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
-        } else {
-            ggml_cpy_scalar_cuda<int16_t, int16_t>
-                (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
-        }
    } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_I32) {
        if (contiguous_srcs) {
            ggml_cpy_scalar_contiguous_cuda<float, int32_t>
@@ -100,7 +100,6 @@ static __global__ void dsv4_hc_comb_f32(
    }
 }

-template <bool gated>
 static __global__ void dsv4_hc_pre_f32(
        const float * x,
        const float * weights,
@@ -113,10 +112,8 @@ static __global__ void dsv4_hc_pre_f32(
        int64_t sx2,
        int64_t sw0,
        int64_t sw1,
-        int64_t sw2,
        int64_t sd0,
-        int64_t sd1,
-        float   scale) {
+        int64_t sd1) {
    ggml_cuda_pdl_lc();
    const int64_t ir = (int64_t) blockIdx.x * blockDim.x + threadIdx.x;
    const int64_t nr = n_embd * n_tokens;
@@ -130,22 +127,16 @@ static __global__ void dsv4_hc_pre_f32(
    const int64_t i0 = ir % n_embd;
    const int64_t it = ir / n_embd;

-    float sum = 0.0f;
-    for (int64_t ih = 0; ih < hc; ++ih) {
+    float sum = x[i0*sx0 + it*sx2] * weights[it*sw1];
+    for (int64_t ih = 1; ih < hc; ++ih) {
        const float xv = x[i0*sx0 + ih*sx1 + it*sx2];
-        float wv;
-        if constexpr (gated) {
-            wv = 1.0f / (1.0f + expf(-weights[i0*sw0 + ih*sw1 + it*sw2]));
-        } else {
-            wv = weights[ih*sw0 + it*sw1];
-        }
+        const float wv = weights[ih*sw0 + it*sw1];
        sum += xv * wv;
    }

-    dst[i0*sd0 + it*sd1] = scale * sum;
+    dst[i0*sd0 + it*sd1] = sum;
 }

-template <bool has_comb>
 static __global__ void dsv4_hc_post_f32(
        const float * x,
        const float * residual,
@@ -183,12 +174,8 @@ static __global__ void dsv4_hc_post_f32(
    const int64_t it   = ir / (n_embd * hc);

    float sum = x[i0*sx0 + it*sx1] * post[idst*sp0 + it*sp1];
-    if constexpr (has_comb) {
-        for (int64_t isrc = 0; isrc < hc; ++isrc) {
-            sum += residual[i0*sr0 + isrc*sr1 + it*sr2] * comb[idst*sc0 + isrc*sc1 + it*sc2];
-        }
-    } else {
-        sum += residual[i0*sr0 + idst*sr1 + it*sr2];
+    for (int64_t isrc = 0; isrc < hc; ++isrc) {
+        sum += residual[i0*sr0 + isrc*sr1 + it*sr2] * comb[idst*sc0 + isrc*sc1 + it*sc2];
    }

    dst[i0*sd0 + idst*sd1 + it*sd2] = sum;
@@ -253,23 +240,18 @@ void ggml_cuda_op_dsv4_hc_pre(ggml_backend_cuda_context & ctx, ggml_tensor * dst
    const int64_t hc       = x->ne[1];
    const int64_t n_tokens = x->ne[2];

-    const float scale = ggml_get_op_params_f32(dst, 0);
-    const bool  gated = ggml_get_op_params_i32(dst, 1) != 0;
-
    const int block_size = 256;
    const int64_t nr = n_embd * n_tokens;
    const dim3 block_dims(block_size, 1, 1);
    const dim3 grid_dims((nr + block_size - 1) / block_size, 1, 1);
    const ggml_cuda_kernel_launch_params launch_params = ggml_cuda_kernel_launch_params(grid_dims, block_dims, 0, ctx.stream());

-    auto kernel = gated ? dsv4_hc_pre_f32<true> : dsv4_hc_pre_f32<false>;
-    ggml_cuda_kernel_launch(kernel, launch_params,
+    ggml_cuda_kernel_launch(dsv4_hc_pre_f32, launch_params,
            (const float *) x->data, (const float *) weights->data, (float *) dst->data,
            n_embd, hc, n_tokens,
            nbx0 / sizeof(float), nbx1 / sizeof(float), nbx2 / sizeof(float),
-            nbw0 / sizeof(float), nbw1 / sizeof(float), nbw2 / sizeof(float),
-            nbd0 / sizeof(float), nbd1 / sizeof(float),
-            scale);
+            nbw0 / sizeof(float), nbw1 / sizeof(float),
+            nbd0 / sizeof(float), nbd1 / sizeof(float));
 }

 void ggml_cuda_op_dsv4_hc_post(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
@@ -281,18 +263,15 @@ void ggml_cuda_op_dsv4_hc_post(ggml_backend_cuda_context & ctx, ggml_tensor * ds
    GGML_ASSERT(x->type == GGML_TYPE_F32);
    GGML_ASSERT(residual->type == GGML_TYPE_F32);
    GGML_ASSERT(post->type == GGML_TYPE_F32);
-    GGML_ASSERT(comb == nullptr || comb->type == GGML_TYPE_F32);
+    GGML_ASSERT(comb->type == GGML_TYPE_F32);
    GGML_ASSERT(dst->type == GGML_TYPE_F32);

    GGML_TENSOR_LOCALS(size_t, nbx, x,        nb);
    GGML_TENSOR_LOCALS(size_t, nbr, residual, nb);
    GGML_TENSOR_LOCALS(size_t, nbp, post,     nb);
+    GGML_TENSOR_LOCALS(size_t, nbc, comb,     nb);
    GGML_TENSOR_LOCALS(size_t, nbd, dst,      nb);

-    const size_t nbc0 = comb ? comb->nb[0] : 0;
-    const size_t nbc1 = comb ? comb->nb[1] : 0;
-    const size_t nbc2 = comb ? comb->nb[2] : 0;
-
    const int64_t n_embd   = x->ne[0];
    const int64_t n_tokens = x->ne[1];
    const int64_t hc       = residual->ne[1];
@@ -303,10 +282,9 @@ void ggml_cuda_op_dsv4_hc_post(ggml_backend_cuda_context & ctx, ggml_tensor * ds
    const dim3 grid_dims((nr + block_size - 1) / block_size, 1, 1);
    const ggml_cuda_kernel_launch_params launch_params = ggml_cuda_kernel_launch_params(grid_dims, block_dims, 0, ctx.stream());

-    auto kernel = comb ? dsv4_hc_post_f32<true> : dsv4_hc_post_f32<false>;
-    ggml_cuda_kernel_launch(kernel, launch_params,
+    ggml_cuda_kernel_launch(dsv4_hc_post_f32, launch_params,
            (const float *) x->data, (const float *) residual->data,
-            (const float *) post->data, comb ? (const float *) comb->data : nullptr, (float *) dst->data,
+            (const float *) post->data, (const float *) comb->data, (float *) dst->data,
            n_embd, hc, n_tokens,
            nbx0 / sizeof(float), nbx1 / sizeof(float),
            nbr0 / sizeof(float), nbr1 / sizeof(float), nbr2 / sizeof(float),
@@ -181,7 +181,7 @@ static constexpr __host__ __device__ fattn_mma_config ggml_cuda_fattn_mma_get_co
    GGML_CUDA_FATTN_MMA_CONFIG_CASE( 64,  64,  8, 128, 1,  64,  32,  32,  32, 1, true);
    GGML_CUDA_FATTN_MMA_CONFIG_CASE( 64,  64, 16, 256, 2,  64,  32,  32,  32, 1, true);
    GGML_CUDA_FATTN_MMA_CONFIG_CASE( 64,  64, 32, 256, 2,  64,  32,  32,  32, 1, true);
-    GGML_CUDA_FATTN_MMA_CONFIG_CASE( 64,  64, 64, 256, 3,  64,  32,  32,  32, 1, true);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE( 64,  64, 64, 256, 4,  64,  32,  32,  32, 1, true);

    GGML_CUDA_FATTN_MMA_CONFIG_CASE( 80,  80,  8, 256, 2,  64,  40,  40,  40, 1, true);
    GGML_CUDA_FATTN_MMA_CONFIG_CASE( 80,  80, 16, 256, 2,  64,  40,  40,  40, 1, true);
@@ -1141,7 +1141,7 @@ template<int DV, int ncols> struct mma_tile_sizes {
    using T_C_KQ  = tile<16, 16, float>; // column-major
    using T_A_VKQ = tile<16,  8, half2>; // row-major
    using T_B_VKQ = tile<16,  8, half2>; // column-major
-    using T_C_VKQ = tile<16, 16, float>; // column-major
+    using T_C_VKQ = tile<16,  8, half2>; // column-major
 };
 #else // Volta
 template<int DV, int ncols> struct mma_tile_sizes {
@@ -1227,9 +1227,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile(
    T_C_VKQ VKQ_C[cols_per_warp == 8 ? DV/T_C_VKQ::I : DV/(2*T_C_VKQ::J)];
 #elif defined(AMD_WMMA_AVAILABLE) && defined(RDNA3)
    T_C_VKQ VKQ_C[DV % 32 != 0       ? DV/T_C_VKQ::J : DV/(2*T_C_VKQ::J)];
-#elif defined(AMD_MFMA_AVAILABLE)
-    T_C_VKQ VKQ_C[                                     DV/T_C_VKQ::J];
-#elif defined(AMD_WMMA_AVAILABLE)
+#elif defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE)
    T_C_VKQ VKQ_C[                                     DV/(2*T_C_VKQ::J)];
 #else // Volta
    T_C_VKQ VKQ_C[                                     DV/(2*T_C_VKQ::J)];
@@ -1620,19 +1620,11 @@ static void ggml_cuda_mul_mat_cublas_impl(ggml_backend_cuda_context & ctx, const
 }

 static void ggml_cuda_mul_mat_cublas(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    const int cc = ggml_cuda_info().devices[ctx.device].cc;
    ggml_type compute_type = src0->type;
    if (ggml_is_quantized(compute_type)) {
-        compute_type = fast_fp16_hardware_available(cc) ? GGML_TYPE_F16 : GGML_TYPE_F32;
-    } else if (compute_type == GGML_TYPE_F16 && !fast_fp16_hardware_available(cc)) {
+        compute_type = fast_fp16_hardware_available(ggml_cuda_info().devices[ctx.device].cc) ? GGML_TYPE_F16 : GGML_TYPE_F32;
+    } else if (compute_type == GGML_TYPE_F16 && !fast_fp16_hardware_available(ggml_cuda_info().devices[ctx.device].cc)) {
        compute_type = GGML_TYPE_F32;
-    } else if (compute_type == GGML_TYPE_BF16 && !fast_bf16_hardware_available(cc)) {
-        if (GGML_CUDA_CC_IS_AMD(cc) && src1->ne[1] > 32) {
-            compute_type = GGML_TYPE_F32;
-        }
-        if (GGML_CUDA_CC_IS_NVIDIA(cc) && src1->ne[1] > (cc >= GGML_CUDA_CC_VOLTA ? 8 : 128)) {
-            compute_type = GGML_TYPE_F32;
-        }
    }
    if (dst->op_params[0] == GGML_PREC_F32) {
        compute_type = GGML_TYPE_F32;
@@ -5298,7 +5290,10 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
                return false;
            } break;
        case GGML_OP_DUP:
-                return true;
+            {
+                ggml_type src0_type = op->src[0]->type;
+                return src0_type != GGML_TYPE_I32 && src0_type != GGML_TYPE_I16;
+            } break;
        case GGML_OP_ARGMAX:
        case GGML_OP_COUNT_EQUAL:
            {
@@ -5466,9 +5461,7 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
            return true;
 #endif
        case GGML_OP_SUM_ROWS:
-            return op->src[0]->type == GGML_TYPE_F32 && op->type == GGML_TYPE_F32 && ggml_is_contiguous_rows(op->src[0]);
        case GGML_OP_MEAN:
-            return op->src[0]->type == GGML_TYPE_F32 && op->type == GGML_TYPE_F32 && ggml_is_contiguous_rows(op->src[0]);
        case GGML_OP_GROUP_NORM:
            return ggml_is_contiguous(op->src[0]);
        case GGML_OP_PAD:
@@ -5497,7 +5490,7 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
                op->type == GGML_TYPE_F32;
        case GGML_OP_DSV4_HC_POST:
            return op->src[0]->type == GGML_TYPE_F32 && op->src[1]->type == GGML_TYPE_F32 &&
-                op->src[2]->type == GGML_TYPE_F32 && (op->src[3] == nullptr || op->src[3]->type == GGML_TYPE_F32) &&
+                op->src[2]->type == GGML_TYPE_F32 && op->src[3]->type == GGML_TYPE_F32 &&
                op->type == GGML_TYPE_F32;
        case GGML_OP_FLASH_ATTN_EXT:
            return ggml_cuda_flash_attn_ext_supported(dev_ctx->device, op);
@@ -7,41 +7,40 @@ template <typename T>
 static  __global__ void im2col_kernel(
        const float * x, T * dst,
        int64_t IC, int64_t IW, int64_t IH, int64_t OH, int64_t OW, int64_t KW, int64_t KH,
-        int64_t N, int64_t IC_IH_IW, int64_t IH_IW, int64_t N_OH, int64_t KH_KW, int64_t IC_KH_KW,
+        int64_t IC_IH_IW, int64_t IH_IW, int64_t N_OH, int64_t KH_KW, int64_t IC_KH_KW,
        int s0, int s1, int p0, int p1, int d0, int d1) {
-    const int tid = threadIdx.x;
+    const int64_t i = threadIdx.x + blockIdx.x * blockDim.x;
+    if (i >= IC_KH_KW) {
+        return;
+    }

-    const int64_t total_channels = IC * KH * KW;
-    const int threads_per_pos = blockDim.x;
-    const int64_t start_ch = tid;
-    const int64_t stride_ch = threads_per_pos;
+    const int64_t iic = i / (KH_KW);
+    const int64_t rem = i - iic * KH_KW;
+    const int64_t ikh = rem / KW;
+    const int64_t ikw = rem - ikh * KW;

-    for (int64_t iow = blockIdx.x; iow < OW; iow += MAX_GRIDDIM_Y) {
-        for (int64_t iz = blockIdx.y; iz < N_OH; iz += MAX_GRIDDIM_Z) {
-            const int64_t in = iz / OH;
-            const int64_t ioh = iz - in * OH;
+    for (int64_t iow = blockIdx.y; iow < OW; iow += MAX_GRIDDIM_Y) {
+        for (int64_t iz = blockIdx.z; iz < N_OH; iz += MAX_GRIDDIM_Z) {
+            const int64_t  in = iz / OH;
+            const int64_t  ioh = iz - in * OH;

-            for (int64_t iic_khw = start_ch; iic_khw < total_channels; iic_khw += stride_ch) {
-                const int64_t iic = iic_khw / KH_KW;
-                const int64_t rem = iic_khw - iic * KH_KW;
-                const int64_t ikh = rem / KW;
-                const int64_t ikw = rem - ikh * KW;
+            const int64_t iiw = iow * s0 + ikw * d0 - p0;
+            const int64_t iih = ioh * s1 + ikh * d1 - p1;

-                const int64_t iiw = iow * s0 + ikw * d0 - p0;
-                const int64_t iih = ioh * s1 + ikh * d1 - p1;
+            const int64_t offset_dst =
+                ((in * OH + ioh) * OW + iow) * IC_KH_KW + iic * KH_KW + ikh * KW + ikw;

-                const int64_t offset_dst =
-                    ((in * OH + ioh) * OW + iow) * IC_KH_KW + iic * KH_KW + ikh * KW + ikw;
-
-                if (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW) {
-                    dst[offset_dst] = 0.0f;
-                } else {
-                    const int64_t offset_src = iic * IC_IH_IW + in * IH_IW;
-                    dst[offset_dst] = x[offset_src + iih * IW + iiw];
-                }
+            if (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW) {
+                dst[offset_dst] = 0.0f;
+            } else {
+                const int64_t offset_src = iic * IC_IH_IW + in * IH_IW;
+                dst[offset_dst] = x[offset_src + iih * IW + iiw];
            }
        }
    }
+
+    GGML_UNUSED(IC);
+    GGML_UNUSED(KH);
 }

 // im2col: [N, IC, IH, IW] => [N, OH, OW, IC*KH*KW]
@@ -51,15 +50,13 @@ static void im2col_cuda(const float * x, T* dst,
    int64_t N, int64_t IC_IH_IW, int64_t IH_IW,
    int s0,int s1,int p0,int p1,int d0,int d1, cudaStream_t stream) {
    const int64_t IC_KH_KW = IC * KH * KW;
+    const int64_t num_blocks = (IC_KH_KW + CUDA_IM2COL_BLOCK_SIZE - 1) / CUDA_IM2COL_BLOCK_SIZE;
    const int64_t N_OH = N * OH;
    const int64_t KH_KW = KW*KH;
-    const int threads_per_block = MIN((int)IC_KH_KW, CUDA_IM2COL_BLOCK_SIZE);
-    dim3 block_nums(MIN(OW, MAX_GRIDDIM_Y), MIN(N_OH, MAX_GRIDDIM_Z));
-
-    im2col_kernel<<<block_nums, threads_per_block, 0, stream>>>(
-        x, dst, IC, IW, IH, OH, OW, KW, KH,
-        N, IC_IH_IW, IH_IW, N_OH, KH_KW, IC_KH_KW,
-        s0, s1, p0, p1, d0, d1);
+    dim3 block_nums(num_blocks, MIN(OW, MAX_GRIDDIM_Y), MIN(N_OH, MAX_GRIDDIM_Z));
+    im2col_kernel<<<block_nums, MIN(IC_KH_KW, CUDA_IM2COL_BLOCK_SIZE) , 0, stream>>>(x, dst, IC, IW, IH, OH, OW, KW, KH,
+                                                                                     IC_IH_IW, IH_IW, N_OH, KH_KW, IC_KH_KW,
+                                                                                     s0, s1, p0, p1, d0, d1);
 }

 static void im2col_cuda_f16(const float * x, half * dst,
@@ -18,7 +18,7 @@ void ggml_cuda_op_mean(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {

    GGML_ASSERT(src0->type == GGML_TYPE_F32);
    GGML_ASSERT(dst->type == GGML_TYPE_F32);
-    GGML_ASSERT(ggml_is_contiguous_rows(src0));
+    GGML_ASSERT(ggml_is_contiguous(src0));

    const int64_t ncols = src0->ne[0];
    const int64_t nrows = ggml_nrows(src0);
@@ -65,20 +65,13 @@ void ggml_cuda_op_mean(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {

    // Heuristic for block size selection to optimize occupancy.
    // See discussion in: https://github.com/ggml-org/llama.cpp/pull/15132
-    dim3 block_dims;
    if ((nrows / nsm) < 2) {
-        block_dims = dim3(512, 1, 1);
+        const dim3 block_dims(512, 1, 1);
+        const ggml_cuda_kernel_launch_params launch_params = ggml_cuda_kernel_launch_params(block_nums, block_dims, 0, stream);
+        ggml_cuda_kernel_launch(reduce_rows_f32</*norm=*/true>, launch_params, src0_d, dst_d, ncols);
    } else {
-        block_dims = dim3(ncols < 1024 ? 32 : 128, 1, 1);
-    }
-    const ggml_cuda_kernel_launch_params launch_params = ggml_cuda_kernel_launch_params(block_nums, block_dims, 0, stream);
-
-    if (ggml_is_contiguous(src0)) {
+        const dim3 block_dims(ncols < 1024 ? 32 : 128, 1, 1);
+        const ggml_cuda_kernel_launch_params launch_params = ggml_cuda_kernel_launch_params(block_nums, block_dims, 0, stream);
        ggml_cuda_kernel_launch(reduce_rows_f32</*norm=*/true>, launch_params, src0_d, dst_d, ncols);
-        return;
    }
-
-    const char * src0_d_bytes = (const char *) src0->data;
-    ggml_cuda_kernel_launch(reduce_rows_f32_strided</*norm=*/true>, launch_params, src0_d_bytes, dst_d, ncols,
-            src0->ne[1], src0->ne[2], src0->nb[1], src0->nb[2], src0->nb[3]);
 }
@@ -247,7 +247,7 @@ void ggml_cuda_mul_mat_q(
    // Each expert only sees ne12*n_expert_used/ne02 tokens on average.
    // On RDNA3 and RDNA4 it is faster to pick the tile size against this value instead of ne12.
    int64_t ncols_opt = ne12;
-    if (GGML_CUDA_CC_IS_RDNA3(cc) || GGML_CUDA_CC_IS_RDNA4(cc)) {
+    if (GGML_CUDA_CC_IS_RDNA3_0(cc) || GGML_CUDA_CC_IS_RDNA4(cc)) {
        ncols_opt = (ne12*n_expert_used + ne02 - 1) / ne02;
    }

@@ -1,6 +1,11 @@
 #include "common.cuh"

-static __device__ __forceinline__ float reduce_row_f32(const float * x, const int ncols) {
+// Row reduction kernel template - compute sum (norm=false) or mean (norm=true)
+template <bool norm>
+static __global__ void reduce_rows_f32(const float * x_ptr, float * dst_ptr, const int ncols) {
+    const float * GGML_CUDA_RESTRICT x   = x_ptr;
+    float       * GGML_CUDA_RESTRICT dst = dst_ptr;
+    const int row = blockIdx.x;
    const int col = threadIdx.x;

    float     sum        = 0.0f;
@@ -12,7 +17,7 @@ static __device__ __forceinline__ float reduce_row_f32(const float * x, const in
    for (int i = col; i < ncols;) {
        for (int j = 0; j < num_unroll; ++j) {
            if (i < ncols) {
-                temp[j] = x[i];
+                temp[j] = x[row * ncols + i];
            } else {
                temp[j] = 0;
            }
@@ -30,40 +35,6 @@ static __device__ __forceinline__ float reduce_row_f32(const float * x, const in
    __shared__ float shared_vals[32];
    sum = block_reduce<block_reduce_method::SUM>(sum, shared_vals);

-    return sum;
-}
-
-// Row reduction kernel template - compute sum (norm=false) or mean (norm=true)
-template <bool norm>
-static __global__ void reduce_rows_f32(const float * x_ptr, float * dst_ptr, const int ncols) {
-    float       * GGML_CUDA_RESTRICT dst = dst_ptr;
-    const int64_t row = blockIdx.x;
-    const int col = threadIdx.x;
-
-    const float * GGML_CUDA_RESTRICT x = x_ptr + row*ncols;
-    const float sum = reduce_row_f32(x, ncols);
-
-    if (col != 0) {
-        return;
-    }
-
-    dst[row] = norm ? sum / ncols : sum;
-}
-
-template <bool norm>
-static __global__ void reduce_rows_f32_strided(const char * x_ptr, float * dst_ptr, const int ncols,
-        const int64_t ne1, const int64_t ne2, const int64_t nb1, const int64_t nb2, const int64_t nb3) {
-    float       * GGML_CUDA_RESTRICT dst = dst_ptr;
-    const int64_t row = blockIdx.x;
-    const int col = threadIdx.x;
-
-    const int64_t i1 = row % ne1;
-    const int64_t i2 = (row / ne1) % ne2;
-    const int64_t i3 = row / (ne1 * ne2);
-
-    const float * GGML_CUDA_RESTRICT x = (const float *) (x_ptr + i1*nb1 + i2*nb2 + i3*nb3);
-    const float sum = reduce_row_f32(x, ncols);
-
    if (col != 0) {
        return;
    }
@@ -24,30 +24,24 @@ void ggml_cuda_op_sum_rows(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {

    GGML_ASSERT(src0->type == GGML_TYPE_F32);
    GGML_ASSERT( dst->type == GGML_TYPE_F32);
-    GGML_ASSERT(ggml_is_contiguous_rows(src0));
+    GGML_ASSERT(ggml_is_contiguous(src0));

    const int64_t ncols = src0->ne[0];
    const int64_t nrows = ggml_nrows(src0);

-    if (ggml_is_contiguous(src0)) {
-        sum_rows_f32_cuda(src0_d, dst_d, ncols, nrows, stream);
-        return;
-    }
-
    const dim3 block_nums(nrows, 1, 1);

    const int id  = ggml_cuda_get_device();
    const int nsm = ggml_cuda_info().devices[id].nsm;
-    dim3 block_dims;
    if ((nrows / nsm) < 2) {
        // Increase num threads to 512 for small nrows to better hide the latency
-        block_dims = dim3(512, 1, 1);
+        const dim3 block_dims(512, 1, 1);
+        const ggml_cuda_kernel_launch_params launch_params = ggml_cuda_kernel_launch_params(block_nums, block_dims, 0, stream);
+        ggml_cuda_kernel_launch(reduce_rows_f32</*norm=*/false>, launch_params, src0_d, dst_d, ncols);
    } else {
        // Enough active SMs to hide latency, use smaller blocks to allow better scheduling
-        block_dims = dim3(ncols < 1024 ? 32 : 128, 1, 1);
+        const dim3 block_dims(ncols < 1024 ? 32 : 128, 1, 1);
+        const ggml_cuda_kernel_launch_params launch_params = ggml_cuda_kernel_launch_params(block_nums, block_dims, 0, stream);
+        ggml_cuda_kernel_launch(reduce_rows_f32</*norm=*/false>, launch_params, src0_d, dst_d, ncols);
    }
-    const ggml_cuda_kernel_launch_params launch_params = ggml_cuda_kernel_launch_params(block_nums, block_dims, 0, stream);
-    const char * src0_d_bytes = (const char *) src0->data;
-    ggml_cuda_kernel_launch(reduce_rows_f32_strided</*norm=*/false>, launch_params, src0_d_bytes, dst_d, ncols,
-            src0->ne[1], src0->ne[2], src0->nb[1], src0->nb[2], src0->nb[3]);
 }
@@ -73,10 +73,6 @@
 #define cudaGetDeviceProperties hipGetDeviceProperties
 #define cudaGetErrorString hipGetErrorString
 #define cudaGetLastError hipGetLastError
-#define cudaHostAlloc hipHostMalloc
-#define cudaHostAllocPortable hipHostMallocPortable
-#define cudaHostAllocMapped hipHostMallocMapped
-#define cudaHostGetDevicePointer hipHostGetDevicePointer
 #define cudaHostRegister hipHostRegister
 #define cudaHostRegisterPortable hipHostRegisterPortable
 #define cudaHostRegisterReadOnly hipHostRegisterReadOnly
@@ -21,7 +21,6 @@
 #include <queue>
 #include <deque>
 #include <algorithm>
-#include <cmath>

 #ifdef _WIN32
 #    define WIN32_LEAN_AND_MEAN
@@ -251,20 +250,7 @@ enum ggml_hexagon_tensor_flags {
 static inline bool ggml_hexagon_is_repack_type(enum ggml_type type) {
    return type == GGML_TYPE_Q4_0 || type == GGML_TYPE_Q4_1 ||
           type == GGML_TYPE_Q8_0 || type == GGML_TYPE_IQ4_NL ||
-           type == GGML_TYPE_MXFP4 || type == GGML_TYPE_Q6_K ||
-           type == GGML_TYPE_Q4_K;
-}
-
-// Size of one repacked row in the DSP tiled layout. The Q6_K and Q4_K tiles store uncompressed scales/mins,
-// so they are larger than the ggml blocks. For the other repack types the tile has the same size as the ggml blocks.
-static inline size_t ggml_hexagon_tiled_row_size(enum ggml_type type, int64_t ne0) {
-    if (type == GGML_TYPE_Q6_K) {
-        return (size_t) (ne0 / 32) * (HTP_MM_WEIGHT_TILE_SIZE_Q6_K / 32);
-    }
-    if (type == GGML_TYPE_Q4_K) {
-        return (size_t) (ne0 / 32) * (HTP_MM_WEIGHT_TILE_SIZE_Q4_1 / 32);
-    }
-    return ggml_row_size(type, ne0);
+           type == GGML_TYPE_MXFP4;
 }

 static inline bool ggml_hexagon_is_hmx_weight_type(enum ggml_type type) {
@@ -1316,377 +1302,6 @@ static void repack_tiled_mxfp4(void * data, const ggml_tensor * t, size_t offset
    }
 }

-// unsigned 6-bit value (0..63) of element e of a Q6_K block, same bit layout as dequantize_row_q6_K
-static inline uint8_t q6_K_get_quant(const block_q6_K * b, int e) {
-    const int c = e / 128;
-    const int w = e % 128;
-    const int g = w / 32;
-    const int l = w % 32;
-    const uint8_t * ql = b->ql + c * 64;
-    const uint8_t * qh = b->qh + c * 32;
-    uint8_t lo, hi;
-    switch (g) {
-        case 0:  lo = ql[l]      & 0xF; hi = (qh[l] >> 0) & 3; break;
-        case 1:  lo = ql[l + 32] & 0xF; hi = (qh[l] >> 2) & 3; break;
-        case 2:  lo = ql[l]      >> 4;  hi = (qh[l] >> 4) & 3; break;
-        default: lo = ql[l + 32] >> 4;  hi = (qh[l] >> 6) & 3; break;
-    }
-    return (uint8_t) (lo | (hi << 4));
-}
-
-// tile layout: see HTP_MM_WEIGHT_TILE_SIZE_Q6_K in htp/matmul-ops.h
-static void repack_q6_K_tiled(ggml_tensor * t, const void * data, size_t offset, size_t size) {
-    GGML_ASSERT(offset == 0);
-
-    const block_q6_K * src_matrix = (const block_q6_K *) data;
-    int64_t ne0 = t->ne[0];
-    int64_t ne1 = t->ne[1];
-    int64_t ne2 = t->ne[2];
-    int64_t ne3 = t->ne[3];
-    int64_t ne0_padded = hex_round_up(ne0, 32);
-    int64_t ne1_padded = hex_round_up(ne1, 32);
-
-    GGML_ASSERT(ne0 % QK_K == 0);
-
-    const int n_col_tiles = ne1_padded / 32;
-    const int n_k_tiles   = ne0_padded / 32;
-    const size_t tile_size   = HTP_MM_WEIGHT_TILE_SIZE_Q6_K;
-    const size_t matrix_size = (size_t) n_col_tiles * n_k_tiles * tile_size;
-
-    const int64_t sb_per_row = ne0 / QK_K;
-
-    for (int i3 = 0; i3 < ne3; i3++) {
-        for (int i2 = 0; i2 < ne2; i2++) {
-            const block_q6_K * src_slice = src_matrix + (i3 * ne2 + i2) * (ne1 * sb_per_row);
-            uint8_t * matrix_dst = (uint8_t *) t->data + (i3 * ne2 + i2) * matrix_size;
-
-            memset(matrix_dst, 0, matrix_size);  // padding rows and the OR-ed nibbles below need zeroed tiles
-
-            for (int64_t r = 0; r < ne1; r++) {
-                const int ct  = (int) (r / 32);
-                const int row = (int) (r % 32);
-                const block_q6_K * src_row = src_slice + r * sb_per_row;
-
-                for (int kt = 0; kt < n_k_tiles; kt++) {
-                    const int kt_local = kt % 8;  // k-tile within the super-block
-                    const block_q6_K * b = &src_row[kt / 8];
-                    const float d = GGML_FP16_TO_FP32(b->d);
-
-                    uint8_t * tile = matrix_dst + ((size_t) ct * n_k_tiles + kt) * tile_size;
-                    uint8_t * lo_pl = tile;
-                    uint8_t * hi_pl = tile + 512;
-                    ggml_half * sc_pl = (ggml_half *) (tile + 768);
-
-                    for (int lk = 0; lk < 32; lk++) {
-                        const uint8_t q6 = q6_K_get_quant(b, kt_local * 32 + lk);
-                        const int g   = lk >> 2;
-                        const int pos = row * 4 + (lk & 3);
-                        lo_pl[(g >> 1) * 128 + pos] |= (uint8_t) ((q6 & 0xF) << ((g & 1) * 4));
-                        hi_pl[(g >> 2) * 128 + pos] |= (uint8_t) ((q6 >> 4) << ((g & 3) * 2));
-                    }
-                    for (int sub = 0; sub < 2; sub++) {
-                        sc_pl[sub * 32 + row] = GGML_FP32_TO_FP16(d * (float) b->scales[kt_local * 2 + sub]);
-                    }
-                }
-            }
-        }
-    }
-
-    GGML_UNUSED(size);
-}
-
-// Reverse of repack_q6_K_tiled. Unpacks quants losslessly and normalizes sub-block scales. Read-back only.
-static void repack_tiled_q6_K(void * data, const ggml_tensor * t, size_t offset, size_t size) {
-    GGML_ASSERT(offset == 0);
-
-    block_q6_K * dst_matrix = (block_q6_K *) data;
-    int64_t ne0 = t->ne[0];
-    int64_t ne1 = t->ne[1];
-    int64_t ne2 = t->ne[2];
-    int64_t ne3 = t->ne[3];
-    int64_t ne0_padded = hex_round_up(ne0, 32);
-    int64_t ne1_padded = hex_round_up(ne1, 32);
-
-    GGML_ASSERT(ne0 % QK_K == 0);
-
-    const int n_col_tiles = ne1_padded / 32;
-    const int n_k_tiles   = ne0_padded / 32;
-    const size_t tile_size   = HTP_MM_WEIGHT_TILE_SIZE_Q6_K;
-    const size_t matrix_size = (size_t) n_col_tiles * n_k_tiles * tile_size;
-
-    const int64_t sb_per_row = ne0 / QK_K;
-
-    for (int i3 = 0; i3 < ne3; i3++) {
-        for (int i2 = 0; i2 < ne2; i2++) {
-            block_q6_K *    dst_slice  = dst_matrix + (i3 * ne2 + i2) * (ne1 * sb_per_row);
-            const uint8_t * matrix_src = (const uint8_t *) t->data + (i3 * ne2 + i2) * matrix_size;
-
-            for (int64_t r = 0; r < ne1; r++) {
-                const int ct  = (int) (r / 32);
-                const int row = (int) (r % 32);
-                block_q6_K * dst_row = dst_slice + r * sb_per_row;
-
-                for (int64_t sb = 0; sb < sb_per_row; sb++) {
-                    block_q6_K * b = &dst_row[sb];
-                    memset(b, 0, sizeof(block_q6_K));
-
-                    float sub_scales[16];
-                    for (int kt_local = 0; kt_local < 8; kt_local++) {
-                        const int kt = sb * 8 + kt_local;
-                        const uint8_t *   tile  = matrix_src + ((size_t) ct * n_k_tiles + kt) * tile_size;
-                        const uint8_t *   lo_pl = tile;
-                        const uint8_t *   hi_pl = tile + 512;
-                        const ggml_half * sc_pl = (const ggml_half *) (tile + 768);
-
-                        const int c = kt_local / 4;
-                        const int g = kt_local % 4;
-                        uint8_t * ql = b->ql + c * 64;
-                        uint8_t * qh = b->qh + c * 32;
-
-                        for (int lk = 0; lk < 32; lk++) {
-                            const int g_tile = lk >> 2;
-                            const int pos    = row * 4 + (lk & 3);
-                            const uint8_t lo = (lo_pl[(g_tile >> 1) * 128 + pos] >> ((g_tile & 1) * 4)) & 0xF;
-                            const uint8_t hi = (hi_pl[(g_tile >> 2) * 128 + pos] >> ((g_tile & 3) * 2)) & 3;
-
-                            switch (g) {
-                                case 0:
-                                    ql[lk]      |= lo;
-                                    qh[lk]      |= (hi << 0);
-                                    break;
-                                case 1:
-                                    ql[lk + 32] |= lo;
-                                    qh[lk]      |= (hi << 2);
-                                    break;
-                                case 2:
-                                    ql[lk]      |= (lo << 4);
-                                    qh[lk]      |= (hi << 4);
-                                    break;
-                                default:
-                                    ql[lk + 32] |= (lo << 4);
-                                    qh[lk]      |= (hi << 6);
-                                    break;
-                            }
-                        }
-
-                        for (int sub = 0; sub < 2; sub++) {
-                            sub_scales[kt_local * 2 + sub] = GGML_FP16_TO_FP32(sc_pl[sub * 32 + row]);
-                        }
-                    }
-
-                    float max_abs_scale = 0.0f;
-                    for (int s = 0; s < 16; s++) {
-                        float abs_scale = fabsf(sub_scales[s]);
-                        if (abs_scale > max_abs_scale) {
-                            max_abs_scale = abs_scale;
-                        }
-                    }
-
-                    if (max_abs_scale == 0.0f) {
-                        b->d = GGML_FP32_TO_FP16(0.0f);
-                        memset(b->scales, 0, sizeof(b->scales));
-                    } else {
-                        float d_flt = max_abs_scale / 127.0f;
-                        b->d = GGML_FP32_TO_FP16(d_flt);
-                        float d_actual = GGML_FP16_TO_FP32(b->d);
-                        float inv_d = (d_actual != 0.0f) ? (1.0f / d_actual) : 0.0f;
-                        for (int s = 0; s < 16; s++) {
-                            int sc = (int) roundf(sub_scales[s] * inv_d);
-                            b->scales[s] = (int8_t) (std::max)(-128, (std::min)(127, sc));
-                        }
-                    }
-                }
-            }
-        }
-    }
-
-    GGML_UNUSED(size);
-}
-
-static inline void get_scale_min_k4(int j, const uint8_t * q, uint8_t * d, uint8_t * m) {
-    if (j < 4) {
-        *d = q[j] & 63;
-        *m = q[j + 4] & 63;
-    } else {
-        *d = (q[j + 4] & 0xF) | ((q[j - 4] >> 6) << 4);
-        *m = (q[j + 4] >>  4) | ((q[j - 0] >> 6) << 4);
-    }
-}
-
-// tile layout: see HTP_MM_WEIGHT_TILE_SIZE_Q4_1 in htp/matmul-ops.h
-static void repack_q4_K_tiled(ggml_tensor * t, const void * data, size_t offset, size_t size) {
-    GGML_ASSERT(offset == 0);
-
-    const block_q4_K * src_matrix = (const block_q4_K *) data;
-    int64_t ne0 = t->ne[0];
-    int64_t ne1 = t->ne[1];
-    int64_t ne2 = t->ne[2];
-    int64_t ne3 = t->ne[3];
-    int64_t ne0_padded = hex_round_up(ne0, 32);
-    int64_t ne1_padded = hex_round_up(ne1, 32);
-
-    GGML_ASSERT(ne0 % QK_K == 0);
-
-    const int n_col_tiles = ne1_padded / 32;
-    const int n_k_tiles   = ne0_padded / 32;
-    const size_t tile_size   = HTP_MM_WEIGHT_TILE_SIZE_Q4_1;
-    const size_t matrix_size = (size_t) n_col_tiles * n_k_tiles * tile_size;
-
-    const int64_t sb_per_row = ne0 / QK_K;
-
-    for (int i3 = 0; i3 < ne3; i3++) {
-        for (int i2 = 0; i2 < ne2; i2++) {
-            const block_q4_K * src_slice = src_matrix + (i3 * ne2 + i2) * (ne1 * sb_per_row);
-            uint8_t * matrix_dst = (uint8_t *) t->data + (i3 * ne2 + i2) * matrix_size;
-
-            memset(matrix_dst, 0, matrix_size);
-
-            for (int64_t r = 0; r < ne1; r++) {
-                const int ct  = (int) (r / 32);
-                const int row = (int) (r % 32);
-                const block_q4_K * src_row = src_slice + r * sb_per_row;
-
-                for (int kt = 0; kt < n_k_tiles; kt++) {
-                    const int kt_local = kt % 8;
-                    const block_q4_K * b = &src_row[kt / 8];
-                    const float d = GGML_FP16_TO_FP32(b->d);
-                    const float dmin = GGML_FP16_TO_FP32(b->dmin);
-
-                    uint8_t * tile_dst = matrix_dst + ((size_t) ct * n_k_tiles + kt) * tile_size;
-
-                    uint8_t sc, m;
-                    get_scale_min_k4(kt_local, b->scales, &sc, &m);
-
-                    const float D = d * (float) sc;
-                    const float M = -dmin * (float) m;
-
-                    const uint8_t * qs_sub = b->qs + (kt_local / 2) * 32;
-                    const int shift = (kt_local & 1) ? 4 : 0;
-
-                    for (int cp = 0; cp < 16; cp++) {
-                        const uint8_t q0 = (qs_sub[2 * cp + 0] >> shift) & 0x0F;
-                        const uint8_t q1 = (qs_sub[2 * cp + 1] >> shift) & 0x0F;
-                        tile_dst[cp * 32 + row] = (uint8_t) ((q1 << 4) | q0);
-                    }
-
-                    ggml_half * scale_dst = (ggml_half *) (tile_dst + 512);
-                    scale_dst[2 * row + 0] = GGML_FP32_TO_FP16(D);
-                    scale_dst[2 * row + 1] = GGML_FP32_TO_FP16(M);
-                }
-            }
-        }
-    }
-
-    GGML_UNUSED(size);
-}
-
-// Reverse of repack_q4_K_tiled. Unpacks quants and normalizes scales/mins. Read-back only.
-static void repack_tiled_q4_K(void * data, const ggml_tensor * t, size_t offset, size_t size) {
-    GGML_ASSERT(offset == 0);
-
-    block_q4_K * dst_matrix = (block_q4_K *) data;
-    int64_t ne0 = t->ne[0];
-    int64_t ne1 = t->ne[1];
-    int64_t ne2 = t->ne[2];
-    int64_t ne3 = t->ne[3];
-    int64_t ne0_padded = hex_round_up(ne0, 32);
-    int64_t ne1_padded = hex_round_up(ne1, 32);
-
-    GGML_ASSERT(ne0 % QK_K == 0);
-
-    const int n_col_tiles = ne1_padded / 32;
-    const int n_k_tiles   = ne0_padded / 32;
-    const size_t tile_size   = HTP_MM_WEIGHT_TILE_SIZE_Q4_1;
-    const size_t matrix_size = (size_t) n_col_tiles * n_k_tiles * tile_size;
-
-    const int64_t sb_per_row = ne0 / QK_K;
-
-    for (int i3 = 0; i3 < ne3; i3++) {
-        for (int i2 = 0; i2 < ne2; i2++) {
-            block_q4_K * dst_slice = dst_matrix + (i3 * ne2 + i2) * (ne1 * sb_per_row);
-            const uint8_t * matrix_src = (const uint8_t *) t->data + (i3 * ne2 + i2) * matrix_size;
-
-            for (int64_t r = 0; r < ne1; r++) {
-                const int ct  = (int) (r / 32);
-                const int row = (int) (r % 32);
-                block_q4_K * dst_row = dst_slice + r * sb_per_row;
-
-                for (int64_t sb = 0; sb < sb_per_row; sb++) {
-                    block_q4_K * b = &dst_row[sb];
-                    memset(b, 0, sizeof(block_q4_K));
-
-                    float sub_scales[8];
-                    float sub_mins[8];
-
-                    for (int kt_local = 0; kt_local < 8; kt_local++) {
-                        const int kt = sb * 8 + kt_local;
-                        const uint8_t * tile_src = matrix_src + ((size_t) ct * n_k_tiles + kt) * tile_size;
-                        const ggml_half * scale_src = (const ggml_half *) (tile_src + 512);
-
-                        uint8_t * qs_sub = b->qs + (kt_local / 2) * 32;
-                        const int shift = (kt_local & 1) ? 4 : 0;
-
-                        for (int cp = 0; cp < 16; cp++) {
-                            const uint8_t val = tile_src[cp * 32 + row];
-                            const uint8_t q0 = val & 0x0F;
-                            const uint8_t q1 = val >> 4;
-                            qs_sub[2 * cp + 0] |= (uint8_t) (q0 << shift);
-                            qs_sub[2 * cp + 1] |= (uint8_t) (q1 << shift);
-                        }
-
-                        const float D = GGML_FP16_TO_FP32(scale_src[2 * row + 0]);
-                        const float M = GGML_FP16_TO_FP32(scale_src[2 * row + 1]);
-                        sub_scales[kt_local] = (D > 0.0f) ? D : 0.0f;
-                        sub_mins[kt_local]   = (-M > 0.0f) ? -M : 0.0f;
-                    }
-
-                    float max_scale = 0.0f;
-                    float max_min   = 0.0f;
-                    for (int j = 0; j < 8; j++) {
-                        if (sub_scales[j] > max_scale) max_scale = sub_scales[j];
-                        if (sub_mins[j]   > max_min)   max_min   = sub_mins[j];
-                    }
-
-                    float inv_scale = 0.0f;
-                    if (max_scale > 0.0f) {
-                        b->d = GGML_FP32_TO_FP16(max_scale / 63.0f);
-                        const float d_actual = GGML_FP16_TO_FP32(b->d);
-                        inv_scale = (d_actual > 0.0f) ? (1.0f / d_actual) : 0.0f;
-                    } else {
-                        b->d = GGML_FP32_TO_FP16(0.0f);
-                    }
-
-                    float inv_min = 0.0f;
-                    if (max_min > 0.0f) {
-                        b->dmin = GGML_FP32_TO_FP16(max_min / 63.0f);
-                        const float dmin_actual = GGML_FP16_TO_FP32(b->dmin);
-                        inv_min = (dmin_actual > 0.0f) ? (1.0f / dmin_actual) : 0.0f;
-                    } else {
-                        b->dmin = GGML_FP32_TO_FP16(0.0f);
-                    }
-
-                    for (int j = 0; j < 8; j++) {
-                        uint8_t ls = (uint8_t) roundf(inv_scale * sub_scales[j]);
-                        uint8_t lm = (uint8_t) roundf(inv_min * sub_mins[j]);
-                        ls = (std::min)((uint8_t) 63, ls);
-                        lm = (std::min)((uint8_t) 63, lm);
-                        if (j < 4) {
-                            b->scales[j]     = ls;
-                            b->scales[j + 4] = lm;
-                        } else {
-                            b->scales[j + 4] = (ls & 0xF) | ((lm & 0xF) << 4);
-                            b->scales[j - 4] |= ((ls >> 4) << 6);
-                            b->scales[j - 0] |= ((lm >> 4) << 6);
-                        }
-                    }
-                }
-            }
-        }
-    }
-
-    GGML_UNUSED(size);
-}
-
 static void repack_tensor_tiled(ggml_tensor * tensor, const void * data, size_t size) {
    switch (tensor->type) {
        case GGML_TYPE_Q4_0:
@@ -1697,10 +1312,6 @@ static void repack_tensor_tiled(ggml_tensor * tensor, const void * data, size_t
            repack_q4_1_tiled(tensor, data, 0, size);
            break;

-        case GGML_TYPE_Q4_K:
-            repack_q4_K_tiled(tensor, data, 0, size);
-            break;
-
        case GGML_TYPE_Q8_0:
            repack_q8_0_tiled(tensor, data, 0, size);
            break;
@@ -1713,10 +1324,6 @@ static void repack_tensor_tiled(ggml_tensor * tensor, const void * data, size_t
            repack_mxfp4_tiled(tensor, data, 0, size);
            break;

-        case GGML_TYPE_Q6_K:
-            repack_q6_K_tiled(tensor, data, 0, size);
-            break;
-
        default:
            break;
    }
@@ -1795,12 +1402,6 @@ static void ggml_backend_hexagon_buffer_get_tensor(ggml_backend_buffer_t buffer,
            repack_tiled_q4_1(data, tensor, offset, size);
            break;

-        case GGML_TYPE_Q4_K:
-            GGML_ASSERT(offset == 0);
-            GGML_ASSERT(offset + size <= ggml_nbytes(tensor));
-            repack_tiled_q4_K(data, tensor, offset, size);
-            break;
-
        case GGML_TYPE_Q8_0:
            GGML_ASSERT(offset == 0);
            GGML_ASSERT(offset + size <= ggml_nbytes(tensor));
@@ -1819,12 +1420,6 @@ static void ggml_backend_hexagon_buffer_get_tensor(ggml_backend_buffer_t buffer,
            repack_tiled_mxfp4(data, tensor, offset, size);
            break;

-        case GGML_TYPE_Q6_K:
-            GGML_ASSERT(offset == 0);
-            GGML_ASSERT(offset + size <= ggml_nbytes(tensor));
-            repack_tiled_q6_K(data, tensor, offset, size);
-            break;
-
        default:
            memcpy(data, (const char *) tensor->data + offset, size);
            break;
@@ -1930,10 +1525,6 @@ static void ggml_backend_hexagon_buffer_get_tensor_2d(ggml_backend_buffer_t buff
            repack_tiled_q4_1(temp_buf.data(), tensor, offset, temp_size);
            break;

-        case GGML_TYPE_Q4_K:
-            repack_tiled_q4_K(temp_buf.data(), tensor, offset, temp_size);
-            break;
-
        case GGML_TYPE_Q8_0:
            repack_tiled_q8_0(temp_buf.data(), tensor, offset, temp_size);
            break;
@@ -1946,10 +1537,6 @@ static void ggml_backend_hexagon_buffer_get_tensor_2d(ggml_backend_buffer_t buff
            repack_tiled_mxfp4(temp_buf.data(), tensor, offset, temp_size);
            break;

-        case GGML_TYPE_Q6_K:
-            repack_tiled_q6_K(temp_buf.data(), tensor, offset, temp_size);
-            break;
-
        default:
            memcpy(temp_buf.data(), (const uint8_t *) tensor->data + offset, temp_size);
            break;
@@ -2058,7 +1645,7 @@ static size_t ggml_backend_hexagon_buffer_type_get_alloc_size(ggml_backend_buffe
        int64_t ne1 = hex_round_up(t->ne[1], 32);
        int64_t ne2 = t->ne[2];
        int64_t ne3 = t->ne[3];
-        return ggml_hexagon_tiled_row_size(t->type, ne0) * ne1 * ne2 * ne3;
+        return ggml_row_size(t->type, ne0) * ne1 * ne2 * ne3;
    }
    return ggml_nbytes(t);

@@ -2219,7 +1806,7 @@ struct ggml_hexagon_opbatch {
            ne0 = hex_round_up(ne0, 32);
            ne1 = hex_round_up(ne1, 32);
        }
-        int64_t nb1 = is_repack ? (int64_t) ggml_hexagon_tiled_row_size(t->type, ne0) : t->nb[1];
+        int64_t nb1 = is_repack ? ggml_row_size(t->type, ne0) : t->nb[1];
        int64_t nb2 = is_repack ? nb1 * ne1      : t->nb[2];
        int64_t nb3 = is_repack ? nb2 * t->ne[2] : t->nb[3];

@@ -2268,7 +1855,7 @@ struct ggml_hexagon_opbatch {
            h.ne[3] = t->ne[3];

            h.nb[0] = t->nb[0];
-            h.nb[1] = ggml_hexagon_tiled_row_size(t->type, h.ne[0]);
+            h.nb[1] = ggml_row_size(t->type, h.ne[0]);
            h.nb[2] = h.nb[1] * h.ne[1];
            h.nb[3] = h.nb[2] * h.ne[2];
            h.size  = h.nb[3] * h.ne[3];
@@ -4345,7 +3932,7 @@ static bool ggml_hexagon_precompute_hmx_mm_params(
    kparams->n_act_threads = act_threads_selected;
    kparams->tile_size = htp_mm_get_weight_tile_size(wtype);
    kparams->aligned_tile_size = aligned_tile_size;
-    kparams->src1_row_size = (wtype == GGML_TYPE_Q4_1 || wtype == GGML_TYPE_Q4_K) ? htp_mm_q8_1_tiled_row_size(ne10) : htp_mm_q8_0_tiled_row_size(ne10);
+    kparams->src1_row_size = (wtype == GGML_TYPE_Q4_1) ? htp_mm_q8_1_tiled_row_size(ne10) : htp_mm_q8_0_tiled_row_size(ne10);
    kparams->vtcm_size = vtcm_size;
    kparams->vtcm_src0_size = 0;
    kparams->div_n_act_threads = init_fastdiv_values(act_threads_selected);
@@ -4395,7 +3982,7 @@ static void ggml_hexagon_precompute_hvx_mm_params(

        if (is_matmul_id) {
            kparams->kernel_type   = (src1_nrows < (int) sess->n_threads) ? HTP_MM_KERNEL_HVX_QUANT_BLOCK : HTP_MM_KERNEL_HVX_QUANT_ROW;
-            kparams->src1_row_size = (wtype == GGML_TYPE_Q4_1 || wtype == GGML_TYPE_Q4_K) ? htp_mm_q8_1_tiled_row_size(ne10) : htp_mm_q8_0_tiled_row_size(ne10);
+            kparams->src1_row_size = (wtype == GGML_TYPE_Q4_1) ? htp_mm_q8_1_tiled_row_size(ne10) : htp_mm_q8_0_tiled_row_size(ne10);

            struct htp_mm_hvx_vtcm_layout L;
            uint32_t max_prefetch = (src1_nrows > HTP_MM_HMX_MIN_NROWS) ? 2 : 16;
@@ -4424,7 +4011,7 @@ static void ggml_hexagon_precompute_hvx_mm_params(
        } else {
            bool try_tiled = (k_align && opt_mm_select >= 2);
            if (try_tiled) {
-                kparams->src1_row_size = (wtype == GGML_TYPE_Q4_1 || wtype == GGML_TYPE_Q4_K) ? htp_mm_q8_1_tiled_row_size(ne10) : htp_mm_q8_0_tiled_row_size(ne10);
+                kparams->src1_row_size = (wtype == GGML_TYPE_Q4_1) ? htp_mm_q8_1_tiled_row_size(ne10) : htp_mm_q8_0_tiled_row_size(ne10);
                if (src1_nrows < (int)sess->n_threads) {
                    kparams->kernel_type = HTP_MM_KERNEL_HVX_QUANT_BLOCK;
                } else {
@@ -4465,7 +4052,7 @@ static void ggml_hexagon_precompute_hvx_mm_params(

            // Flat HVX fallback
            {
-                kparams->src1_row_size = (wtype == GGML_TYPE_Q4_1 || wtype == GGML_TYPE_Q4_K) ? htp_mm_q8_1_flat_row_size(ne10) : htp_mm_q8_0_flat_row_size(ne10);
+                kparams->src1_row_size = (wtype == GGML_TYPE_Q4_1) ? htp_mm_q8_1_flat_row_size(ne10) : htp_mm_q8_0_flat_row_size(ne10);
                kparams->kernel_type = HTP_MM_KERNEL_HVX_QUANT_ROW_FLAT;

                struct htp_mm_hvx_vtcm_layout L;
@@ -4857,7 +4444,7 @@ static void ggml_hexagon_precompute_fused_mmnx_params(

    {
        const int src1_nrows = ne11 * ne12 * ne13;
-        const size_t src1_row_size = (wtype == GGML_TYPE_Q4_1 || wtype == GGML_TYPE_Q4_K) ? htp_mm_q8_1_tiled_row_size(ne10) : htp_mm_q8_0_tiled_row_size(ne10);
+        const size_t src1_row_size = (wtype == GGML_TYPE_Q4_1) ? htp_mm_q8_1_tiled_row_size(ne10) : htp_mm_q8_0_tiled_row_size(ne10);
        const size_t src0_row_size = src0->nb[1];

        uint32_t best_n_prefetch = 16;
@@ -4897,7 +4484,7 @@ static void ggml_hexagon_precompute_fused_mmnx_params(
            kparams->n_weights      = n_weights;
        } else {
            kparams->kernel_type = HTP_MM_KERNEL_HVX_QUANT_ROW_FLAT;
-            size_t flat_src1_row_size = (wtype == GGML_TYPE_Q4_1 || wtype == GGML_TYPE_Q4_K) ? htp_mm_q8_1_flat_row_size(ne10) : htp_mm_q8_0_flat_row_size(ne10);
+            size_t flat_src1_row_size = (wtype == GGML_TYPE_Q4_1) ? htp_mm_q8_1_flat_row_size(ne10) : htp_mm_q8_0_flat_row_size(ne10);

            htp_mm_hvx_vtcm_layout_build(
                &L, HTP_MM_KERNEL_HVX_QUANT_ROW_FLAT, wtype, ne10, src1_nrows, sess->n_threads,
@@ -4960,9 +4547,7 @@ static bool ggml_hexagon_supported_mul_mat(const struct ggml_hexagon_session * s
        case GGML_TYPE_Q8_0:
        case GGML_TYPE_IQ4_NL:
        case GGML_TYPE_MXFP4:
-        case GGML_TYPE_Q4_K:
-        case GGML_TYPE_Q6_K:
-            if (src0->ne[0] % ((src0->type == GGML_TYPE_Q6_K || src0->type == GGML_TYPE_Q4_K) ? QK_K : 32)) {
+            if (src0->ne[0] % 32) {
                return false;
            }

@@ -5026,9 +4611,7 @@ static bool ggml_hexagon_supported_mul_mat_id(const struct ggml_hexagon_session
        case GGML_TYPE_Q8_0:
        case GGML_TYPE_IQ4_NL:
        case GGML_TYPE_MXFP4:
-        case GGML_TYPE_Q4_K:
-        case GGML_TYPE_Q6_K:
-            if (src0->ne[0] % ((src0->type == GGML_TYPE_Q6_K || src0->type == GGML_TYPE_Q4_K) ? QK_K : 32)) {
+            if ((src0->ne[0] % 32)) {
                return false;
            }

@@ -5403,9 +4986,7 @@ static bool ggml_hexagon_supported_rope(const struct ggml_hexagon_session * sess
    const int mode   = op_params[2];
    const int n_offs = op_params[15];

-    // llama probes weight placement with a dummy rope where every param is 0 (llama-model-loader.cpp).
-    // Rejecting it puts rope_freqs on the CPU, which then splits the graph at every full-attention layer.
-    if (n_dims < 0 || n_dims % 2 != 0) {
+    if (n_dims <= 0 || n_dims % 2 != 0) {
        return false;
    }

@@ -5416,7 +4997,7 @@ static bool ggml_hexagon_supported_rope(const struct ggml_hexagon_session * sess

    float freq_base;
    memcpy(&freq_base, op_params + 5, sizeof(float));
-    if (freq_base < 0.0f) {
+    if (freq_base <= 0.0f) {
        return false;
    }

@@ -5764,8 +5345,8 @@ static bool is_supported_mul_mat_nx_kernel(const ggml_tensor * src0, const struc
        return kparams->kernel_type == HTP_MM_KERNEL_HMX_2D;
    }

-    if (!ggml_hexagon_is_repack_type(src0->type) || src0->type == GGML_TYPE_Q6_K) {
-        return false;  // Q6_K has no fused HVX kernel
+    if (!ggml_hexagon_is_repack_type(src0->type)) {
+        return false;
    }

    return kparams->kernel_type == HTP_MM_KERNEL_HVX_QUANT_ROW || kparams->kernel_type == HTP_MM_KERNEL_HVX_QUANT_ROW_FLAT;
@@ -5795,7 +5376,7 @@ static bool is_mergeable_mul_mat(const ggml_tensor * t) {
        return ggml_hexagon_is_hmx_weight_type(src0->type);
    }

-    return ggml_hexagon_is_repack_type(src0->type) && src0->type != GGML_TYPE_Q6_K;
+    return ggml_hexagon_is_repack_type(src0->type);
 }

 static bool is_mergeable_mul_mat_pair(const ggml_tensor * n1, const ggml_tensor * n2) {
@@ -7184,10 +6765,6 @@ static void ggml_hexagon_init(ggml_backend_reg * reg) {
                  "please update hexagon_type to match ggml_type");
    static_assert((unsigned int) HTP_TYPE_IQ4_NL == (unsigned int) GGML_TYPE_IQ4_NL,
                  "please update hexagon_type to match ggml_type");
-    static_assert((unsigned int) HTP_TYPE_Q4_K == (unsigned int) GGML_TYPE_Q4_K,
-                  "please update hexagon_type to match ggml_type");
-    static_assert((unsigned int) HTP_TYPE_Q6_K == (unsigned int) GGML_TYPE_Q6_K,
-                  "please update hexagon_type to match ggml_type");

    const char * str_verbose  = getenv("GGML_HEXAGON_VERBOSE");
    const char * str_opbatch  = getenv("GGML_HEXAGON_OPBATCH");
@@ -126,13 +126,6 @@ static void cpy_thread_##NAME##_reshape(unsigned int nth, unsigned int ith, void
    const uint32_t th_end   = MIN(th_start + th_nelem, ct->elem_start + ct->nelem);                  \
    if (th_start >= th_end) return;                                                                  \
                                                                                                     \
-    if (htp_tensor_is_contiguous(src0, ELEM_SIZE) && htp_tensor_is_contiguous(dst, ELEM_SIZE)) {     \
-        hvx_copy_uu((uint8_t *) dst->data + (size_t) th_start * ELEM_SIZE,                           \
-                    (const uint8_t *) src0->data + (size_t) th_start * ELEM_SIZE,                    \
-                    th_end - th_start, ELEM_SIZE);                                                   \
-        return;                                                                                      \
-    }                                                                                                \
-                                                                                                     \
    const uint32_t ne01_ne00      = ne01 * ne00;                                                     \
    const uint32_t ne02_ne01_ne00 = ne02 * ne01_ne00;                                                \
    const uint32_t ne1_ne0        = ne1 * ne0;                                                       \
@@ -156,21 +149,11 @@ static void cpy_thread_##NAME##_reshape(unsigned int nth, unsigned int ith, void
    char * dst_ptr        = (char *)       dst->data  + i10*nb0  + i11*nb1  + i12*nb2  + i13*nb3;    \
    const char * src0_ptr = (const char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03;   \
                                                                                                     \
-    const bool rows_contig = (nb00 == ELEM_SIZE) && (nb0 == ELEM_SIZE);                              \
+    for (; e < th_end; e++) {                                                                        \
+        *((ELEM_TYPE *) dst_ptr) = *((const ELEM_TYPE *) src0_ptr);                                  \
                                                                                                     \
-    while (e < th_end) {                                                                             \
-        uint32_t run = 1;                                                                            \
-        if (rows_contig) {                                                                           \
-            run = MIN(MIN(ne00 - i00, ne0 - i10), th_end - e);                                       \
-            hvx_copy_uu((uint8_t *) dst_ptr, (const uint8_t *) src0_ptr, run, ELEM_SIZE);            \
-        } else {                                                                                     \
-            *((ELEM_TYPE *) dst_ptr) = *((const ELEM_TYPE *) src0_ptr);                              \
-        }                                                                                            \
-        e += run;                                                                                    \
-                                                                                                     \
-        dst_ptr += run * nb0;                                                                        \
-        i10     += run;                                                                              \
-        if (i10 == ne0) {                                                                            \
+        dst_ptr += nb0;                                                                              \
+        if (++i10 == ne0) {                                                                          \
            i10 = 0;                                                                                 \
            if (++i11 == ne1) {                                                                      \
                i11 = 0;                                                                             \
@@ -182,9 +165,8 @@ static void cpy_thread_##NAME##_reshape(unsigned int nth, unsigned int ith, void
            dst_ptr = (char *) dst->data + i11*nb1 + i12*nb2 + i13*nb3;                              \
        }                                                                                            \
                                                                                                     \
-        src0_ptr += run * nb00;                                                                      \
-        i00      += run;                                                                             \
-        if (i00 == ne00) {                                                                           \
+        src0_ptr += nb00;                                                                            \
+        if (++i00 == ne00) {                                                                         \
            i00 = 0;                                                                                 \
            if (++i01 == ne01) {                                                                     \
                i01 = 0;                                                                             \
@@ -312,18 +294,6 @@ static inline void cpy_dma_sametype_sameshape(
    dma_queue_flush(q);
 }

-static inline void cpy_dma_sametype_reshape_contig(
-    struct htp_ops_context * octx,
-    const struct htp_tensor * dst,
-    const struct htp_tensor * src0,
-    uint32_t total_bytes
-) {
-    dma_queue * q = octx->ctx->dma[0];
-    dma_queue_push(q, dma_make_ptr((void *) dst->data, (const void *) src0->data),
-                   total_bytes, total_bytes, total_bytes, /*nrows=*/ 1);
-    dma_queue_pop(q);
-}
-
 static int exec_cpy(struct htp_ops_context * octx, bool * use_dma) {
    cpy_preamble;
    *use_dma = false;
@@ -357,7 +327,6 @@ static int exec_cpy(struct htp_ops_context * octx, bool * use_dma) {

    const uint32_t n_threads = octx->n_threads;

-    const bool src_is_contiguous = htp_tensor_is_contiguous(src0, ct.src0_type_size);
    const bool dst_is_contiguous = htp_tensor_is_contiguous(dst, ct.dst_type_size);

    if (sameshape) {
@@ -406,12 +375,6 @@ static int exec_cpy(struct htp_ops_context * octx, bool * use_dma) {
        const uint32_t total_elems = ne0 * ne1 * ne2 * ne3;
        const uint32_t elems_per_line = (ct.dst_type_size == 4) ? 32 : 64;

-        if (octx->ctx->mdev.count <= 1 && dst_is_contiguous && src_is_contiguous) {
-            *use_dma = true;
-            cpy_dma_sametype_reshape_contig(octx, dst, src0, total_elems * ct.dst_type_size);
-            return HTP_STATUS_OK;
-        }
-
        ct.div_ne0            = init_fastdiv_values(ne0);
        ct.div_ne1_ne0        = init_fastdiv_values(ne1 * ne0);
        ct.div_ne2_ne1_ne0    = init_fastdiv_values(ne2 * ne1 * ne0);
@@ -506,41 +506,6 @@ static void dequantize_tiled_weight_to_fp16_task_q8_0(
    }
 }

-// Q6_K stores 6-bit weights and one fp16 scale per 16 k, see HTP_MM_WEIGHT_TILE_SIZE_Q6_K.
-// A k-group holds 4 k per row, the HMX tile holds 2, so each group is dealt into two tiles.
-static void dequantize_tiled_weight_to_fp16_task_q6_k(
-        const tiled_dequantize_state_t *state,
-        uint32_t start_tile, uint32_t end_tile) {
-
-    const HVX_Vector mask_0f = Q6_Vb_vsplat_R(0x0F);
-    const HVX_Vector mask_03 = Q6_Vb_vsplat_R(0x03);
-    const HVX_Vector i32     = Q6_Vb_vsplat_R(32);
-
-    for (uint32_t t = start_tile; t < end_tile; t++) {
-        const HVX_Vector * vptr = (const HVX_Vector *) (state->src + t * state->aligned_tile_size);
-        __fp16 * dst_ptr = state->dst + t * HTP_MM_HMX_TILE_N_ELMS;
-
-        HVX_Vector v_sc      = vptr[6];
-        HVX_Vector v_sc_k16  = Q6_V_vror_VR(v_sc, 64);
-        HVX_Vector v_scale_k0  = Q6_V_lo_W(Q6_W_vshuff_VVR(v_sc, v_sc, -2));
-        HVX_Vector v_scale_k16 = Q6_V_lo_W(Q6_W_vshuff_VVR(v_sc_k16, v_sc_k16, -2));
-
-        #pragma unroll
-        for (int g = 0; g < 8; g++) {
-            const HVX_Vector v_scale = (g < 4) ? v_scale_k0 : v_scale_k16;
-
-            HVX_Vector     v_q   = unpack_q6_k_group(vptr, g, mask_0f, mask_03, i32);
-            HVX_VectorPair vp16  = Q6_Wh_vunpack_Vb(v_q);
-            HVX_VectorPair vp_k  = Q6_W_vdeal_VVR(Q6_V_hi_W(vp16), Q6_V_lo_W(vp16), -4);
-
-            hvx_vmem(dst_ptr + (2 * g + 0) * 64) =
-                Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(Q6_Vhf_equals_Vh(Q6_V_lo_W(vp_k)), v_scale));
-            hvx_vmem(dst_ptr + (2 * g + 1) * 64) =
-                Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(Q6_Vhf_equals_Vh(Q6_V_hi_W(vp_k)), v_scale));
-        }
-    }
-}
-
 static __attribute__((noinline))
 void convert_f16_weight_to_fp16_tiles_task(
        const tiled_dequantize_state_t *state,
@@ -838,12 +803,15 @@ static void transfer_output_chunk_fp16_to_fp32_col_chunk(
            HVX_Vector v = ((const HVX_Vector *) tile)[r1];
            HVX_VectorPair vp = Q6_Wqf32_vmpy_VhfVhf(v, one);

+            HVX_Vector *pv_out0 = (HVX_Vector *) (output_row_base + c + 0);
+            HVX_Vector *pv_out1 = (HVX_Vector *) (output_row_base + c + dst_stride);
+
            HVX_Vector v_out0 = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(vp));
            if (src2_row_base) {
                HVX_Vector v_src2_0 = hvx_vmemu(src2_row_base + c + 0);
                v_out0 = hvx_vec_add_f32_f32(v_out0, v_src2_0);
            }
-            hvx_vmemu(output_row_base + c + 0) = v_out0;
+            *pv_out0 = v_out0;

            if (r + 1 < n_rows) {
                HVX_Vector v_out1 = Q6_Vsf_equals_Vqf32(Q6_V_hi_W(vp));
@@ -851,7 +819,7 @@ static void transfer_output_chunk_fp16_to_fp32_col_chunk(
                    HVX_Vector v_src2_1 = hvx_vmemu(src2_row_base + c + src2_stride);
                    v_out1 = hvx_vec_add_f32_f32(v_out1, v_src2_1);
                }
-                hvx_vmemu(output_row_base + c + dst_stride) = v_out1;
+                *pv_out1 = v_out1;
            }
        }

@@ -1398,9 +1366,12 @@ static void transfer_output_chunk_fp16_to_fp32_scattered(
            HVX_Vector v = ((const HVX_Vector *) tile)[r1];
            HVX_VectorPair vp = Q6_Wqf32_vmpy_VhfVhf(v, one);

-            hvx_vmemu(output_row0 + c) = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(vp));
-            if (output_row1) {
-                hvx_vmemu(output_row1 + c) = Q6_Vsf_equals_Vqf32(Q6_V_hi_W(vp));
+            HVX_Vector *pv_out0 = (HVX_Vector *) (output_row0 + c);
+            HVX_Vector *pv_out1 = output_row1 ? (HVX_Vector *) (output_row1 + c) : NULL;
+
+            *pv_out0 = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(vp));
+            if (pv_out1) {
+                *pv_out1 = Q6_Vsf_equals_Vqf32(Q6_V_hi_W(vp));
            }
        }
    }
@@ -22,8 +22,6 @@ enum htp_data_type {
    HTP_TYPE_Q4_0   = 2,
    HTP_TYPE_Q4_1   = 3,
    HTP_TYPE_Q8_0   = 8,
-    HTP_TYPE_Q4_K   = 12,
-    HTP_TYPE_Q6_K   = 14,
    HTP_TYPE_IQ4_NL = 20,
    HTP_TYPE_I32    = 26,
    HTP_TYPE_I64    = 27,
@@ -744,143 +744,6 @@ static void flat_vec_dot_q8_0_32x2(const uint32_t n, float * restrict s0, float
    }
 }

-static void flat_vec_dot_q6_k_32x1(const uint32_t n, float * restrict s, const void * restrict vx, const void * restrict vy, uint32_t valid_rows, const float * restrict sz) {
-    const uint8_t * restrict tile_ptr = vx;
-    const uint8_t * restrict y_q = vy;
-
-    HVX_Vector v_sum_float = Q6_V_vzero();
-    HVX_Vector i32 = Q6_Vb_vsplat_R(32);
-
-    static const uint8_t __attribute__((aligned(128))) repl[128] = {
-        0x00, 0x00, 0x00, 0x00, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
-        0x10, 0x10, 0x10, 0x10, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
-        0x20, 0x20, 0x20, 0x20, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
-        0x10, 0x10, 0x10, 0x10, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
-        0x40, 0x40, 0x40, 0x40, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
-        0x10, 0x10, 0x10, 0x10, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
-        0x20, 0x20, 0x20, 0x20, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
-        0x10, 0x10, 0x10, 0x10, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
-    };
-    HVX_Vector v_repl_ctrl = * (const HVX_Vector *) repl;
-
-    const uint32_t quants_size = hex_round_up(n, 128);
-    const __fp16 * restrict y_scales = (const __fp16 *) (y_q + quants_size);
-
-    uint32_t n_k_tiles = n / 32;
-    for (uint32_t kt = 0; kt < n_k_tiles; kt++) {
-        const HVX_Vector * restrict vptr = (const HVX_Vector *) (tile_ptr + kt * 896);
-
-        uint32_t block_idx = kt / 4;
-        uint32_t sub_idx = kt % 4;
-
-        HVX_Vector vx_i8 = * (const HVX_Vector *) (y_q + block_idx * 128);
-        HVX_Vector v_act_raw = Q6_V_vror_VR(vx_i8, sub_idx * 32);
-
-        HVX_Vector v_act_rep[8];
-        v_act_rep[0] = Q6_V_vdelta_VV(v_act_raw, v_repl_ctrl);
-        v_act_rep[1] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act_raw, 4), v_repl_ctrl);
-        v_act_rep[2] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act_raw, 8), v_repl_ctrl);
-        v_act_rep[3] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act_raw, 12), v_repl_ctrl);
-        v_act_rep[4] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act_raw, 16), v_repl_ctrl);
-        v_act_rep[5] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act_raw, 20), v_repl_ctrl);
-        v_act_rep[6] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act_raw, 24), v_repl_ctrl);
-        v_act_rep[7] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act_raw, 28), v_repl_ctrl);
-
-        HVX_VectorPair v_sums = accum_q6_k_32x1(vptr, v_act_rep, i32);
-
-        __fp16 scale_a_val = y_scales[kt];
-        HVX_Vector v_scale_a = hvx_vec_repl_f16(Q6_Vh_vsplat_R(*(const int16_t *)&scale_a_val));
-
-        v_sum_float = hvx_vec_add_f32_f32(v_sum_float, scale_q6_k_32x1(v_sums, vptr[6], v_scale_a));
-    }
-
-    if (sz) {
-        hvx_vec_store_u(s, valid_rows * sizeof(float), hvx_vec_add_f32_f32(v_sum_float, hvx_vmemu(sz)));
-    } else {
-        hvx_vec_store_u(s, valid_rows * sizeof(float), v_sum_float);
-    }
-}
-
-static void flat_vec_dot_q6_k_32x2(const uint32_t n, float * restrict s0, float * restrict s1, const void * restrict vx, const void * restrict vy0, const void * restrict vy1, uint32_t valid_rows, const float * restrict sz0, const float * restrict sz1) {
-    const uint8_t * restrict tile_ptr = vx;
-    const uint8_t * restrict y0_q = vy0;
-    const uint8_t * restrict y1_q = vy1;
-
-    HVX_Vector v_sum_float_c0 = Q6_V_vzero();
-    HVX_Vector v_sum_float_c1 = Q6_V_vzero();
-    HVX_Vector i32 = Q6_Vb_vsplat_R(32);
-
-    static const uint8_t __attribute__((aligned(128))) repl[128] = {
-        0x00, 0x00, 0x00, 0x00, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
-        0x10, 0x10, 0x10, 0x10, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
-        0x20, 0x20, 0x20, 0x20, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
-        0x10, 0x10, 0x10, 0x10, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
-        0x40, 0x40, 0x40, 0x40, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
-        0x10, 0x10, 0x10, 0x10, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
-        0x20, 0x20, 0x20, 0x20, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
-        0x10, 0x10, 0x10, 0x10, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
-    };
-    HVX_Vector v_repl_ctrl = * (const HVX_Vector *) repl;
-
-    const uint32_t quants_size = hex_round_up(n, 128);
-    const __fp16 * restrict y0_scales = (const __fp16 *) (y0_q + quants_size);
-    const __fp16 * restrict y1_scales = (const __fp16 *) (y1_q + quants_size);
-
-    uint32_t n_k_tiles = n / 32;
-    for (uint32_t kt = 0; kt < n_k_tiles; kt++) {
-        const HVX_Vector * restrict vptr = (const HVX_Vector *) (tile_ptr + kt * 896);
-
-        uint32_t block_idx = kt / 4;
-        uint32_t sub_idx = kt % 4;
-
-        HVX_Vector vx0_i8 = * (const HVX_Vector *) (y0_q + block_idx * 128);
-        HVX_Vector vx1_i8 = * (const HVX_Vector *) (y1_q + block_idx * 128);
-        HVX_Vector v_act0_raw = Q6_V_vror_VR(vx0_i8, sub_idx * 32);
-        HVX_Vector v_act1_raw = Q6_V_vror_VR(vx1_i8, sub_idx * 32);
-
-        HVX_Vector v_act0_rep[8];
-        HVX_Vector v_act1_rep[8];
-        v_act0_rep[0] = Q6_V_vdelta_VV(v_act0_raw, v_repl_ctrl);
-        v_act0_rep[1] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act0_raw, 4), v_repl_ctrl);
-        v_act0_rep[2] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act0_raw, 8), v_repl_ctrl);
-        v_act0_rep[3] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act0_raw, 12), v_repl_ctrl);
-        v_act0_rep[4] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act0_raw, 16), v_repl_ctrl);
-        v_act0_rep[5] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act0_raw, 20), v_repl_ctrl);
-        v_act0_rep[6] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act0_raw, 24), v_repl_ctrl);
-        v_act0_rep[7] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act0_raw, 28), v_repl_ctrl);
-        v_act1_rep[0] = Q6_V_vdelta_VV(v_act1_raw, v_repl_ctrl);
-        v_act1_rep[1] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act1_raw, 4), v_repl_ctrl);
-        v_act1_rep[2] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act1_raw, 8), v_repl_ctrl);
-        v_act1_rep[3] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act1_raw, 12), v_repl_ctrl);
-        v_act1_rep[4] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act1_raw, 16), v_repl_ctrl);
-        v_act1_rep[5] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act1_raw, 20), v_repl_ctrl);
-        v_act1_rep[6] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act1_raw, 24), v_repl_ctrl);
-        v_act1_rep[7] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act1_raw, 28), v_repl_ctrl);
-
-        HVX_VectorPair v_sums0, v_sums1;
-        accum_q6_k_32x2(vptr, v_act0_rep, v_act1_rep, i32, &v_sums0, &v_sums1);
-
-        __fp16 scale_a0_val = y0_scales[kt];
-        __fp16 scale_a1_val = y1_scales[kt];
-        HVX_Vector v_scale_a0 = hvx_vec_repl_f16(Q6_Vh_vsplat_R(*(const int16_t *)&scale_a0_val));
-        HVX_Vector v_scale_a1 = hvx_vec_repl_f16(Q6_Vh_vsplat_R(*(const int16_t *)&scale_a1_val));
-
-        v_sum_float_c0 = hvx_vec_add_f32_f32(v_sum_float_c0, scale_q6_k_32x1(v_sums0, vptr[6], v_scale_a0));
-        v_sum_float_c1 = hvx_vec_add_f32_f32(v_sum_float_c1, scale_q6_k_32x1(v_sums1, vptr[6], v_scale_a1));
-    }
-
-    if (sz0) {
-        hvx_vec_store_u(s0, valid_rows * sizeof(float), hvx_vec_add_f32_f32(v_sum_float_c0, hvx_vmemu(sz0)));
-    } else {
-        hvx_vec_store_u(s0, valid_rows * sizeof(float), v_sum_float_c0);
-    }
-    if (sz1) {
-        hvx_vec_store_u(s1, valid_rows * sizeof(float), hvx_vec_add_f32_f32(v_sum_float_c1, hvx_vmemu(sz1)));
-    } else {
-        hvx_vec_store_u(s1, valid_rows * sizeof(float), v_sum_float_c1);
-    }
-}
-
 static void flat_vec_dot_iq4nl_32x1(const uint32_t n, float * restrict s, const void * restrict vx, const void * restrict vy, uint32_t valid_rows, const float * restrict sz) {
    const uint8_t * restrict tile_ptr = vx;
    const uint8_t * restrict y_q = vy;
@@ -378,74 +378,6 @@ static inline HVX_VectorPair accum_q8_0_32x2(
    return Q6_W_vcombine_VV(v_sum1, v_sum0);
 }

-// Q6_K weights are stored unsigned (0..63), see HTP_MM_WEIGHT_TILE_SIZE_Q6_K. Unpack k-group g of a tile to signed bytes (q - 32)
-static inline HVX_Vector unpack_q6_k_group(const HVX_Vector * restrict vptr, int g, HVX_Vector mask_0f, HVX_Vector mask_03, HVX_Vector i32) {
-    HVX_Vector v_lo = (g & 1) ? Q6_Vub_vlsr_VubR(vptr[g >> 1], 4) : Q6_V_vand_VV(vptr[g >> 1], mask_0f);
-    HVX_Vector v_hi = (g & 3) ? Q6_Vub_vlsr_VubR(vptr[4 + (g >> 2)], 2 * (g & 3)) : vptr[4 + (g >> 2)];
-    HVX_Vector v_q  = Q6_V_vor_VV(v_lo, Q6_Vw_vasl_VwR(Q6_V_vand_VV(v_hi, mask_03), 4));
-    return Q6_Vb_vsub_VbVb(v_q, i32);
-}
-
-// k 0..15 and k 16..31 of a Q6_K tile have different scales: lo half of the pair sums k 0..15, hi half sums k 16..31
-static inline HVX_VectorPair accum_q6_k_32x1(
-    const HVX_Vector * restrict vptr,
-    const HVX_Vector * restrict v_act,
-    HVX_Vector i32
-) {
-    HVX_Vector v_sum_lo = Q6_V_vzero();
-    HVX_Vector v_sum_hi = Q6_V_vzero();
-    HVX_Vector mask_0f = Q6_Vb_vsplat_R(0x0F);
-    HVX_Vector mask_03 = Q6_Vb_vsplat_R(0x03);
-
-    #pragma unroll
-    for (int g = 0; g < 4; g++) {
-        HVX_Vector v_W_lo = unpack_q6_k_group(vptr, g,     mask_0f, mask_03, i32);
-        HVX_Vector v_W_hi = unpack_q6_k_group(vptr, g + 4, mask_0f, mask_03, i32);
-        v_sum_lo = Q6_Vw_vrmpyacc_VwVbVb(v_sum_lo, v_W_lo, v_act[g]);
-        v_sum_hi = Q6_Vw_vrmpyacc_VwVbVb(v_sum_hi, v_W_hi, v_act[g + 4]);
-    }
-
-    return Q6_W_vcombine_VV(v_sum_hi, v_sum_lo);
-}
-
-static inline void accum_q6_k_32x2(
-    const HVX_Vector * restrict vptr,
-    const HVX_Vector * restrict v_act0,
-    const HVX_Vector * restrict v_act1,
-    HVX_Vector i32,
-    HVX_VectorPair * v_sums0,
-    HVX_VectorPair * v_sums1
-) {
-    HVX_Vector v_sum0_lo = Q6_V_vzero();
-    HVX_Vector v_sum0_hi = Q6_V_vzero();
-    HVX_Vector v_sum1_lo = Q6_V_vzero();
-    HVX_Vector v_sum1_hi = Q6_V_vzero();
-    HVX_Vector mask_0f = Q6_Vb_vsplat_R(0x0F);
-    HVX_Vector mask_03 = Q6_Vb_vsplat_R(0x03);
-
-    #pragma unroll
-    for (int g = 0; g < 4; g++) {
-        HVX_Vector v_W_lo = unpack_q6_k_group(vptr, g,     mask_0f, mask_03, i32);
-        HVX_Vector v_W_hi = unpack_q6_k_group(vptr, g + 4, mask_0f, mask_03, i32);
-        v_sum0_lo = Q6_Vw_vrmpyacc_VwVbVb(v_sum0_lo, v_W_lo, v_act0[g]);
-        v_sum0_hi = Q6_Vw_vrmpyacc_VwVbVb(v_sum0_hi, v_W_hi, v_act0[g + 4]);
-        v_sum1_lo = Q6_Vw_vrmpyacc_VwVbVb(v_sum1_lo, v_W_lo, v_act1[g]);
-        v_sum1_hi = Q6_Vw_vrmpyacc_VwVbVb(v_sum1_hi, v_W_hi, v_act1[g + 4]);
-    }
-
-    *v_sums0 = Q6_W_vcombine_VV(v_sum0_hi, v_sum0_lo);
-    *v_sums1 = Q6_W_vcombine_VV(v_sum1_hi, v_sum1_lo);
-}
-
-// scale the two half sums with the per-row tile scales (v_scale_w = vptr[6]) and the activation scale
-static inline HVX_Vector scale_q6_k_32x1(HVX_VectorPair v_sums, HVX_Vector v_scale_w, HVX_Vector v_scale_a) {
-    HVX_Vector v_scale_lo = hvx_vec_mul_f16_f16_to_f32_lower32(v_scale_w, v_scale_a);
-    HVX_Vector v_scale_hi = hvx_vec_mul_f16_f16_to_f32_lower32(Q6_V_vror_VR(v_scale_w, 64), v_scale_a);
-    HVX_Vector v_lo = hvx_vec_mul_f32_f32(Q6_Vsf_equals_Vw(Q6_V_lo_W(v_sums)), v_scale_lo);
-    HVX_Vector v_hi = hvx_vec_mul_f32_f32(Q6_Vsf_equals_Vw(Q6_V_hi_W(v_sums)), v_scale_hi);
-    return hvx_vec_add_f32_f32(v_lo, v_hi);
-}
-
 static void tiled_vec_dot_q4_0_32x1(const uint32_t n, float * restrict s, const void * restrict vx, const void * restrict vy, uint32_t valid_rows, const float * restrict sz) {
    const uint8_t * restrict tile_ptr = vx;
    const uint8_t * restrict y_q = vy;
@@ -853,63 +785,6 @@ static void tiled_vec_dot_q8_0_32x2(const uint32_t n, float * restrict s0, float
    }
 }

-static void tiled_vec_dot_q6_k_32x1(const uint32_t n, float * restrict s, const void * restrict vx, const void * restrict vy, uint32_t valid_rows, const float * restrict sz) {
-    const uint8_t * restrict tile_ptr = vx;
-    const uint8_t * restrict y_q = vy;
-
-    HVX_Vector v_sum_float = Q6_V_vzero();
-    HVX_Vector i32 = Q6_Vb_vsplat_R(32);
-
-    uint32_t n_k_tiles = n / 32;
-    for (uint32_t kt = 0; kt < n_k_tiles; kt++) {
-        const HVX_Vector * restrict vptr = (const HVX_Vector *) (tile_ptr + kt * 896);
-        const HVX_Vector * restrict v_act = (const HVX_Vector *) (y_q + kt * 1152);
-
-        HVX_VectorPair v_sums = accum_q6_k_32x1(vptr, v_act, i32);
-        v_sum_float = hvx_vec_add_f32_f32(v_sum_float, scale_q6_k_32x1(v_sums, vptr[6], v_act[8]));
-    }
-
-    if (sz) {
-        hvx_vec_store_u(s, valid_rows * sizeof(float), hvx_vec_add_f32_f32(v_sum_float, hvx_vmemu(sz)));
-    } else {
-        hvx_vec_store_u(s, valid_rows * sizeof(float), v_sum_float);
-    }
-}
-
-static void tiled_vec_dot_q6_k_32x2(const uint32_t n, float * restrict s0, float * restrict s1, const void * restrict vx, const void * restrict vy0, const void * restrict vy1, uint32_t valid_rows, const float * restrict sz0, const float * restrict sz1) {
-    const uint8_t * restrict tile_ptr = vx;
-    const uint8_t * restrict y0_q = vy0;
-    const uint8_t * restrict y1_q = vy1;
-
-    HVX_Vector v_sum_float_c0 = Q6_V_vzero();
-    HVX_Vector v_sum_float_c1 = Q6_V_vzero();
-    HVX_Vector i32 = Q6_Vb_vsplat_R(32);
-
-    uint32_t n_k_tiles = n / 32;
-    for (uint32_t kt = 0; kt < n_k_tiles; kt++) {
-        const HVX_Vector * restrict vptr = (const HVX_Vector *) (tile_ptr + kt * 896);
-        const HVX_Vector * restrict v_act0 = (const HVX_Vector *) (y0_q + kt * 1152);
-        const HVX_Vector * restrict v_act1 = (const HVX_Vector *) (y1_q + kt * 1152);
-
-        HVX_VectorPair v_sums0, v_sums1;
-        accum_q6_k_32x2(vptr, v_act0, v_act1, i32, &v_sums0, &v_sums1);
-
-        v_sum_float_c0 = hvx_vec_add_f32_f32(v_sum_float_c0, scale_q6_k_32x1(v_sums0, vptr[6], v_act0[8]));
-        v_sum_float_c1 = hvx_vec_add_f32_f32(v_sum_float_c1, scale_q6_k_32x1(v_sums1, vptr[6], v_act1[8]));
-    }
-
-    if (sz0) {
-        hvx_vec_store_u(s0, valid_rows * sizeof(float), hvx_vec_add_f32_f32(v_sum_float_c0, hvx_vmemu(sz0)));
-    } else {
-        hvx_vec_store_u(s0, valid_rows * sizeof(float), v_sum_float_c0);
-    }
-    if (sz1) {
-        hvx_vec_store_u(s1, valid_rows * sizeof(float), hvx_vec_add_f32_f32(v_sum_float_c1, hvx_vmemu(sz1)));
-    } else {
-        hvx_vec_store_u(s1, valid_rows * sizeof(float), v_sum_float_c1);
-    }
-}
-
 static void tiled_vec_dot_iq4nl_32x1(const uint32_t n, float * restrict s, const void * restrict vx, const void * restrict vy, uint32_t valid_rows, const float * restrict sz) {
    const uint8_t * restrict tile_ptr = vx;
    const uint8_t * restrict y_q = vy;
@@ -325,9 +325,8 @@ static void hvx_mm_4d(unsigned int nth, unsigned int ith, void * data) {
    }
 }

-// hvx kernels first: the HMX Q6_K dequantizer reuses unpack_q6_k_group from there
-#include "hvx-mm-kernels-tiled.h"
 #include "hmx-mm-kernels-tiled.h"
+#include "hvx-mm-kernels-tiled.h"
 #include "hvx-mm-kernels-flat.h"

 // Specialized repacked matmul macros
@@ -638,14 +637,12 @@ static void hvx_mm_nx_2d_repacked_##SUFFIX(unsigned int nth, unsigned int ith, v
 MATMUL_2D_REPACKED_IMPL(q4_0,       576,  tiled_vec_dot_q4_0_32x2,  tiled_vec_dot_q4_0_32x1)
 MATMUL_2D_REPACKED_IMPL(q4_1,       640,  tiled_vec_dot_q4_1_32x2,  tiled_vec_dot_q4_1_32x1)
 MATMUL_2D_REPACKED_IMPL(q8_0,       1088, tiled_vec_dot_q8_0_32x2,  tiled_vec_dot_q8_0_32x1)
-MATMUL_2D_REPACKED_IMPL(q6_k,       896,  tiled_vec_dot_q6_k_32x2,  tiled_vec_dot_q6_k_32x1)
 MATMUL_2D_REPACKED_IMPL(iq4nl,      576,  tiled_vec_dot_iq4nl_32x2, tiled_vec_dot_iq4nl_32x1)
 MATMUL_2D_REPACKED_IMPL(mxfp4,      544,  tiled_vec_dot_mxfp4_32x2, tiled_vec_dot_mxfp4_32x1)

 MATMUL_2D_REPACKED_IMPL(q4_0_flat,  576,  flat_vec_dot_q4_0_32x2,   flat_vec_dot_q4_0_32x1)
 MATMUL_2D_REPACKED_IMPL(q4_1_flat,  640,  flat_vec_dot_q4_1_32x2,   flat_vec_dot_q4_1_32x1)
 MATMUL_2D_REPACKED_IMPL(q8_0_flat,  1088, flat_vec_dot_q8_0_32x2,   flat_vec_dot_q8_0_32x1)
-MATMUL_2D_REPACKED_IMPL(q6_k_flat,  896,  flat_vec_dot_q6_k_32x2,   flat_vec_dot_q6_k_32x1)
 MATMUL_2D_REPACKED_IMPL(iq4nl_flat, 576,  flat_vec_dot_iq4nl_32x2,  flat_vec_dot_iq4nl_32x1)
 MATMUL_2D_REPACKED_IMPL(mxfp4_flat, 544,  flat_vec_dot_mxfp4_32x2,  flat_vec_dot_mxfp4_32x1)

@@ -740,14 +737,12 @@ static void quantize_f32_q8_1_tiled_block(unsigned int nth, unsigned int ith, vo
 MATVEC_2D_REPACKED_IMPL(q4_0,       576,  tiled_vec_dot_q4_0_32x1)
 MATVEC_2D_REPACKED_IMPL(q4_1,       640,  tiled_vec_dot_q4_1_32x1)
 MATVEC_2D_REPACKED_IMPL(q8_0,       1088, tiled_vec_dot_q8_0_32x1)
-MATVEC_2D_REPACKED_IMPL(q6_k,       896,  tiled_vec_dot_q6_k_32x1)
 MATVEC_2D_REPACKED_IMPL(iq4nl,      576,  tiled_vec_dot_iq4nl_32x1)
 MATVEC_2D_REPACKED_IMPL(mxfp4,      544,  tiled_vec_dot_mxfp4_32x1)

 MATVEC_2D_REPACKED_IMPL(q4_0_flat,  576,  flat_vec_dot_q4_0_32x1)
 MATVEC_2D_REPACKED_IMPL(q4_1_flat,  640,  flat_vec_dot_q4_1_32x1)
 MATVEC_2D_REPACKED_IMPL(q8_0_flat,  1088, flat_vec_dot_q8_0_32x1)
-MATVEC_2D_REPACKED_IMPL(q6_k_flat,  896,  flat_vec_dot_q6_k_32x1)
 MATVEC_2D_REPACKED_IMPL(iq4nl_flat, 576,  flat_vec_dot_iq4nl_32x1)
 MATVEC_2D_REPACKED_IMPL(mxfp4_flat, 544,  flat_vec_dot_mxfp4_32x1)

@@ -1344,7 +1339,6 @@ static int hvx_mm_init_vec_dot(struct htp_mm_context * mmctx, enum htp_data_type
            mmctx->vec_dot_32x1 = tiled_vec_dot_q4_0_32x1;
            return 0;
        case HTP_TYPE_Q4_1:
-        case HTP_TYPE_Q4_K:
            mmctx->type         = "q4_1_tiled-f32";
            mmctx->vec_dot_32x1 = tiled_vec_dot_q4_1_32x1;
            return 0;
@@ -1352,10 +1346,6 @@ static int hvx_mm_init_vec_dot(struct htp_mm_context * mmctx, enum htp_data_type
            mmctx->type         = "q8_0_tiled-f32";
            mmctx->vec_dot_32x1 = tiled_vec_dot_q8_0_32x1;
            return 0;
-        case HTP_TYPE_Q6_K:
-            mmctx->type         = "q6_k_tiled-f32";
-            mmctx->vec_dot_32x1 = tiled_vec_dot_q6_k_32x1;
-            return 0;
        case HTP_TYPE_IQ4_NL:
            mmctx->type         = "iq4nl_tiled-f32";
            mmctx->vec_dot_32x1 = tiled_vec_dot_iq4nl_32x1;
@@ -1405,8 +1395,7 @@ static int hvx_mm_matmul(struct htp_ops_context * octx) {

    bool is_repacked = (src0->type == HTP_TYPE_Q4_0 || src0->type == HTP_TYPE_Q4_1 ||
                        src0->type == HTP_TYPE_Q8_0 || src0->type == HTP_TYPE_IQ4_NL ||
-                        src0->type == HTP_TYPE_MXFP4 || src0->type == HTP_TYPE_Q6_K ||
-                        src0->type == HTP_TYPE_Q4_K);
+                        src0->type == HTP_TYPE_MXFP4);

    // Compute src0_nrows_per_thread
    mmctx->src0_nrows_per_thread  = fastdiv(nrows + octx->n_threads - 1, &octx->n_threads_div);
@@ -1430,10 +1419,8 @@ static int hvx_mm_matmul(struct htp_ops_context * octx) {
        if (is_repacked) {
            switch (src0->type) {
                case HTP_TYPE_Q4_0:   matmul_job_func = hvx_mm_2d_repacked_q4_0;   break;
-                case HTP_TYPE_Q4_1:
-                case HTP_TYPE_Q4_K:   matmul_job_func = hvx_mm_2d_repacked_q4_1;   break;
+                case HTP_TYPE_Q4_1:   matmul_job_func = hvx_mm_2d_repacked_q4_1;   break;
                case HTP_TYPE_Q8_0:   matmul_job_func = hvx_mm_2d_repacked_q8_0;   break;
-                case HTP_TYPE_Q6_K:   matmul_job_func = hvx_mm_2d_repacked_q6_k;   break;
                case HTP_TYPE_IQ4_NL: matmul_job_func = hvx_mm_2d_repacked_iq4nl;  break;
                case HTP_TYPE_MXFP4:  matmul_job_func = hvx_mm_2d_repacked_mxfp4;  break;
                default:              return HTP_STATUS_NO_SUPPORT;
@@ -1445,10 +1432,8 @@ static int hvx_mm_matmul(struct htp_ops_context * octx) {
        if (is_repacked) {
            switch (src0->type) {
                case HTP_TYPE_Q4_0:   matmul_job_func = hvx_mv_2d_repacked_q4_0;   break;
-                case HTP_TYPE_Q4_1:
-                case HTP_TYPE_Q4_K:   matmul_job_func = hvx_mv_2d_repacked_q4_1;   break;
+                case HTP_TYPE_Q4_1:   matmul_job_func = hvx_mv_2d_repacked_q4_1;   break;
                case HTP_TYPE_Q8_0:   matmul_job_func = hvx_mv_2d_repacked_q8_0;   break;
-                case HTP_TYPE_Q6_K:   matmul_job_func = hvx_mv_2d_repacked_q6_k;   break;
                case HTP_TYPE_IQ4_NL: matmul_job_func = hvx_mv_2d_repacked_iq4nl;  break;
                case HTP_TYPE_MXFP4:  matmul_job_func = hvx_mv_2d_repacked_mxfp4;  break;
                default:              return HTP_STATUS_NO_SUPPORT;
@@ -1520,16 +1505,14 @@ static int hvx_mm_matmul(struct htp_ops_context * octx) {

        case HTP_MM_KERNEL_HVX_QUANT_ROW_FLAT: {
            n_quant_tasks = MIN(src1_nrows, octx->n_threads);
-            quant_task_func = (src0->type == HTP_TYPE_Q4_1 || src0->type == HTP_TYPE_Q4_K) ? quantize_f32_q8_1_flat : quantize_f32_q8_0_flat;
-            src1_row_size = (src0->type == HTP_TYPE_Q4_1 || src0->type == HTP_TYPE_Q4_K) ? htp_mm_q8_1_flat_row_size(ne10) : htp_mm_q8_0_flat_row_size(ne10);
+            quant_task_func = (src0->type == HTP_TYPE_Q4_1) ? quantize_f32_q8_1_flat : quantize_f32_q8_0_flat;
+            src1_row_size = (src0->type == HTP_TYPE_Q4_1) ? htp_mm_q8_1_flat_row_size(ne10) : htp_mm_q8_0_flat_row_size(ne10);

            if (src1_nrows > 1) {
                switch (src0->type) {
                    case HTP_TYPE_Q4_0:   matmul_job_func = hvx_mm_2d_repacked_q4_0_flat;   break;
-                    case HTP_TYPE_Q4_1:
-                    case HTP_TYPE_Q4_K:   matmul_job_func = hvx_mm_2d_repacked_q4_1_flat;   break;
+                    case HTP_TYPE_Q4_1:   matmul_job_func = hvx_mm_2d_repacked_q4_1_flat;   break;
                    case HTP_TYPE_Q8_0:   matmul_job_func = hvx_mm_2d_repacked_q8_0_flat;   break;
-                    case HTP_TYPE_Q6_K:   matmul_job_func = hvx_mm_2d_repacked_q6_k_flat;   break;
                    case HTP_TYPE_IQ4_NL: matmul_job_func = hvx_mm_2d_repacked_iq4nl_flat;  break;
                    case HTP_TYPE_MXFP4:  matmul_job_func = hvx_mm_2d_repacked_mxfp4_flat;  break;
                    default:              return HTP_STATUS_NO_SUPPORT;
@@ -1537,10 +1520,8 @@ static int hvx_mm_matmul(struct htp_ops_context * octx) {
            } else {
                switch (src0->type) {
                    case HTP_TYPE_Q4_0:   matmul_job_func = hvx_mv_2d_repacked_q4_0_flat;   break;
-                    case HTP_TYPE_Q4_1:
-                    case HTP_TYPE_Q4_K:   matmul_job_func = hvx_mv_2d_repacked_q4_1_flat;   break;
+                    case HTP_TYPE_Q4_1:   matmul_job_func = hvx_mv_2d_repacked_q4_1_flat;   break;
                    case HTP_TYPE_Q8_0:   matmul_job_func = hvx_mv_2d_repacked_q8_0_flat;   break;
-                    case HTP_TYPE_Q6_K:   matmul_job_func = hvx_mv_2d_repacked_q6_k_flat;   break;
                    case HTP_TYPE_IQ4_NL: matmul_job_func = hvx_mv_2d_repacked_iq4nl_flat;  break;
                    case HTP_TYPE_MXFP4:  matmul_job_func = hvx_mv_2d_repacked_mxfp4_flat;  break;
                    default:              return HTP_STATUS_NO_SUPPORT;
@@ -1562,7 +1543,7 @@ static int hvx_mm_matmul(struct htp_ops_context * octx) {

            if (src1_nrows < octx->n_threads) {
                n_quant_tasks = MIN(total_nb, octx->n_threads);
-                quant_task_func = (src0->type == HTP_TYPE_Q4_1 || src0->type == HTP_TYPE_Q4_K) ? quantize_f32_q8_1_tiled_block : quantize_f32_q8_0_tiled_block;
+                quant_task_func = (src0->type == HTP_TYPE_Q4_1) ? quantize_f32_q8_1_tiled_block : quantize_f32_q8_0_tiled_block;
                for (uint32_t ith = 0; ith < n_quant_tasks; ++ith) {
                    uint32_t ib_first = (total_nb * ith) / n_quant_tasks;
                    uint32_t ib_last  = (total_nb * (ith + 1)) / n_quant_tasks;
@@ -1573,9 +1554,9 @@ static int hvx_mm_matmul(struct htp_ops_context * octx) {
                }
            } else {
                n_quant_tasks = MIN(src1_nrows, octx->n_threads);
-                quant_task_func = (src0->type == HTP_TYPE_Q4_1 || src0->type == HTP_TYPE_Q4_K) ? quantize_f32_q8_1_tiled : quantize_f32_q8_0_tiled;
+                quant_task_func = (src0->type == HTP_TYPE_Q4_1) ? quantize_f32_q8_1_tiled : quantize_f32_q8_0_tiled;
            }
-            src1_row_size = (src0->type == HTP_TYPE_Q4_1 || src0->type == HTP_TYPE_Q4_K) ? htp_mm_q8_1_tiled_row_size(ne10) : htp_mm_q8_0_tiled_row_size(ne10);
+            src1_row_size = (src0->type == HTP_TYPE_Q4_1) ? htp_mm_q8_1_tiled_row_size(ne10) : htp_mm_q8_0_tiled_row_size(ne10);
            break;
    }

@@ -1761,7 +1742,6 @@ DEQUANTIZE_WORKER_LOOP_IMPL(q4_1)
 DEQUANTIZE_WORKER_LOOP_IMPL(iq4_nl)
 DEQUANTIZE_WORKER_LOOP_IMPL(mxfp4)
 DEQUANTIZE_WORKER_LOOP_IMPL(q8_0)
-DEQUANTIZE_WORKER_LOOP_IMPL(q6_k)

 static void convert_f16_worker_loop(unsigned int n, unsigned int i, void *data) {
    tiled_dequantize_state_t *state = (tiled_dequantize_state_t *)data;
@@ -2496,11 +2476,9 @@ static int hmx_mm_2d_f32(struct htp_context *ctx,
    switch (weight_type) {
        case HTP_TYPE_Q4_0:   dequant_worker_fn = dequantize_tiled_worker_loop_q4_0; break;
        case HTP_TYPE_IQ4_NL: dequant_worker_fn = dequantize_tiled_worker_loop_iq4_nl; break;
-        case HTP_TYPE_Q4_1:
-        case HTP_TYPE_Q4_K:   dequant_worker_fn = dequantize_tiled_worker_loop_q4_1; break;
+        case HTP_TYPE_Q4_1:   dequant_worker_fn = dequantize_tiled_worker_loop_q4_1; break;
        case HTP_TYPE_MXFP4:  dequant_worker_fn = dequantize_tiled_worker_loop_mxfp4; break;
        case HTP_TYPE_Q8_0:   dequant_worker_fn = dequantize_tiled_worker_loop_q8_0; break;
-        case HTP_TYPE_Q6_K:   dequant_worker_fn = dequantize_tiled_worker_loop_q6_k; break;
        case HTP_TYPE_F16:    dequant_worker_fn = convert_f16_worker_loop; break;
        case HTP_TYPE_F32:    dequant_worker_fn = quantize_f32_worker_loop; break;
        default:
@@ -2754,11 +2732,9 @@ static int hmx_mm_nx_2d_f32(struct htp_ops_context * octx, const struct htp_mm_k
    switch (weight_type) {
        case HTP_TYPE_Q4_0:   dequant_worker_fn = dequantize_tiled_worker_loop_q4_0; break;
        case HTP_TYPE_IQ4_NL: dequant_worker_fn = dequantize_tiled_worker_loop_iq4_nl; break;
-        case HTP_TYPE_Q4_1:
-        case HTP_TYPE_Q4_K:   dequant_worker_fn = dequantize_tiled_worker_loop_q4_1; break;
+        case HTP_TYPE_Q4_1:   dequant_worker_fn = dequantize_tiled_worker_loop_q4_1; break;
        case HTP_TYPE_MXFP4:  dequant_worker_fn = dequantize_tiled_worker_loop_mxfp4; break;
        case HTP_TYPE_Q8_0:   dequant_worker_fn = dequantize_tiled_worker_loop_q8_0; break;
-        case HTP_TYPE_Q6_K:   dequant_worker_fn = dequantize_tiled_worker_loop_q6_k; break;
        case HTP_TYPE_F16:    dequant_worker_fn = convert_f16_worker_loop; break;
        case HTP_TYPE_F32:    dequant_worker_fn = quantize_f32_worker_loop; break;
        default:
@@ -3348,11 +3324,9 @@ static int hmx_mm_id_2d_f32(struct htp_context *ctx,
    switch (weight_type) {
        case HTP_TYPE_Q4_0:   dequant_worker_fn = dequantize_tiled_worker_loop_q4_0; break;
        case HTP_TYPE_IQ4_NL: dequant_worker_fn = dequantize_tiled_worker_loop_iq4_nl; break;
-        case HTP_TYPE_Q4_1:
-        case HTP_TYPE_Q4_K:   dequant_worker_fn = dequantize_tiled_worker_loop_q4_1; break;
+        case HTP_TYPE_Q4_1:   dequant_worker_fn = dequantize_tiled_worker_loop_q4_1; break;
        case HTP_TYPE_MXFP4:  dequant_worker_fn = dequantize_tiled_worker_loop_mxfp4; break;
        case HTP_TYPE_Q8_0:   dequant_worker_fn = dequantize_tiled_worker_loop_q8_0; break;
-        case HTP_TYPE_Q6_K:   dequant_worker_fn = dequantize_tiled_worker_loop_q6_k; break;
        case HTP_TYPE_F16:    dequant_worker_fn = convert_f16_worker_loop; break;
        case HTP_TYPE_F32:    dequant_worker_fn = quantize_f32_worker_loop; break;
        default:
@@ -3652,7 +3626,7 @@ static int hvx_mm_matmul_id(
    uint32_t n_quant_tasks = 1;
    if (src1_nrows < octx->n_threads) {
        n_quant_tasks = MIN(total_nb, octx->n_threads);
-        quant_task_func = (src0->type == HTP_TYPE_Q4_1 || src0->type == HTP_TYPE_Q4_K) ? quantize_f32_q8_1_tiled_block : quantize_f32_q8_0_tiled_block;
+        quant_task_func = (src0->type == HTP_TYPE_Q4_1) ? quantize_f32_q8_1_tiled_block : quantize_f32_q8_0_tiled_block;
        for (uint32_t ith = 0; ith < n_quant_tasks; ++ith) {
            uint32_t ib_first = (total_nb * ith) / n_quant_tasks;
            uint32_t ib_last  = (total_nb * (ith + 1)) / n_quant_tasks;
@@ -3663,9 +3637,9 @@ static int hvx_mm_matmul_id(
        }
    } else {
        n_quant_tasks = MIN(src1_nrows, octx->n_threads);
-        quant_task_func = (src0->type == HTP_TYPE_Q4_1 || src0->type == HTP_TYPE_Q4_K) ? quantize_f32_q8_1_tiled : quantize_f32_q8_0_tiled;
+        quant_task_func = (src0->type == HTP_TYPE_Q4_1) ? quantize_f32_q8_1_tiled : quantize_f32_q8_0_tiled;
    }
-    size_t src1_row_size  = (src0->type == HTP_TYPE_Q4_1 || src0->type == HTP_TYPE_Q4_K) ? htp_mm_q8_1_tiled_row_size(ne10) : htp_mm_q8_0_tiled_row_size(ne10);
+    size_t src1_row_size  = (src0->type == HTP_TYPE_Q4_1) ? htp_mm_q8_1_tiled_row_size(ne10) : htp_mm_q8_0_tiled_row_size(ne10);

    struct htp_mm_hvx_vtcm_layout L;
    htp_mm_hvx_vtcm_layout_build(&L, kparams->kernel_type, src0->type, ne10, src1_nrows, octx->n_threads,
@@ -3799,7 +3773,7 @@ static int hvx_mm_matmul_id_nx(
    uint32_t n_quant_tasks = 1;
    if (src1_nrows < octx->n_threads) {
        n_quant_tasks = MIN(total_nb, octx->n_threads);
-        quant_task_func = (src0->type == HTP_TYPE_Q4_1 || src0->type == HTP_TYPE_Q4_K) ? quantize_f32_q8_1_tiled_block : quantize_f32_q8_0_tiled_block;
+        quant_task_func = (src0->type == HTP_TYPE_Q4_1) ? quantize_f32_q8_1_tiled_block : quantize_f32_q8_0_tiled_block;
        for (uint32_t ith = 0; ith < n_quant_tasks; ++ith) {
            uint32_t ib_first = (total_nb * ith) / n_quant_tasks;
            uint32_t ib_last  = (total_nb * (ith + 1)) / n_quant_tasks;
@@ -3810,9 +3784,9 @@ static int hvx_mm_matmul_id_nx(
        }
    } else {
        n_quant_tasks = MIN(src1_nrows, octx->n_threads);
-        quant_task_func = (src0->type == HTP_TYPE_Q4_1 || src0->type == HTP_TYPE_Q4_K) ? quantize_f32_q8_1_tiled : quantize_f32_q8_0_tiled;
+        quant_task_func = (src0->type == HTP_TYPE_Q4_1) ? quantize_f32_q8_1_tiled : quantize_f32_q8_0_tiled;
    }
-    size_t src1_row_size = (src0->type == HTP_TYPE_Q4_1 || src0->type == HTP_TYPE_Q4_K) ? htp_mm_q8_1_tiled_row_size(act->ne[0]) : htp_mm_q8_0_tiled_row_size(act->ne[0]);
+    size_t src1_row_size = (src0->type == HTP_TYPE_Q4_1) ? htp_mm_q8_1_tiled_row_size(act->ne[0]) : htp_mm_q8_0_tiled_row_size(act->ne[0]);

    struct htp_mm_hvx_vtcm_layout L;
    htp_mm_hvx_vtcm_layout_build(&L, kparams->kernel_type, src0->type, act->ne[0], src1_nrows, octx->n_threads,
@@ -4155,7 +4129,7 @@ int op_matmul_nx(struct htp_ops_context * octx) {

    bool is_repacked = (src0->type == HTP_TYPE_Q4_0 || src0->type == HTP_TYPE_Q4_1 ||
                        src0->type == HTP_TYPE_Q8_0 || src0->type == HTP_TYPE_IQ4_NL ||
-                        src0->type == HTP_TYPE_MXFP4 || src0->type == HTP_TYPE_Q4_K);
+                        src0->type == HTP_TYPE_MXFP4);

    struct htp_mm_context mmctx_struct = {0};
    struct htp_mm_context * mmctx = &mmctx_struct;
@@ -4179,10 +4153,10 @@ int op_matmul_nx(struct htp_ops_context * octx) {
    uint32_t n_quant_tasks = 1;
    if (kparams->kernel_type == HTP_MM_KERNEL_HVX_QUANT_ROW_FLAT) {
        n_quant_tasks = MIN(src1_nrows, octx->n_threads);
-        quant_task_func = (src0->type == HTP_TYPE_Q4_1 || src0->type == HTP_TYPE_Q4_K) ? quantize_f32_q8_1_flat : quantize_f32_q8_0_flat;
+        quant_task_func = (src0->type == HTP_TYPE_Q4_1) ? quantize_f32_q8_1_flat : quantize_f32_q8_0_flat;
    } else if (src1_nrows < octx->n_threads) {
        n_quant_tasks = MIN(total_nb, octx->n_threads);
-        quant_task_func = (src0->type == HTP_TYPE_Q4_1 || src0->type == HTP_TYPE_Q4_K) ? quantize_f32_q8_1_tiled_block : quantize_f32_q8_0_tiled_block;
+        quant_task_func = (src0->type == HTP_TYPE_Q4_1) ? quantize_f32_q8_1_tiled_block : quantize_f32_q8_0_tiled_block;
        for (uint32_t ith = 0; ith < n_quant_tasks; ++ith) {
            uint32_t ib_first = (total_nb * ith) / n_quant_tasks;
            uint32_t ib_last  = (total_nb * (ith + 1)) / n_quant_tasks;
@@ -4193,14 +4167,14 @@ int op_matmul_nx(struct htp_ops_context * octx) {
        }
    } else {
        n_quant_tasks = MIN(src1_nrows, octx->n_threads);
-        quant_task_func = (src0->type == HTP_TYPE_Q4_1 || src0->type == HTP_TYPE_Q4_K) ? quantize_f32_q8_1_tiled : quantize_f32_q8_0_tiled;
+        quant_task_func = (src0->type == HTP_TYPE_Q4_1) ? quantize_f32_q8_1_tiled : quantize_f32_q8_0_tiled;
    }

    size_t src1_row_size;
    if (kparams->kernel_type == HTP_MM_KERNEL_HVX_QUANT_ROW_FLAT) {
-        src1_row_size = (src0->type == HTP_TYPE_Q4_1 || src0->type == HTP_TYPE_Q4_K) ? htp_mm_q8_1_flat_row_size(act->ne[0]) : htp_mm_q8_0_flat_row_size(act->ne[0]);
+        src1_row_size = (src0->type == HTP_TYPE_Q4_1) ? htp_mm_q8_1_flat_row_size(act->ne[0]) : htp_mm_q8_0_flat_row_size(act->ne[0]);
    } else {
-        src1_row_size = (src0->type == HTP_TYPE_Q4_1 || src0->type == HTP_TYPE_Q4_K) ? htp_mm_q8_1_tiled_row_size(act->ne[0]) : htp_mm_q8_0_tiled_row_size(act->ne[0]);
+        src1_row_size = (src0->type == HTP_TYPE_Q4_1) ? htp_mm_q8_1_tiled_row_size(act->ne[0]) : htp_mm_q8_0_tiled_row_size(act->ne[0]);
    }

    struct htp_mm_hvx_vtcm_layout L;
@@ -4245,8 +4219,7 @@ int op_matmul_nx(struct htp_ops_context * octx) {
        if (kparams->kernel_type == HTP_MM_KERNEL_HVX_QUANT_ROW_FLAT) {
            switch (src0->type) {
                case HTP_TYPE_Q4_0:   matmul_job_func = hvx_mm_nx_2d_repacked_q4_0_flat;   break;
-                case HTP_TYPE_Q4_1:
-                case HTP_TYPE_Q4_K:   matmul_job_func = hvx_mm_nx_2d_repacked_q4_1_flat;   break;
+                case HTP_TYPE_Q4_1:   matmul_job_func = hvx_mm_nx_2d_repacked_q4_1_flat;   break;
                case HTP_TYPE_Q8_0:   matmul_job_func = hvx_mm_nx_2d_repacked_q8_0_flat;   break;
                case HTP_TYPE_IQ4_NL: matmul_job_func = hvx_mm_nx_2d_repacked_iq4nl_flat;  break;
                case HTP_TYPE_MXFP4:  matmul_job_func = hvx_mm_nx_2d_repacked_mxfp4_flat;  break;
@@ -4255,8 +4228,7 @@ int op_matmul_nx(struct htp_ops_context * octx) {
        } else {
            switch (src0->type) {
                case HTP_TYPE_Q4_0:   matmul_job_func = hvx_mm_nx_2d_repacked_q4_0;   break;
-                case HTP_TYPE_Q4_1:
-                case HTP_TYPE_Q4_K:   matmul_job_func = hvx_mm_nx_2d_repacked_q4_1;   break;
+                case HTP_TYPE_Q4_1:   matmul_job_func = hvx_mm_nx_2d_repacked_q4_1;   break;
                case HTP_TYPE_Q8_0:   matmul_job_func = hvx_mm_nx_2d_repacked_q8_0;   break;
                case HTP_TYPE_IQ4_NL: matmul_job_func = hvx_mm_nx_2d_repacked_iq4nl;  break;
                case HTP_TYPE_MXFP4:  matmul_job_func = hvx_mm_nx_2d_repacked_mxfp4;  break;
@@ -25,11 +25,6 @@ extern "C" {
 #define HTP_MM_WEIGHT_TILE_SIZE_Q8_0   1088
 #define HTP_MM_WEIGHT_TILE_SIZE_IQ4_NL 576
 #define HTP_MM_WEIGHT_TILE_SIZE_MXFP4  544
-// Q6_K native 6-bit tile (32 rows x 32 k), vrmpy-ready: byte 4*row+b of a vector holds k = 4*group+b
-//   vectors 0..3: low nibbles, vector i holds group 2i (low nibble) and group 2i+1 (high nibble)
-//   vectors 4..5: high 2 bits, vector m holds groups 4m..4m+3 at bit offsets 0,2,4,6
-//   vector 6: fp16 scales per row, d * scales[]: k 0..15 in lanes 0..31, k 16..31 in lanes 32..63
-#define HTP_MM_WEIGHT_TILE_SIZE_Q6_K   896

 // --- Weight Repacked Aligned Tile Sizes ---
 #define HTP_MM_WEIGHT_ALIGNED_TILE_SIZE_Q4_0   640
@@ -37,7 +32,6 @@ extern "C" {
 #define HTP_MM_WEIGHT_ALIGNED_TILE_SIZE_Q8_0   1152
 #define HTP_MM_WEIGHT_ALIGNED_TILE_SIZE_IQ4_NL 640
 #define HTP_MM_WEIGHT_ALIGNED_TILE_SIZE_MXFP4  640
-#define HTP_MM_WEIGHT_ALIGNED_TILE_SIZE_Q6_K   896

 // --- Activation Tiled Block Sizes (including padding) ---
 #define HTP_MM_ACT_TILE_SIZE_Q8_0      1152
@@ -201,12 +195,9 @@ static inline uint32_t htp_mm_get_weight_tile_size(int weight_type) {
        case HTP_TYPE_IQ4_NL:
            return HTP_MM_WEIGHT_TILE_SIZE_Q4_0;
        case HTP_TYPE_Q4_1:
-        case HTP_TYPE_Q4_K:
            return HTP_MM_WEIGHT_TILE_SIZE_Q4_1;
        case HTP_TYPE_Q8_0:
            return HTP_MM_WEIGHT_TILE_SIZE_Q8_0;
-        case HTP_TYPE_Q6_K:
-            return HTP_MM_WEIGHT_TILE_SIZE_Q6_K;
        case HTP_TYPE_MXFP4:
            return HTP_MM_WEIGHT_TILE_SIZE_MXFP4;
        default:
@@ -220,12 +211,9 @@ static inline uint32_t htp_mm_get_weight_aligned_tile_size(int weight_type) {
        case HTP_TYPE_IQ4_NL:
            return HTP_MM_WEIGHT_ALIGNED_TILE_SIZE_Q4_0;
        case HTP_TYPE_Q4_1:
-        case HTP_TYPE_Q4_K:
            return HTP_MM_WEIGHT_ALIGNED_TILE_SIZE_Q4_1;
        case HTP_TYPE_Q8_0:
            return HTP_MM_WEIGHT_ALIGNED_TILE_SIZE_Q8_0;
-        case HTP_TYPE_Q6_K:
-            return HTP_MM_WEIGHT_ALIGNED_TILE_SIZE_Q6_K;
        case HTP_TYPE_MXFP4:
            return HTP_MM_WEIGHT_ALIGNED_TILE_SIZE_MXFP4;
        default:
@@ -266,9 +254,7 @@ static inline size_t htp_mm_get_tiled_row_stride(int weight_type, uint32_t k) {
        case HTP_TYPE_Q4_0:
        case HTP_TYPE_IQ4_NL:
        case HTP_TYPE_Q4_1:
-        case HTP_TYPE_Q4_K:
        case HTP_TYPE_Q8_0:
-        case HTP_TYPE_Q6_K:
        case HTP_TYPE_MXFP4:
            return (size_t) nb * htp_mm_get_weight_tile_size(weight_type);
        case HTP_TYPE_F16:
@@ -498,8 +484,7 @@ static inline void htp_mm_hvx_vtcm_layout_build(

    const bool is_repack = (wtype == HTP_TYPE_Q4_0 || wtype == HTP_TYPE_Q4_1 ||
                            wtype == HTP_TYPE_Q8_0 || wtype == HTP_TYPE_IQ4_NL ||
-                            wtype == HTP_TYPE_MXFP4 || wtype == HTP_TYPE_Q6_K ||
-                            wtype == HTP_TYPE_Q4_K);
+                            wtype == HTP_TYPE_MXFP4);

    if (is_fused_nx) {
        const size_t src0_row_size_padded = hex_round_up(src0_row_size, 128);
@@ -517,8 +502,8 @@ static inline void htp_mm_hvx_vtcm_layout_build(
            weight_sz_per_thread = hex_round_up(n_prefetch * src0_row_size_padded, 128);
        }

-        size_t flat_act_row_size  = (wtype == HTP_TYPE_Q4_1 || wtype == HTP_TYPE_Q4_K) ? htp_mm_q8_1_flat_row_size(ne10)  : htp_mm_q8_0_flat_row_size(ne10);
-        size_t tiled_act_row_size = (wtype == HTP_TYPE_Q4_1 || wtype == HTP_TYPE_Q4_K) ? htp_mm_q8_1_tiled_row_size(ne10) : htp_mm_q8_0_tiled_row_size(ne10);
+        size_t flat_act_row_size  = (wtype == HTP_TYPE_Q4_1) ? htp_mm_q8_1_flat_row_size(ne10)  : htp_mm_q8_0_flat_row_size(ne10);
+        size_t tiled_act_row_size = (wtype == HTP_TYPE_Q4_1) ? htp_mm_q8_1_tiled_row_size(ne10) : htp_mm_q8_0_tiled_row_size(ne10);

        size_t act_sz = (kernel_type == HTP_MM_KERNEL_HVX_QUANT_ROW_FLAT)
            ? hex_round_up(flat_act_row_size  * src1_nrows, 128)
@@ -531,8 +516,8 @@ static inline void htp_mm_hvx_vtcm_layout_build(
        dst_sz  = quant_scratch_size;
    } else if (is_matmul_id) {
        const size_t src0_row_size_padded = htp_mm_round_up(src0_row_size, 128);
-        const size_t src1_row_size_tiled = (wtype == HTP_TYPE_Q4_1 || wtype == HTP_TYPE_Q4_K) ? htp_mm_q8_1_tiled_row_size(ne10)
-                                                                                               : htp_mm_q8_0_tiled_row_size(ne10);
+        const size_t src1_row_size_tiled = (wtype == HTP_TYPE_Q4_1) ? htp_mm_q8_1_tiled_row_size(ne10)
+                                                                    : htp_mm_q8_0_tiled_row_size(ne10);

        size_t src0_sz_per_thread = htp_mm_round_up(n_prefetch * src0_row_size_padded, 256);
        src1_sz                   = htp_mm_round_up(src1_row_size_tiled * src1_nrows, 256);
@@ -577,7 +562,7 @@ static inline void htp_mm_hvx_vtcm_layout_build(
            }
            case HTP_MM_KERNEL_HVX_QUANT_BLOCK:
            case HTP_MM_KERNEL_HVX_QUANT_ROW: {
-                size_t q_src1_row_size = (wtype == HTP_TYPE_Q4_1 || wtype == HTP_TYPE_Q4_K) ? htp_mm_q8_1_tiled_row_size(ne10) : htp_mm_q8_0_tiled_row_size(ne10);
+                size_t q_src1_row_size = (wtype == HTP_TYPE_Q4_1) ? htp_mm_q8_1_tiled_row_size(ne10) : htp_mm_q8_0_tiled_row_size(ne10);

                src0_sz = htp_mm_round_up(n_prefetch * src0_row_size_padded, 256);
                src1_sz = htp_mm_round_up(q_src1_row_size * src1_nrows, 256);
@@ -599,7 +584,7 @@ static inline void htp_mm_hvx_vtcm_layout_build(
                break;
            }
            case HTP_MM_KERNEL_HVX_QUANT_ROW_FLAT: {
-                size_t q_src1_row_size = (wtype == HTP_TYPE_Q4_1 || wtype == HTP_TYPE_Q4_K) ? htp_mm_q8_1_flat_row_size(ne10) : htp_mm_q8_0_flat_row_size(ne10);
+                size_t q_src1_row_size = (wtype == HTP_TYPE_Q4_1) ? htp_mm_q8_1_flat_row_size(ne10) : htp_mm_q8_0_flat_row_size(ne10);

                src0_sz = htp_mm_round_up(n_prefetch * src0_row_size_padded, 256);
                src1_sz = htp_mm_round_up(q_src1_row_size * src1_nrows, 256);
@@ -1063,40 +1063,6 @@ ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_mul_mv(ggml_meta
    return res;
 }

-ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_mul_mm_id_amax_part(ggml_metal_library_t lib) {
-    char base[256];
-    char name[256];
-
-    snprintf(base, 256, "kernel_mul_mm_id_amax_part_f32");
-    snprintf(name, 256, "%s", base);
-
-    ggml_metal_pipeline_with_params res = ggml_metal_library_get_pipeline(lib, name);
-    if (!res.pipeline) {
-        res = ggml_metal_library_compile_pipeline(lib, base, name, nullptr);
-    }
-
-    res.smem = 32*sizeof(float);
-
-    return res;
-}
-
-ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_mul_mm_id_amax(ggml_metal_library_t lib) {
-    char base[256];
-    char name[256];
-
-    snprintf(base, 256, "kernel_mul_mm_id_amax_f32");
-    snprintf(name, 256, "%s", base);
-
-    ggml_metal_pipeline_with_params res = ggml_metal_library_get_pipeline(lib, name);
-    if (!res.pipeline) {
-        res = ggml_metal_library_compile_pipeline(lib, base, name, nullptr);
-    }
-
-    res.smem = 32*sizeof(float);
-
-    return res;
-}
-
 ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_mul_mm_id_map0(ggml_metal_library_t lib, int ne02, int ne20) {
    char base[256];
    char name[256];
@@ -138,8 +138,6 @@ struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_mul_mv_ex
 struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_mul_mm            (ggml_metal_library_t lib, const struct ggml_tensor * op);
 struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_mul_mv            (ggml_metal_library_t lib, const struct ggml_tensor * op);
 struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_mul_mm_id_map0    (ggml_metal_library_t lib, int ne02, int ne20);
-struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_mul_mm_id_amax(ggml_metal_library_t lib);
-struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_mul_mm_id_amax_part(ggml_metal_library_t lib);
 struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_mul_mm_id         (ggml_metal_library_t lib, const struct ggml_tensor * op);
 struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_mul_mv_id         (ggml_metal_library_t lib, const struct ggml_tensor * op);
 struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_argmax            (ggml_metal_library_t lib, const struct ggml_tensor * op);
@@ -1803,7 +1803,6 @@ bool ggml_metal_device_supports_op(ggml_metal_device_t dev, const struct ggml_te
                op->type         == GGML_TYPE_F32 &&
                op->src[0]->ne[1] == 4 &&
                op->src[1]->ne[0] == 4 &&
-                op->src[1]->ne[2] == 1 &&
                ggml_is_contiguous_rows(op->src[0]) &&
                ggml_is_contiguous_rows(op->src[1]);
        case GGML_OP_DSV4_HC_POST:
@@ -1811,7 +1810,6 @@ bool ggml_metal_device_supports_op(ggml_metal_device_t dev, const struct ggml_te
                op->src[0]->type == GGML_TYPE_F32 &&
                op->src[1]->type == GGML_TYPE_F32 &&
                op->src[2]->type == GGML_TYPE_F32 &&
-                op->src[3] != NULL &&
                op->src[3]->type == GGML_TYPE_F32 &&
                op->type         == GGML_TYPE_F32 &&
                op->src[1]->ne[1] == 4 &&
@@ -14,8 +14,6 @@
 #define N_MM_SIMD_GROUP_X 2
 #define N_MM_SIMD_GROUP_Y 2

-#define N_MM_NPART_AMAX 256
-
 // kernel parameters for mat-vec threadgroups
 //
 // N_R0: number of src0 rows to process per simdgroup
@@ -557,14 +555,6 @@ typedef struct {
    uint64_t nb21;
 } ggml_metal_kargs_mul_mm_id_map0;

-typedef struct {
-    int32_t  ne00;
-    int32_t  ne01;
-    int32_t  ne02;
-    uint64_t nb01;
-    uint64_t nb02;
-} ggml_metal_kargs_mul_mm_id_amax;
-
 typedef struct {
    int32_t  ne00;
    int32_t  ne02;
@@ -2631,15 +2631,6 @@ size_t ggml_metal_op_mul_mat_id_extra_ids(const ggml_tensor * op) {
    return ggml_type_size(GGML_TYPE_I32)*ne02*ne21;
 }

-size_t ggml_metal_op_mul_mat_id_extra_amax(const ggml_tensor * op) {
-    assert(op->op == GGML_OP_MUL_MAT_ID);
-
-    GGML_UNUSED(op);
-
-    // 2 scaling factors (8 bytes) + N_MM_NPART_AMAX per-threadgroup scales for stage-1
-    return 8 + N_MM_NPART_AMAX*sizeof(float);
-}
-
 int ggml_metal_op_mul_mat_id(ggml_metal_op_t ctx, int idx) {
    ggml_tensor * op = ctx->node(idx);

@@ -2691,36 +2682,6 @@ int ggml_metal_op_mul_mat_id(ggml_metal_op_t ctx, int idx) {
        ggml_metal_buffer_id bid_ids = bid_tpe;
        bid_ids.offs += ggml_metal_op_mul_mat_id_extra_tpe(op);

-        ggml_metal_buffer_id bid_amax = bid_ids;
-        bid_amax.offs += ggml_metal_op_mul_mat_id_extra_ids(op);
-
-        // src1 rescale factors, computed before the matmul
-        // ref: https://github.com/ggml-org/llama.cpp/pull/26223
-        {
-            ggml_metal_kargs_mul_mm_id_amax args = {
-                /*.ne00 =*/ ne10,
-                /*.ne01 =*/ ne11,
-                /*.ne02 =*/ ne12,
-                /*.nb01 =*/ nb11,
-                /*.nb02 =*/ nb12,
-            };
-
-            auto pipeline = ggml_metal_library_get_pipeline_mul_mm_id_amax_part(lib);
-
-            const size_t smem = pipeline.smem;
-
-            GGML_ASSERT(smem <= props_dev->max_theadgroup_memory_size);
-
-            ggml_metal_encoder_set_pipeline(enc, pipeline);
-            ggml_metal_encoder_set_bytes   (enc, &args, sizeof(args), 0);
-            ggml_metal_encoder_set_buffer  (enc, bid_src1, 1);
-            ggml_metal_encoder_set_buffer  (enc, bid_amax, 2);
-
-            ggml_metal_encoder_set_threadgroup_memory_size(enc, smem, 0);
-
-            ggml_metal_encoder_dispatch_threadgroups(enc, N_MM_NPART_AMAX, 1, 1, 256, 1, 1);
-        }
-
        {
            ggml_metal_kargs_mul_mm_id_map0 args = {
                ne02,
@@ -2752,18 +2713,7 @@ int ggml_metal_op_mul_mat_id(ggml_metal_op_t ctx, int idx) {
            ggml_metal_encoder_dispatch_threadgroups(enc, 1, 1, 1, ne02, 1, 1);
        }

-        ggml_metal_op_concurrency_reset(ctx);
-
-        {
-            auto pipeline = ggml_metal_library_get_pipeline_mul_mm_id_amax(lib);
-
-            ggml_metal_encoder_set_pipeline(enc, pipeline);
-            ggml_metal_encoder_set_buffer  (enc, bid_amax, 0);
-
-            ggml_metal_encoder_dispatch_threadgroups(enc, 1, 1, 1, 32, 1, 1);
-        }
-
-        // the next kernel has to wait for the amax data
+        // this barrier is always needed because the next kernel has to wait for the id maps to be computed
        ggml_metal_op_concurrency_reset(ctx);

        {
@@ -2795,7 +2745,6 @@ int ggml_metal_op_mul_mat_id(ggml_metal_op_t ctx, int idx) {
            ggml_metal_encoder_set_buffer  (enc, bid_tpe,  3);
            ggml_metal_encoder_set_buffer  (enc, bid_ids,  4);
            ggml_metal_encoder_set_buffer  (enc, bid_dst,  5);
-            ggml_metal_encoder_set_buffer  (enc, bid_amax, 6);

            const size_t smem = pipeline.smem;

@@ -2974,7 +2923,6 @@ static int ggml_metal_op_flash_attn_ext_n_kv_max_sparse(const ggml_tensor * op)
    const bool dk_dv_ok = (dk == 32  && dv == 32)  ||
                          (dk == 64  && dv == 64)  ||
                          (dk == 96  && dv == 96)  ||
-                          (dk == 96  && dv == 64)  ||
                          (dk == 128 && dv == 128) ||
                          (dk == 192 && dv == 128) ||
                          (dk == 192 && dv == 192) ||
@@ -36,7 +36,6 @@ size_t ggml_metal_op_mul_mat_id_extra_tpe(const struct ggml_tensor * op);

 // id map [n_tokens, n_expert]
 size_t ggml_metal_op_mul_mat_id_extra_ids(const struct ggml_tensor * op);
-size_t ggml_metal_op_mul_mat_id_extra_amax(const struct ggml_tensor * op);

 // return true if we should use the FA vector kernel for this op
 bool ggml_metal_op_flash_attn_ext_use_vec(const struct ggml_tensor * op);
@@ -34,9 +34,6 @@ int fa_vec_baseline_ne(int dk, int dv) {
    if (dk == 96 && dv == 96) {
        return 4;
    }
-    if (dk == 96 && dv == 64) {
-        return 4;
-    }
    if (dk == 128 && dv == 128) {
        return 1;
    }
@@ -226,7 +226,6 @@ static size_t ggml_backend_metal_buffer_type_get_alloc_size(ggml_backend_buffer_
            {
                res += ggml_metal_op_mul_mat_id_extra_tpe(tensor);
                res += ggml_metal_op_mul_mat_id_extra_ids(tensor);
-                res += ggml_metal_op_mul_mat_id_extra_amax(tensor);
            } break;
        case GGML_OP_FLASH_ATTN_EXT:
            {
@@ -930,7 +930,6 @@ template [[host_name("kernel_flash_attn_ext_f32_dk64_dv64"  )]]  kernel flash_at
 template [[host_name("kernel_flash_attn_ext_f32_dk72_dv72"  )]]  kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES_F32, float4x4,   1, dequantize_f32,  float4x4,   1, dequantize_f32,  72,  72>;
 template [[host_name("kernel_flash_attn_ext_f32_dk80_dv80"  )]]  kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES_F32, float4x4,   1, dequantize_f32,  float4x4,   1, dequantize_f32,  80,  80>;
 template [[host_name("kernel_flash_attn_ext_f32_dk96_dv96"  )]]  kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES_F32, float4x4,   1, dequantize_f32,  float4x4,   1, dequantize_f32,  96,  96>;
-template [[host_name("kernel_flash_attn_ext_f32_dk96_dv64"  )]]  kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES_F32, float4x4,   1, dequantize_f32,  float4x4,   1, dequantize_f32,  96,  64>;
 template [[host_name("kernel_flash_attn_ext_f32_dk112_dv112")]]  kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES_F32, float4x4,   1, dequantize_f32,  float4x4,   1, dequantize_f32,  112, 112>;
 template [[host_name("kernel_flash_attn_ext_f32_dk128_dv128")]]  kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES_F32, float4x4,   1, dequantize_f32,  float4x4,   1, dequantize_f32,  128, 128>;
 template [[host_name("kernel_flash_attn_ext_f32_dk192_dv192")]]  kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES_F32, float4x4,   1, dequantize_f32,  float4x4,   1, dequantize_f32,  192, 192>;
@@ -947,7 +946,6 @@ template [[host_name("kernel_flash_attn_ext_f16_dk64_dv64"  )]]  kernel flash_at
 template [[host_name("kernel_flash_attn_ext_f16_dk72_dv72"  )]]  kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES,    half4x4,    1, dequantize_f16,  half4x4,    1, dequantize_f16,  72,  72>;
 template [[host_name("kernel_flash_attn_ext_f16_dk80_dv80"  )]]  kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES,    half4x4,    1, dequantize_f16,  half4x4,    1, dequantize_f16,  80,  80>;
 template [[host_name("kernel_flash_attn_ext_f16_dk96_dv96"  )]]  kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES,    half4x4,    1, dequantize_f16,  half4x4,    1, dequantize_f16,  96,  96>;
-template [[host_name("kernel_flash_attn_ext_f16_dk96_dv64"  )]]  kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES,    half4x4,    1, dequantize_f16,  half4x4,    1, dequantize_f16,  96,  64>;
 template [[host_name("kernel_flash_attn_ext_f16_dk112_dv112")]]  kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES,    half4x4,    1, dequantize_f16,  half4x4,    1, dequantize_f16,  112, 112>;
 template [[host_name("kernel_flash_attn_ext_f16_dk128_dv128")]]  kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES,    half4x4,    1, dequantize_f16,  half4x4,    1, dequantize_f16,  128, 128>;
 template [[host_name("kernel_flash_attn_ext_f16_dk192_dv192")]]  kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES,    half4x4,    1, dequantize_f16,  half4x4,    1, dequantize_f16,  192, 192>;
@@ -965,7 +963,6 @@ template [[host_name("kernel_flash_attn_ext_bf16_dk64_dv64"  )]] kernel flash_at
 template [[host_name("kernel_flash_attn_ext_bf16_dk72_dv72"  )]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES_BF, bfloat4x4,  1, dequantize_bf16, bfloat4x4,  1, dequantize_bf16, 72,  72>;
 template [[host_name("kernel_flash_attn_ext_bf16_dk80_dv80"  )]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES_BF, bfloat4x4,  1, dequantize_bf16, bfloat4x4,  1, dequantize_bf16, 80,  80>;
 template [[host_name("kernel_flash_attn_ext_bf16_dk96_dv96"  )]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES_BF, bfloat4x4,  1, dequantize_bf16, bfloat4x4,  1, dequantize_bf16, 96,  96>;
-template [[host_name("kernel_flash_attn_ext_bf16_dk96_dv64"  )]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES_BF, bfloat4x4,  1, dequantize_bf16, bfloat4x4,  1, dequantize_bf16, 96,  64>;
 template [[host_name("kernel_flash_attn_ext_bf16_dk112_dv112")]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES_BF, bfloat4x4,  1, dequantize_bf16, bfloat4x4,  1, dequantize_bf16, 112, 112>;
 template [[host_name("kernel_flash_attn_ext_bf16_dk128_dv128")]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES_BF, bfloat4x4,  1, dequantize_bf16, bfloat4x4,  1, dequantize_bf16, 128, 128>;
 template [[host_name("kernel_flash_attn_ext_bf16_dk192_dv192")]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES_BF, bfloat4x4,  1, dequantize_bf16, bfloat4x4,  1, dequantize_bf16, 192, 192>;
@@ -983,7 +980,6 @@ template [[host_name("kernel_flash_attn_ext_q4_0_dk64_dv64"  )]] kernel flash_at
 template [[host_name("kernel_flash_attn_ext_q4_0_dk72_dv72"  )]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES,    block_q4_0, 2, dequantize_q4_0, block_q4_0, 2, dequantize_q4_0, 72,  72>;
 template [[host_name("kernel_flash_attn_ext_q4_0_dk80_dv80"  )]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES,    block_q4_0, 2, dequantize_q4_0, block_q4_0, 2, dequantize_q4_0, 80,  80>;
 template [[host_name("kernel_flash_attn_ext_q4_0_dk96_dv96"  )]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES,    block_q4_0, 2, dequantize_q4_0, block_q4_0, 2, dequantize_q4_0, 96,  96>;
-template [[host_name("kernel_flash_attn_ext_q4_0_dk96_dv64"  )]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES,    block_q4_0, 2, dequantize_q4_0, block_q4_0, 2, dequantize_q4_0, 96,  64>;
 template [[host_name("kernel_flash_attn_ext_q4_0_dk112_dv112")]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES,    block_q4_0, 2, dequantize_q4_0, block_q4_0, 2, dequantize_q4_0, 112, 112>;
 template [[host_name("kernel_flash_attn_ext_q4_0_dk128_dv128")]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES,    block_q4_0, 2, dequantize_q4_0, block_q4_0, 2, dequantize_q4_0, 128, 128>;
 template [[host_name("kernel_flash_attn_ext_q4_0_dk192_dv192")]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES,    block_q4_0, 2, dequantize_q4_0, block_q4_0, 2, dequantize_q4_0, 192, 192>;
@@ -1000,7 +996,6 @@ template [[host_name("kernel_flash_attn_ext_q4_1_dk64_dv64"  )]] kernel flash_at
 template [[host_name("kernel_flash_attn_ext_q4_1_dk72_dv72"  )]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES,    block_q4_1, 2, dequantize_q4_1, block_q4_1, 2, dequantize_q4_1, 72,  72>;
 template [[host_name("kernel_flash_attn_ext_q4_1_dk80_dv80"  )]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES,    block_q4_1, 2, dequantize_q4_1, block_q4_1, 2, dequantize_q4_1, 80,  80>;
 template [[host_name("kernel_flash_attn_ext_q4_1_dk96_dv96"  )]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES,    block_q4_1, 2, dequantize_q4_1, block_q4_1, 2, dequantize_q4_1, 96,  96>;
-template [[host_name("kernel_flash_attn_ext_q4_1_dk96_dv64"  )]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES,    block_q4_1, 2, dequantize_q4_1, block_q4_1, 2, dequantize_q4_1, 96,  64>;
 template [[host_name("kernel_flash_attn_ext_q4_1_dk112_dv112")]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES,    block_q4_1, 2, dequantize_q4_1, block_q4_1, 2, dequantize_q4_1, 112, 112>;
 template [[host_name("kernel_flash_attn_ext_q4_1_dk128_dv128")]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES,    block_q4_1, 2, dequantize_q4_1, block_q4_1, 2, dequantize_q4_1, 128, 128>;
 template [[host_name("kernel_flash_attn_ext_q4_1_dk192_dv192")]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES,    block_q4_1, 2, dequantize_q4_1, block_q4_1, 2, dequantize_q4_1, 192, 192>;
@@ -1017,7 +1012,6 @@ template [[host_name("kernel_flash_attn_ext_q5_0_dk64_dv64"  )]] kernel flash_at
 template [[host_name("kernel_flash_attn_ext_q5_0_dk72_dv72"  )]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES,    block_q5_0, 2, dequantize_q5_0, block_q5_0, 2, dequantize_q5_0, 72,  72>;
 template [[host_name("kernel_flash_attn_ext_q5_0_dk80_dv80"  )]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES,    block_q5_0, 2, dequantize_q5_0, block_q5_0, 2, dequantize_q5_0, 80,  80>;
 template [[host_name("kernel_flash_attn_ext_q5_0_dk96_dv96"  )]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES,    block_q5_0, 2, dequantize_q5_0, block_q5_0, 2, dequantize_q5_0, 96,  96>;
-template [[host_name("kernel_flash_attn_ext_q5_0_dk96_dv64"  )]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES,    block_q5_0, 2, dequantize_q5_0, block_q5_0, 2, dequantize_q5_0, 96,  64>;
 template [[host_name("kernel_flash_attn_ext_q5_0_dk112_dv112")]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES,    block_q5_0, 2, dequantize_q5_0, block_q5_0, 2, dequantize_q5_0, 112, 112>;
 template [[host_name("kernel_flash_attn_ext_q5_0_dk128_dv128")]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES,    block_q5_0, 2, dequantize_q5_0, block_q5_0, 2, dequantize_q5_0, 128, 128>;
 template [[host_name("kernel_flash_attn_ext_q5_0_dk192_dv192")]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES,    block_q5_0, 2, dequantize_q5_0, block_q5_0, 2, dequantize_q5_0, 192, 192>;
@@ -1034,7 +1028,6 @@ template [[host_name("kernel_flash_attn_ext_q5_1_dk64_dv64"  )]] kernel flash_at
 template [[host_name("kernel_flash_attn_ext_q5_1_dk72_dv72"  )]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES,    block_q5_1, 2, dequantize_q5_1, block_q5_1, 2, dequantize_q5_1, 72,  72>;
 template [[host_name("kernel_flash_attn_ext_q5_1_dk80_dv80"  )]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES,    block_q5_1, 2, dequantize_q5_1, block_q5_1, 2, dequantize_q5_1, 80,  80>;
 template [[host_name("kernel_flash_attn_ext_q5_1_dk96_dv96"  )]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES,    block_q5_1, 2, dequantize_q5_1, block_q5_1, 2, dequantize_q5_1, 96,  96>;
-template [[host_name("kernel_flash_attn_ext_q5_1_dk96_dv64"  )]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES,    block_q5_1, 2, dequantize_q5_1, block_q5_1, 2, dequantize_q5_1, 96,  64>;
 template [[host_name("kernel_flash_attn_ext_q5_1_dk112_dv112")]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES,    block_q5_1, 2, dequantize_q5_1, block_q5_1, 2, dequantize_q5_1, 112, 112>;
 template [[host_name("kernel_flash_attn_ext_q5_1_dk128_dv128")]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES,    block_q5_1, 2, dequantize_q5_1, block_q5_1, 2, dequantize_q5_1, 128, 128>;
 template [[host_name("kernel_flash_attn_ext_q5_1_dk192_dv192")]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES,    block_q5_1, 2, dequantize_q5_1, block_q5_1, 2, dequantize_q5_1, 192, 192>;
@@ -1051,7 +1044,6 @@ template [[host_name("kernel_flash_attn_ext_q8_0_dk64_dv64"  )]] kernel flash_at
 template [[host_name("kernel_flash_attn_ext_q8_0_dk72_dv72"  )]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES,    block_q8_0, 2, dequantize_q8_0, block_q8_0, 2, dequantize_q8_0, 72,  72>;
 template [[host_name("kernel_flash_attn_ext_q8_0_dk80_dv80"  )]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES,    block_q8_0, 2, dequantize_q8_0, block_q8_0, 2, dequantize_q8_0, 80,  80>;
 template [[host_name("kernel_flash_attn_ext_q8_0_dk96_dv96"  )]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES,    block_q8_0, 2, dequantize_q8_0, block_q8_0, 2, dequantize_q8_0, 96,  96>;
-template [[host_name("kernel_flash_attn_ext_q8_0_dk96_dv64"  )]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES,    block_q8_0, 2, dequantize_q8_0, block_q8_0, 2, dequantize_q8_0, 96,  64>;
 template [[host_name("kernel_flash_attn_ext_q8_0_dk112_dv112")]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES,    block_q8_0, 2, dequantize_q8_0, block_q8_0, 2, dequantize_q8_0, 112, 112>;
 template [[host_name("kernel_flash_attn_ext_q8_0_dk128_dv128")]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES,    block_q8_0, 2, dequantize_q8_0, block_q8_0, 2, dequantize_q8_0, 128, 128>;
 template [[host_name("kernel_flash_attn_ext_q8_0_dk192_dv192")]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES,    block_q8_0, 2, dequantize_q8_0, block_q8_0, 2, dequantize_q8_0, 192, 192>;
@@ -1913,29 +1905,6 @@ template [[host_name("kernel_flash_attn_ext_vec_q8_0_dk96_dv96")]]   kernel flas
 template [[host_name("kernel_flash_attn_ext_vec_q8_0_dk96_dv96_q2_ne4")]]   kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec<FA_TYPES,     block_q8_0, 8, dequantize_q8_0_t4, block_q8_0,  8, dequantize_q8_0_t4, 96, 96, 4, 2>;
 template [[host_name("kernel_flash_attn_ext_vec_q8_0_dk96_dv96_q4_ne4")]]   kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec<FA_TYPES,     block_q8_0, 8, dequantize_q8_0_t4, block_q8_0,  8, dequantize_q8_0_t4, 96, 96, 4, 4>;

-template [[host_name("kernel_flash_attn_ext_vec_f32_dk96_dv64")]]    kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec<FA_TYPES_F32, float4,     1, dequantize_f32_t4,  float4,      1, dequantize_f32_t4,  96, 64, 4>;
-template [[host_name("kernel_flash_attn_ext_vec_f16_dk96_dv64")]]    kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec<FA_TYPES,     half4,      1, dequantize_f16_t4,  half4,       1, dequantize_f16_t4,  96, 64, 4>;
-template [[host_name("kernel_flash_attn_ext_vec_f16_dk96_dv64_q2_ne4")]]   kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec<FA_TYPES,     half4,      1, dequantize_f16_t4,  half4,       1, dequantize_f16_t4,  96, 64, 4, 2>;
-template [[host_name("kernel_flash_attn_ext_vec_f16_dk96_dv64_q4_ne4")]]   kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec<FA_TYPES,     half4,      1, dequantize_f16_t4,  half4,       1, dequantize_f16_t4,  96, 64, 4, 4>;
-#if defined(GGML_METAL_HAS_BF16)
-template [[host_name("kernel_flash_attn_ext_vec_bf16_dk96_dv64")]]   kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec<FA_TYPES,     bfloat4,    1, dequantize_bf16_t4, bfloat4,     1, dequantize_bf16_t4, 96, 64, 4>;
-#endif
-template [[host_name("kernel_flash_attn_ext_vec_q4_0_dk96_dv64")]]   kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec<FA_TYPES,     block_q4_0, 8, dequantize_q4_0_t4, block_q4_0,  8, dequantize_q4_0_t4, 96, 64, 4>;
-template [[host_name("kernel_flash_attn_ext_vec_q4_0_dk96_dv64_q2_ne4")]]   kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec<FA_TYPES,     block_q4_0, 8, dequantize_q4_0_t4, block_q4_0,  8, dequantize_q4_0_t4, 96, 64, 4, 2>;
-template [[host_name("kernel_flash_attn_ext_vec_q4_0_dk96_dv64_q4_ne4")]]   kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec<FA_TYPES,     block_q4_0, 8, dequantize_q4_0_t4, block_q4_0,  8, dequantize_q4_0_t4, 96, 64, 4, 4>;
-template [[host_name("kernel_flash_attn_ext_vec_q4_1_dk96_dv64")]]   kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec<FA_TYPES,     block_q4_1, 8, dequantize_q4_1_t4, block_q4_1,  8, dequantize_q4_1_t4, 96, 64, 4>;
-template [[host_name("kernel_flash_attn_ext_vec_q4_1_dk96_dv64_q2_ne4")]]   kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec<FA_TYPES,     block_q4_1, 8, dequantize_q4_1_t4, block_q4_1,  8, dequantize_q4_1_t4, 96, 64, 4, 2>;
-template [[host_name("kernel_flash_attn_ext_vec_q4_1_dk96_dv64_q4_ne4")]]   kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec<FA_TYPES,     block_q4_1, 8, dequantize_q4_1_t4, block_q4_1,  8, dequantize_q4_1_t4, 96, 64, 4, 4>;
-template [[host_name("kernel_flash_attn_ext_vec_q5_0_dk96_dv64")]]   kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec<FA_TYPES,     block_q5_0, 8, dequantize_q5_0_t4, block_q5_0,  8, dequantize_q5_0_t4, 96, 64, 4>;
-template [[host_name("kernel_flash_attn_ext_vec_q5_0_dk96_dv64_q2_ne4")]]   kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec<FA_TYPES,     block_q5_0, 8, dequantize_q5_0_t4, block_q5_0,  8, dequantize_q5_0_t4, 96, 64, 4, 2>;
-template [[host_name("kernel_flash_attn_ext_vec_q5_0_dk96_dv64_q4_ne4")]]   kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec<FA_TYPES,     block_q5_0, 8, dequantize_q5_0_t4, block_q5_0,  8, dequantize_q5_0_t4, 96, 64, 4, 4>;
-template [[host_name("kernel_flash_attn_ext_vec_q5_1_dk96_dv64")]]   kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec<FA_TYPES,     block_q5_1, 8, dequantize_q5_1_t4, block_q5_1,  8, dequantize_q5_1_t4, 96, 64, 4>;
-template [[host_name("kernel_flash_attn_ext_vec_q5_1_dk96_dv64_q2_ne4")]]   kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec<FA_TYPES,     block_q5_1, 8, dequantize_q5_1_t4, block_q5_1,  8, dequantize_q5_1_t4, 96, 64, 4, 2>;
-template [[host_name("kernel_flash_attn_ext_vec_q5_1_dk96_dv64_q4_ne4")]]   kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec<FA_TYPES,     block_q5_1, 8, dequantize_q5_1_t4, block_q5_1,  8, dequantize_q5_1_t4, 96, 64, 4, 4>;
-template [[host_name("kernel_flash_attn_ext_vec_q8_0_dk96_dv64")]]   kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec<FA_TYPES,     block_q8_0, 8, dequantize_q8_0_t4, block_q8_0,  8, dequantize_q8_0_t4, 96, 64, 4>;
-template [[host_name("kernel_flash_attn_ext_vec_q8_0_dk96_dv64_q2_ne4")]]   kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec<FA_TYPES,     block_q8_0, 8, dequantize_q8_0_t4, block_q8_0,  8, dequantize_q8_0_t4, 96, 64, 4, 2>;
-template [[host_name("kernel_flash_attn_ext_vec_q8_0_dk96_dv64_q4_ne4")]]   kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec<FA_TYPES,     block_q8_0, 8, dequantize_q8_0_t4, block_q8_0,  8, dequantize_q8_0_t4, 96, 64, 4, 4>;
-
 template [[host_name("kernel_flash_attn_ext_vec_f32_dk128_dv128")]]  kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec<FA_TYPES_F32, float4,     1, dequantize_f32_t4,  float4,      1, dequantize_f32_t4,  128, 128, 1>;
 template [[host_name("kernel_flash_attn_ext_vec_f16_dk128_dv128")]]  kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec<FA_TYPES,     half4,      1, dequantize_f16_t4,  half4,       1, dequantize_f16_t4,  128, 128, 1>;
 template [[host_name("kernel_flash_attn_ext_vec_f16_dk128_dv128_q1_ne2")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec<FA_TYPES,     half4,      1, dequantize_f16_t4,  half4,       1, dequantize_f16_t4,  128, 128, 2, 1>;
@@ -413,85 +413,6 @@ kernel void kernel_mul_mm_id_map0(
    tpe_u32[ide] = n_all;
 }

-kernel void kernel_mul_mm_id_amax_part_f32(
-        constant ggml_metal_kargs_mul_mm_id_amax & args,
-        device   const char * src1,
-        device         char * dst,
-        threadgroup    char * shmem [[threadgroup(0)]],
-        uint  tgpig[[threadgroup_position_in_grid]],
-        ushort tiitg[[thread_index_in_threadgroup]],
-        ushort tiisg[[thread_index_in_simdgroup]],
-        ushort sgitg[[simdgroup_index_in_threadgroup]],
-        ushort   ntg[[threads_per_threadgroup]]) {
-    const int nrow = args.ne01*args.ne02;
-
-    float lmax = 0.0f;
-
-    for (int ir = tgpig; ir < nrow; ir += N_MM_NPART_AMAX) {
-        const int i01 = ir % args.ne01;
-        const int i02 = ir / args.ne01;
-
-        device const float * row = (device const float *) (src1 + i02*args.nb02 + i01*args.nb01);
-
-        for (int i00 = tiitg; i00 < args.ne00; i00 += ntg) {
-            lmax = max(lmax, fabs(row[i00]));
-        }
-    }
-
-    float amax = simd_max(lmax);
-
-    threadgroup float * shared_amax = (threadgroup float *) shmem;
-
-    if (ntg > N_SIMDWIDTH) {
-        if (sgitg == 0) {
-            shared_amax[tiisg] = 0.0f;
-        }
-        threadgroup_barrier(mem_flags::mem_threadgroup);
-
-        if (tiisg == 0) {
-            shared_amax[sgitg] = amax;
-        }
-        threadgroup_barrier(mem_flags::mem_threadgroup);
-
-        amax = shared_amax[tiisg];
-        amax = simd_max(amax);
-    }
-
-    if (tiitg == 0) {
-        ((device float *) (dst + 8))[tgpig] = amax;
-    }
-}
-
-kernel void kernel_mul_mm_id_amax_f32(
-        device char * dst,
-        ushort tiitg[[thread_index_in_threadgroup]]) {
-    device const float * part = (device const float *) (dst + 8);
-
-    float amax = 0.0f;
-
-    for (int i = tiitg; i < N_MM_NPART_AMAX; i += N_SIMDWIDTH) {
-        amax = max(amax, part[i]);
-    }
-
-    amax = simd_max(amax);
-
-    if (tiitg == 0) {
-        // leave a comfortable margin below the f16 max of 65504
-        float scale = 1.0f;
-
-        // isfinite: src1 already inf/nan is not ours to fix - keep the
-        // scale at 1.0 instead of turning it into a different failure
-        if (isfinite(amax) && amax > 32768.0f) {
-            scale = exp2(ceil(log2(amax)) - 15.0f);
-        }
-
-        device float * d = (device float *) dst;
-
-        d[0] = 1.0f/scale; // exact: scale is a power of two
-        d[1] = scale;
-    }
-}
-
 typedef decltype(kernel_mul_mm_id_map0<1>) kernel_mul_mm_id_map0_t;

 template [[host_name("kernel_mul_mm_id_map0_ne20_1" )]] kernel kernel_mul_mm_id_map0_t kernel_mul_mm_id_map0<1>;
@@ -512,7 +433,6 @@ kernel void kernel_mul_mm_id(
        device const char * htpe,
        device const char * hids,
        device       char * dst,
-        device const char * amax,
        threadgroup  char * shmem [[threadgroup(0)]],
        uint3  tgpig[[threadgroup_position_in_grid]],
        ushort tiitg[[thread_index_in_threadgroup]],
@@ -583,10 +503,6 @@ kernel void kernel_mul_mm_id(

    const short lb1 = (short) tiitg/NL1; // 0 .. NR1-1, this thread's row of the B tile

-    // power-of-two rescaling
-    const float s1_inv   = ((device const float *) amax)[0];
-    const float s1_scale = ((device const float *) amax)[1];
-
 #ifndef GGML_METAL_HAS_TENSOR
    S0_8x8 ma[4];
    S1_8x8 mb[2];
@@ -670,7 +586,7 @@ kernel void kernel_mul_mm_id(

                const short ib = 4*sx + sy;

-                *(sb + 64*ib + 8*ly + lx) = loop_k + iy + i < args.ne00 ? (S1) (*((device T1 *) y + i) * (T1) s1_inv) : 0;
+                *(sb + 64*ib + 8*ly + lx) = loop_k + iy + i < args.ne00 ? (S1) *((device T1 *) y + i) : 0;
            }
        } else {
            const short sx = (tiitg%NL1);
@@ -683,7 +599,7 @@ kernel void kernel_mul_mm_id(

            const short ib = 4*sx + sy;

-            *(threadgroup S1_2x4 *)(sb + 64*ib + 8*ly) = (S1_2x4)((*((device T1_2x4 *) y)) * (T1) s1_inv);
+            *(threadgroup S1_2x4 *)(sb + 64*ib + 8*ly) = (S1_2x4)(*((device T1_2x4 *) y));
        }
 #else
        // load data and store to threadgroup memory
@@ -731,7 +647,7 @@ kernel void kernel_mul_mm_id(
                //const short lx = (tiitg/NL1)%8;
                //const short ly = i;

-                *(sb + NK*(8*sy + ly) + 8*sx + lx) = loop_k + iy + i < args.ne00 ? (S1) (*((device T1 *) y + i) * (T1) s1_inv) : 0;
+                *(sb + NK*(8*sy + ly) + 8*sx + lx) = loop_k + iy + i < args.ne00 ? (S1) *((device T1 *) y + i) : 0;
            }
        } else {
            const short sx = (tiitg%NL1);
@@ -742,7 +658,7 @@ kernel void kernel_mul_mm_id(
            //const short lx = (tiitg/NL1)%8;
            //const short ly = i;

-            *(threadgroup S1_2x4 *)(sb + NK*(8*sy + ly) + 8*sx) = (S1_2x4)((*((device T1_2x4 *) y)) * (T1) s1_inv);
+            *(threadgroup S1_2x4 *)(sb + NK*(8*sy + ly) + 8*sx) = (S1_2x4)(*((device T1_2x4 *) y));
        }
 #endif

@@ -833,12 +749,12 @@ kernel void kernel_mul_mm_id(

        int i = tiisg;
        for (; i < nr0/4; i += 32) {
-            *(D4 + i) = *(C4 + i) * s1_scale;
+            *(D4 + i) = *(C4 + i);
        }

        i = (4*(nr0/4)) + tiisg;
        for (; i < nr0; i += 32) {
-            *(D + i) = *(C + i) * s1_scale;
+            *(D + i) = *(C + i);
        }
    }
 }
@@ -982,7 +982,6 @@ struct ggml_backend_opencl_context {
    // [size_idx][kda][tgpp] where size_idx: 0=S_V=16, 1=32, 2=64, 3=128; kda: 0 or 1.
    // tgpp 0 = TG variant (COLS_PER_LANE_GROUP=1), tgpp 1 = prefill variant (COLS_PER_LANE_GROUP=4).
    cl_kernel kernel_gated_delta_net_f32[4][2][2] = {};
-    cl_kernel kernel_ssm_scan_f32 = nullptr;
    cl_kernel kernel_ssm_scan_f32_mamba2_d128 = nullptr;
    cl_kernel kernel_ssm_scan_f32_mamba2_d256 = nullptr;

@@ -1440,7 +1439,6 @@ static void load_cl_kernels_argsort(ggml_backend_opencl_context *backend_ctx) {

 static bool use_adreno_bin_kernels(ggml_backend_opencl_context * backend_ctx) {
 #ifndef GGML_OPENCL_USE_ADRENO_BIN_KERNELS
-    GGML_UNUSED(backend_ctx);
    return false;
 #else
    if (backend_ctx->gpu_family != GPU_FAMILY::ADRENO) {
@@ -3459,7 +3457,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) {
        GGML_LOG_CONT(".");
    }

-    // ssm_scan
+    // ssm_scan (Mamba-2 fused per-token recurrent step; d_state in {128, 256})
    {
 #ifdef GGML_OPENCL_EMBED_KERNELS
        const std::string kernel_src {
@@ -3471,34 +3469,8 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) {
        cl_program prog =
            build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts);

-        CL_CHECK((backend_ctx->kernel_ssm_scan_f32 = clCreateKernel(prog, "kernel_ssm_scan_f32", &err), err));
        CL_CHECK((backend_ctx->kernel_ssm_scan_f32_mamba2_d128 = clCreateKernel(prog, "kernel_ssm_scan_f32_mamba2_d128", &err), err));
        CL_CHECK((backend_ctx->kernel_ssm_scan_f32_mamba2_d256 = clCreateKernel(prog, "kernel_ssm_scan_f32_mamba2_d256", &err), err));
-
-        cl_kernel * kernels[] = {
-            &backend_ctx->kernel_ssm_scan_f32_mamba2_d128,
-            &backend_ctx->kernel_ssm_scan_f32_mamba2_d256
-        };
-
-        // specialized kernels use subgroups and assume subgroup size is 64,
-        // if device does not support subgroups or subgroup size is not 64,
-        // release these kernels
-        for (int i = 0; i < 2; ++i) {
-            size_t subgroup_size = 0;
-#if CL_TARGET_OPENCL_VERSION >= 210
-            const size_t local_work_size[] = { 64, 1 };
-            const cl_int subgroup_err = clGetKernelSubGroupInfo(*kernels[i], backend_ctx->device, CL_KERNEL_MAX_SUB_GROUP_SIZE_FOR_NDRANGE,
-                    sizeof(local_work_size), local_work_size, sizeof(subgroup_size), &subgroup_size, nullptr);
-            if (subgroup_err != CL_SUCCESS) {
-                subgroup_size = 0;
-            }
-#endif
-            // The specialized kernels reduce over one 64-lane subgroup.
-            if (subgroup_size != 64) {
-                CL_CHECK(clReleaseKernel(*kernels[i]));
-                *kernels[i] = nullptr;
-            }
-        }
        CL_CHECK(clReleaseProgram(prog));
        GGML_LOG_CONT(".");
    }
@@ -6310,8 +6282,6 @@ static void ggml_opencl_print_backend_info(ggml_backend_opencl_device_context *

    auto * backend_ctx = dev_ctx->backend_ctx;

-    GGML_LOG_INFO("ggml_opencl: OpenCL device: %s\n",
-        backend_ctx->device_name.c_str());
    GGML_LOG_INFO("ggml_opencl: OpenCL driver: %s\n",
        backend_ctx->driver_version.c_str());
    GGML_LOG_INFO("ggml_opencl: vector subgroup broadcast support: %s\n",
@@ -6328,11 +6298,11 @@ static void ggml_opencl_print_backend_info(ggml_backend_opencl_device_context *
        backend_ctx->global_mem_size/1024/1024);
    GGML_LOG_INFO("ggml_opencl: max mem alloc size: %zu MB\n",
        backend_ctx->max_alloc_size/1024/1024);
-    GGML_LOG_INFO("ggml_opencl: device max image buffer size (pixels): %zu\n",
+    GGML_LOG_INFO("ggml_opencl: device max image buffer size (pixels): %lu\n",
        backend_ctx->image_max_buffer_size);
-    GGML_LOG_INFO("ggml_opencl: device max image2d size: %zu x %zu\n",
+    GGML_LOG_INFO("ggml_opencl: device max image2d size: %lu x %lu\n",
        backend_ctx->image2d_max_width, backend_ctx->image2d_max_height);
-    GGML_LOG_INFO("ggml_opencl: device max workgroup size: %zu\n",
+    GGML_LOG_INFO("ggml_opencl: device max workgroup size: %lu\n",
        backend_ctx->max_workgroup_size);
    GGML_LOG_INFO("ggml_opencl: SVM coarse grain buffer support: %s\n",
        backend_ctx->svm_caps & CL_DEVICE_SVM_COARSE_GRAIN_BUFFER ? "true" : "false");
@@ -7630,7 +7600,7 @@ static void ggml_cl_moe_bias_glu_fused(ggml_backend_t backend, ggml_tensor * gat
    size_t global_work_size[] = { (size_t)glu->ne[1]*nth, (size_t)glu->ne[2], 1 };
    size_t local_work_size[]  = { (size_t)nth, 1, 1 };

-    backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, glu);
+    backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, (ggml_tensor *)glu);
 }

 // Fusion B: the MoE down-projection bias add feeding the combine.
@@ -7774,7 +7744,7 @@ static void ggml_cl_moe_bias_combine_fused(ggml_backend_t backend, const ggml_te

    size_t lws[2] = { 64, 1 };
    size_t gws[2] = { (size_t)(((n_embd4 + 63) / 64) * 64), (size_t)nt };
-    backend_ctx->enqueue_ndrange_kernel(kernel, 2, gws, lws, dst);
+    backend_ctx->enqueue_ndrange_kernel(kernel, 2, gws, lws, (ggml_tensor *)dst);
 }


@@ -8117,6 +8087,7 @@ static void ggml_cl_mul_mat_q4_k_glu_fused(ggml_backend_t backend, ggml_tensor *
    GGML_UNUSED(gate_tensor);
    GGML_UNUSED(up_tensor);
    GGML_UNUSED(glu_tensor);
+    GGML_ABORT("q4_K GLU fusion requires GGML_OPENCL_USE_ADRENO_KERNELS");
 #endif
 }

@@ -8331,20 +8302,9 @@ inline bool use_adreno_kernels(const ggml_backend_opencl_context *backend_ctx, c
    bool threashold_ok = tensor->ne[0] >= threshold_ne0 && tensor->ne[1] >= threshold_ne1 &&
            tensor->ne[2] == 1 && tensor->ne[3] == 1;

-    // The noshuffle layout packs 2 rows per 32-bit texel and the GEMV reads it at an
-    // ne1/2 texel stride with an exact-cover dispatch, so it is only addressable when
-    // ne1 is a multiple of 64; an unaligned ne1 truncates the stride and the weight is
-    // read misaligned. That is a property of the layout, not of one quant -- q4_K, q5_K
-    // and q8_0 read the same packing as q6_K. The bound is 64, not 128: a q8_0 attention
-    // weight of ne1 = 2880 is a multiple of 64 but not 128 and is correct.
-    switch (tensor->type) {
-        case GGML_TYPE_Q4_K:
-        case GGML_TYPE_Q5_K:
-        case GGML_TYPE_Q6_K:
-        case GGML_TYPE_Q8_0:
-            return threashold_ok && tensor->ne[1] % 64 == 0;
-        default:
-            break;
+    // q6_K adreno kernels requires ne1 is multiple of 128
+    if (tensor->type == GGML_TYPE_Q6_K) {
+        return threashold_ok && tensor->ne[1] % 128 == 0;
    }
    return threashold_ok;
 }
@@ -8763,16 +8723,22 @@ static bool ggml_opencl_supports_op(ggml_backend_dev_t dev, const struct ggml_te
        case GGML_OP_SSM_CONV:
            return (op->src[0]->type == GGML_TYPE_F32 && op->src[1]->type == GGML_TYPE_F32 && op->type == GGML_TYPE_F32);
        case GGML_OP_SSM_SCAN: {
-                if (op->type != GGML_TYPE_F32 || op->src[0]->type != GGML_TYPE_F32 ||
-                    op->src[1]->type != GGML_TYPE_F32 || op->src[2]->type != GGML_TYPE_F32 ||
-                    op->src[3]->type != GGML_TYPE_F32 || op->src[4]->type != GGML_TYPE_F32 ||
-                    op->src[5]->type != GGML_TYPE_F32 || op->src[6]->type != GGML_TYPE_I32) {
+            // Mamba-2 fused per-token scan. Requires src3->ne[0] == 1 (scalar
+            // A per head); d_state in {128, 256}; all sources f32. Falls back
+            // to CPU otherwise (incl. Mamba-1 element-wise A).
+            for (int i = 0; i < 6; ++i) {
+                if (op->src[i]->type != GGML_TYPE_F32) {
                    return false;
                }
-
-                const int64_t d_state = op->src[0]->ne[0];
-                return d_state >= 1 && d_state <= 256 && (d_state & (d_state - 1)) == 0;
            }
+            if (op->type != GGML_TYPE_F32) {
+                return false;
+            }
+            const int K = ggml_get_op_params_i32(op, 0);
+            const int d_state = (int) op->src[0]->ne[0];
+            const bool is_mamba2 = (op->src[3]->ne[0] == 1);
+            return is_mamba2 && (d_state == 128 || d_state == 256) && (K == 1);
+        }
        case GGML_OP_GATED_DELTA_NET:
            {
                // Match the Vulkan backend: only F32 -> F32, S_v in {16, 32, 64, 128}.
@@ -11338,6 +11304,7 @@ static void ggml_backend_opencl_buffer_get_tensor(ggml_backend_buffer_t buffer,

 #ifdef GGML_OPENCL_USE_ADRENO_KERNELS
        if (use_adreno_moe_kernels(backend_ctx, tensor)) {
+            cl_int err;
            cl_kernel kernel = backend_ctx->kernel_restore_block_q4_0_trans4_ns;

            cl_mem data_device = ggml_cl_create_temp_download_buffer(context, queue, ggml_nbytes(tensor), tensor->name);
@@ -11536,6 +11503,7 @@ static void ggml_backend_opencl_buffer_get_tensor(ggml_backend_buffer_t buffer,

 #ifdef GGML_OPENCL_USE_ADRENO_KERNELS
        if (use_adreno_moe_kernels(backend_ctx, tensor)) {
+            cl_int err;
            // TODO: use ggml_cl_buffer to manage this temporary buffer
            cl_mem data_device = ggml_cl_create_temp_download_buffer(context, queue, ggml_nbytes(tensor), tensor->name);
            GGML_ASSERT(data_device != NULL && "get_tensor: temp download buffer alloc failed");
@@ -11638,6 +11606,7 @@ static void ggml_backend_opencl_buffer_get_tensor(ggml_backend_buffer_t buffer,

 #ifdef GGML_OPENCL_USE_ADRENO_KERNELS
        if (use_adreno_moe_kernels(backend_ctx, tensor)) {
+            cl_int err;
            // TODO: use ggml_cl_buffer to manage this temporary buffer
            cl_mem data_device = ggml_cl_create_temp_download_buffer(context, queue, ggml_nbytes(tensor), tensor->name);
            GGML_ASSERT(data_device != NULL && "get_tensor: temp download buffer alloc failed");
@@ -14063,109 +14032,81 @@ static void ggml_cl_mean(ggml_backend_t backend, const ggml_tensor * src0, const
 }

 static void ggml_cl_ssm_scan(ggml_backend_t backend, ggml_tensor * dst) {
-    GGML_ASSERT(dst);
-    GGML_ASSERT(dst->extra);
-    GGML_ASSERT(dst->src[0]);
-    GGML_ASSERT(dst->src[0]->extra);
-    GGML_ASSERT(dst->src[1]);
-    GGML_ASSERT(dst->src[1]->extra);
-    GGML_ASSERT(dst->src[2]);
-    GGML_ASSERT(dst->src[2]->extra);
-    GGML_ASSERT(dst->src[3]);
-    GGML_ASSERT(dst->src[3]->extra);
-    GGML_ASSERT(dst->src[4]);
-    GGML_ASSERT(dst->src[4]->extra);
-    GGML_ASSERT(dst->src[5]);
-    GGML_ASSERT(dst->src[5]->extra);
-    GGML_ASSERT(dst->src[6]);
-    GGML_ASSERT(dst->src[6]->extra);
+    const ggml_tensor * src0 = dst->src[0]; // s
+    const ggml_tensor * src1 = dst->src[1]; // x
+    const ggml_tensor * src2 = dst->src[2]; // dt
+    const ggml_tensor * src3 = dst->src[3]; // A
+    const ggml_tensor * src4 = dst->src[4]; // B
+    const ggml_tensor * src5 = dst->src[5]; // C
+    const ggml_tensor * src6 = dst->src[6]; // ids
+
+    GGML_ASSERT(src0 && src1 && src2 && src3 && src4 && src5 && src6 && dst);

    ggml_backend_opencl_context * backend_ctx = (ggml_backend_opencl_context *) backend->context;

-    ggml_tensor_extra_cl * extra0 = (ggml_tensor_extra_cl *) dst->src[0]->extra;
-    ggml_tensor_extra_cl * extra1 = (ggml_tensor_extra_cl *) dst->src[1]->extra;
-    ggml_tensor_extra_cl * extra2 = (ggml_tensor_extra_cl *) dst->src[2]->extra;
-    ggml_tensor_extra_cl * extra3 = (ggml_tensor_extra_cl *) dst->src[3]->extra;
-    ggml_tensor_extra_cl * extra4 = (ggml_tensor_extra_cl *) dst->src[4]->extra;
-    ggml_tensor_extra_cl * extra5 = (ggml_tensor_extra_cl *) dst->src[5]->extra;
-    ggml_tensor_extra_cl * extra6 = (ggml_tensor_extra_cl *) dst->src[6]->extra;
-    ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *) dst->extra;
+    ggml_tensor_extra_cl * e0 = (ggml_tensor_extra_cl *) src0->extra;
+    ggml_tensor_extra_cl * e1 = (ggml_tensor_extra_cl *) src1->extra;
+    ggml_tensor_extra_cl * e2 = (ggml_tensor_extra_cl *) src2->extra;
+    ggml_tensor_extra_cl * e3 = (ggml_tensor_extra_cl *) src3->extra;
+    ggml_tensor_extra_cl * e4 = (ggml_tensor_extra_cl *) src4->extra;
+    ggml_tensor_extra_cl * e5 = (ggml_tensor_extra_cl *) src5->extra;
+    ggml_tensor_extra_cl * e6 = (ggml_tensor_extra_cl *) src6->extra;
+    ggml_tensor_extra_cl * ed = (ggml_tensor_extra_cl *) dst->extra;

-    const cl_ulong offset0 = extra0->offset + dst->src[0]->view_offs;
-    const cl_ulong offset1 = extra1->offset + dst->src[1]->view_offs;
-    const cl_ulong offset2 = extra2->offset + dst->src[2]->view_offs;
-    const cl_ulong offset3 = extra3->offset + dst->src[3]->view_offs;
-    const cl_ulong offset4 = extra4->offset + dst->src[4]->view_offs;
-    const cl_ulong offset5 = extra5->offset + dst->src[5]->view_offs;
-    const cl_ulong offset6 = extra6->offset + dst->src[6]->view_offs;
-    const cl_ulong offsetd = extrad->offset + dst->view_offs;
+    cl_ulong o0 = e0->offset + src0->view_offs;
+    cl_ulong o1 = e1->offset + src1->view_offs;
+    cl_ulong o2 = e2->offset + src2->view_offs;
+    cl_ulong o3 = e3->offset + src3->view_offs;
+    cl_ulong o4 = e4->offset + src4->view_offs;
+    cl_ulong o5 = e5->offset + src5->view_offs;
+    cl_ulong o6 = e6->offset + src6->view_offs;
+    cl_ulong od = ed->offset + dst->view_offs;

-    const ggml_tensor * s   = dst->src[0];
-    const ggml_tensor * x   = dst->src[1];
-    const ggml_tensor * dt  = dst->src[2];
-    const ggml_tensor * A   = dst->src[3];
-    const ggml_tensor * B   = dst->src[4];
-    const ggml_tensor * C   = dst->src[5];
+    const int d_state  = (int) src0->ne[0];
+    const int head_dim = (int) src0->ne[1];
+    const int n_head   = (int) src1->ne[1];
+    const int n_group  = (int) src4->ne[1];
+    const int n_tokens = (int) src1->ne[2];
+    const int n_seqs   = (int) src1->ne[3];

-    const cl_ulong s_nb1  = s->nb[1];
-    const cl_ulong s_nb2  = s->nb[2];
-    const cl_ulong s_nb3  = s->nb[3];
-    const cl_ulong x_nb1  = x->nb[1];
-    const cl_ulong x_nb2  = x->nb[2];
-    const cl_ulong x_nb3  = x->nb[3];
-    const cl_ulong dt_nb1 = dt->nb[1];
-    const cl_ulong dt_nb2 = dt->nb[2];
-    const cl_ulong A_nb1  = A->nb[1];
-    const cl_ulong B_nb1  = B->nb[1];
-    const cl_ulong B_nb2  = B->nb[2];
-    const cl_ulong B_nb3  = B->nb[3];
-    const cl_ulong C_nb1  = C->nb[1];
-    const cl_ulong C_nb2  = C->nb[2];
-    const cl_ulong C_nb3  = C->nb[3];
+    // Mirror CPU ref: s_off = ggml_nelements(src1) * sizeof(float)
+    const cl_ulong s_off_bytes = (cl_ulong) ggml_nelements(src1) * sizeof(float);

-    const cl_uint A_ne0     = A->ne[0];
-    const cl_uint d_state   = s->ne[0];
-    const cl_int  head_dim  = x->ne[0];
-    const cl_int  n_head    = x->ne[1];
-    const cl_int  n_group   = B->ne[1];
-    const cl_int  n_tokens  = x->ne[2];
-    const cl_uint n_seqs    = x->ne[3];
-    const cl_uint K         = ggml_get_op_params_i32(dst, 0);
-    const cl_ulong s_off_bytes = (cl_ulong) ggml_nelements(x) * sizeof(float);
+    cl_kernel kernel = (d_state == 128)
+        ? backend_ctx->kernel_ssm_scan_f32_mamba2_d128
+        : backend_ctx->kernel_ssm_scan_f32_mamba2_d256;
+    GGML_ASSERT(kernel != nullptr);

-    cl_kernel kernel = backend_ctx->kernel_ssm_scan_f32;
-    size_t nth = d_state;
-    if (A_ne0 == 1 && K == 1) {
-        cl_kernel kernel_mamba2 = nullptr;
-        if (d_state == 128) {
-            kernel_mamba2 = backend_ctx->kernel_ssm_scan_f32_mamba2_d128;
-        } else if (d_state == 256) {
-            kernel_mamba2 = backend_ctx->kernel_ssm_scan_f32_mamba2_d256;
-        }
-        if (kernel_mamba2 != nullptr) {
-            kernel = kernel_mamba2;
-            nth = 64;
-        }
-    }
+    cl_ulong s0_nb2 = src0->nb[2];
+    cl_ulong s0_nb3 = src0->nb[3];
+    cl_ulong x_nb2  = src1->nb[2];
+    cl_ulong x_nb3  = src1->nb[3];
+    cl_ulong dt_nb1 = src2->nb[1];
+    cl_ulong dt_nb2 = src2->nb[2];
+    cl_ulong A_nb1  = src3->nb[1];
+    cl_ulong B_nb2  = src4->nb[2];
+    cl_ulong B_nb3  = src4->nb[3];
+    cl_ulong C_nb2  = src5->nb[2];
+    cl_ulong C_nb3  = src5->nb[3];

-    CL_CHECK(clSetKernelArg(kernel,  0, sizeof(cl_mem),   &extra0->data_device));
-    CL_CHECK(clSetKernelArg(kernel,  1, sizeof(cl_ulong), &offset0));
-    CL_CHECK(clSetKernelArg(kernel,  2, sizeof(cl_mem),   &extra1->data_device));
-    CL_CHECK(clSetKernelArg(kernel,  3, sizeof(cl_ulong), &offset1));
-    CL_CHECK(clSetKernelArg(kernel,  4, sizeof(cl_mem),   &extra2->data_device));
-    CL_CHECK(clSetKernelArg(kernel,  5, sizeof(cl_ulong), &offset2));
-    CL_CHECK(clSetKernelArg(kernel,  6, sizeof(cl_mem),   &extra3->data_device));
-    CL_CHECK(clSetKernelArg(kernel,  7, sizeof(cl_ulong), &offset3));
-    CL_CHECK(clSetKernelArg(kernel,  8, sizeof(cl_mem),   &extra4->data_device));
-    CL_CHECK(clSetKernelArg(kernel,  9, sizeof(cl_ulong), &offset4));
-    CL_CHECK(clSetKernelArg(kernel, 10, sizeof(cl_mem),   &extra5->data_device));
-    CL_CHECK(clSetKernelArg(kernel, 11, sizeof(cl_ulong), &offset5));
-    CL_CHECK(clSetKernelArg(kernel, 12, sizeof(cl_mem),   &extra6->data_device));
-    CL_CHECK(clSetKernelArg(kernel, 13, sizeof(cl_ulong), &offset6));
-    CL_CHECK(clSetKernelArg(kernel, 14, sizeof(cl_mem),   &extrad->data_device));
-    CL_CHECK(clSetKernelArg(kernel, 15, sizeof(cl_ulong), &offsetd));
-    CL_CHECK(clSetKernelArg(kernel, 16, sizeof(cl_ulong), &s_nb2));
-    CL_CHECK(clSetKernelArg(kernel, 17, sizeof(cl_ulong), &s_nb3));
+    CL_CHECK(clSetKernelArg(kernel,  0, sizeof(cl_mem),   &e0->data_device));
+    CL_CHECK(clSetKernelArg(kernel,  1, sizeof(cl_ulong), &o0));
+    CL_CHECK(clSetKernelArg(kernel,  2, sizeof(cl_mem),   &e1->data_device));
+    CL_CHECK(clSetKernelArg(kernel,  3, sizeof(cl_ulong), &o1));
+    CL_CHECK(clSetKernelArg(kernel,  4, sizeof(cl_mem),   &e2->data_device));
+    CL_CHECK(clSetKernelArg(kernel,  5, sizeof(cl_ulong), &o2));
+    CL_CHECK(clSetKernelArg(kernel,  6, sizeof(cl_mem),   &e3->data_device));
+    CL_CHECK(clSetKernelArg(kernel,  7, sizeof(cl_ulong), &o3));
+    CL_CHECK(clSetKernelArg(kernel,  8, sizeof(cl_mem),   &e4->data_device));
+    CL_CHECK(clSetKernelArg(kernel,  9, sizeof(cl_ulong), &o4));
+    CL_CHECK(clSetKernelArg(kernel, 10, sizeof(cl_mem),   &e5->data_device));
+    CL_CHECK(clSetKernelArg(kernel, 11, sizeof(cl_ulong), &o5));
+    CL_CHECK(clSetKernelArg(kernel, 12, sizeof(cl_mem),   &e6->data_device));
+    CL_CHECK(clSetKernelArg(kernel, 13, sizeof(cl_ulong), &o6));
+    CL_CHECK(clSetKernelArg(kernel, 14, sizeof(cl_mem),   &ed->data_device));
+    CL_CHECK(clSetKernelArg(kernel, 15, sizeof(cl_ulong), &od));
+    CL_CHECK(clSetKernelArg(kernel, 16, sizeof(cl_ulong), &s0_nb2));
+    CL_CHECK(clSetKernelArg(kernel, 17, sizeof(cl_ulong), &s0_nb3));
    CL_CHECK(clSetKernelArg(kernel, 18, sizeof(cl_ulong), &x_nb2));
    CL_CHECK(clSetKernelArg(kernel, 19, sizeof(cl_ulong), &x_nb3));
    CL_CHECK(clSetKernelArg(kernel, 20, sizeof(cl_ulong), &dt_nb1));
@@ -14176,30 +14117,15 @@ static void ggml_cl_ssm_scan(ggml_backend_t backend, ggml_tensor * dst) {
    CL_CHECK(clSetKernelArg(kernel, 25, sizeof(cl_ulong), &C_nb2));
    CL_CHECK(clSetKernelArg(kernel, 26, sizeof(cl_ulong), &C_nb3));
    CL_CHECK(clSetKernelArg(kernel, 27, sizeof(cl_ulong), &s_off_bytes));
-    CL_CHECK(clSetKernelArg(kernel, 28, sizeof(cl_int),   &head_dim));
-    CL_CHECK(clSetKernelArg(kernel, 29, sizeof(cl_int),   &n_head));
-    CL_CHECK(clSetKernelArg(kernel, 30, sizeof(cl_int),   &n_group));
-    CL_CHECK(clSetKernelArg(kernel, 31, sizeof(cl_int),   &n_tokens));
+    CL_CHECK(clSetKernelArg(kernel, 28, sizeof(int),      &head_dim));
+    CL_CHECK(clSetKernelArg(kernel, 29, sizeof(int),      &n_head));
+    CL_CHECK(clSetKernelArg(kernel, 30, sizeof(int),      &n_group));
+    CL_CHECK(clSetKernelArg(kernel, 31, sizeof(int),      &n_tokens));

-    if (kernel == backend_ctx->kernel_ssm_scan_f32) {
-        CL_CHECK(clSetKernelArg(kernel, 32, sizeof(cl_ulong), &s_nb1));
-        CL_CHECK(clSetKernelArg(kernel, 33, sizeof(cl_ulong), &x_nb1));
-        CL_CHECK(clSetKernelArg(kernel, 34, sizeof(cl_ulong), &B_nb1));
-        CL_CHECK(clSetKernelArg(kernel, 35, sizeof(cl_ulong), &C_nb1));
-        CL_CHECK(clSetKernelArg(kernel, 36, sizeof(cl_uint),  &A_ne0));
-        CL_CHECK(clSetKernelArg(kernel, 37, sizeof(cl_uint),  &d_state));
-        CL_CHECK(clSetKernelArg(kernel, 38, sizeof(cl_uint),  &n_seqs));
-        CL_CHECK(clSetKernelArg(kernel, 39, sizeof(cl_uint),  &K));
-        CL_CHECK(clSetKernelArg(kernel, 40, d_state * sizeof(float), nullptr));
-    }
+    size_t global_work_size[] = { (size_t)n_head * head_dim * 64, (size_t)n_seqs, 1 };
+    size_t local_work_size[]  = { 64, 1, 1 };

-    size_t global_work_size[] = {
-        (size_t) head_dim * (size_t) n_head * nth,
-        (size_t) n_seqs,
-    };
-    size_t local_work_size[] = { nth, 1 };
-
-    backend_ctx->enqueue_ndrange_kernel(kernel, 2, global_work_size, local_work_size, dst);
+    backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst);
 }

 static void ggml_cl_ssm_conv(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
@@ -24565,33 +24491,10 @@ static void ggml_cl_mul_mat_id(ggml_backend_t backend, const ggml_tensor * src0,
                    CL_CHECK(clReleaseMemObject(buf_src2));

                } else { // for gemm
-                    // dp4a (int8) prefill GEMM variant
-                    static const char * q4_0_moe_dp4a_env = getenv("GGML_OPENCL_Q4_0_MOE_DP4A");
-
-                    // It turns out that the prebuilt kernel only outperforms the dp4a variant (on X2-90)
-                    // at very large routing counts, so we gate its use accordingly using moe_bin_min,
-                    // which can be overridden via the GGML_OPENCL_MOE_BIN_MIN_ROUTINGS environment variable.
-                    // The routing count is ne20 * ne21 (n_expert_used * n_tokens).
-                    static const char * moe_bin_min_env = getenv("GGML_OPENCL_MOE_BIN_MIN_ROUTINGS");
-                    const int  moe_bin_min   = moe_bin_min_env ? atoi(moe_bin_min_env) : 4096;
-
-                    // whether bin kernels are available
-                    const bool bin_available = backend_ctx->kernel_gemm_moe_q4_0_f32_ns_bin != nullptr;
-                    const bool dp4a_bin_available = backend_ctx->kernel_gemm_moe_q4_0_q8_1_dp4a_bin != nullptr;
-
-                    bool use_moe_dp4a = q4_0_moe_dp4a_env
-                        ? (atoi(q4_0_moe_dp4a_env) != 0)
-                        : (backend_ctx->adreno_gen == ADRENO_GPU_GEN::X2E
-                           && (dp4a_bin_available || !bin_available
-                               || (int)(ne20 * ne21) < moe_bin_min));
-                    // dot prod has to be available
-                    use_moe_dp4a = backend_ctx->has_integer_dot && use_moe_dp4a;
-
-                    const bool use_bin_kernel = bin_available && !use_moe_dp4a;
-
-                    kernel = use_bin_kernel
-                        ? backend_ctx->kernel_gemm_moe_q4_0_f32_ns_bin
-                        : backend_ctx->kernel_gemm_moe_q4_0_f32_ns;
+                    kernel = backend_ctx->kernel_gemm_moe_q4_0_f32_ns;
+                    if (backend_ctx->kernel_gemm_moe_q4_0_f32_ns_bin) {
+                        kernel = backend_ctx->kernel_gemm_moe_q4_0_f32_ns_bin;
+                    }

                    // Reorder router if called from test-backend-ops or when new router is generated.
                    // Otherwise reuse the reordered result from previous mul_mat_id call.
@@ -24604,6 +24507,18 @@ static void ggml_cl_mul_mat_id(ggml_backend_t backend, const ggml_tensor * src0,
                    cl_mem buf_src1_reordered = nullptr, image_src1_reordered = nullptr;
                    cl_mem buf_src2, buf_src2_emap;

+                    // dp4a (int8) prefill GEMM variant
+                    static const char * q4_0_moe_dp4a_env = getenv("GGML_OPENCL_Q4_0_MOE_DP4A");
+                    bool use_moe_dp4a = q4_0_moe_dp4a_env
+                        ? (atoi(q4_0_moe_dp4a_env) != 0)
+                        : (backend_ctx->adreno_gen == ADRENO_GPU_GEN::X2E);
+                    // dot prod has to be available
+                    use_moe_dp4a = backend_ctx->has_integer_dot && use_moe_dp4a;
+                    // bin kernel takes precedence
+                    if (backend_ctx->kernel_gemm_moe_q4_0_q8_1_dp4a_bin == nullptr) {
+                        use_moe_dp4a = use_moe_dp4a && backend_ctx->kernel_gemm_moe_q4_0_f32_ns_bin == nullptr;
+                    }
+
                    cl_buffer_region region;
                    region.origin = 0;
                    region.size = sizeof(int) * max_post_router_tile * n_tile_size;
@@ -24642,7 +24557,7 @@ static void ggml_cl_mul_mat_id(ggml_backend_t backend, const ggml_tensor * src0,
                        cl_image_desc image_desc_buf_src1;
                        image_format_buf_src1 = {CL_RGBA, CL_FLOAT};
                        image_desc_buf_src1 = {CL_MEM_OBJECT_IMAGE1D_BUFFER, static_cast<size_t>(ne00 * max_post_router_tile * n_tile_size / 4), 0,0,0,0,0,0,0, {buf_src1_reordered}};
-                        if (use_bin_kernel) {
+                        if (backend_ctx->kernel_gemm_moe_q4_0_f32_ns_bin) {
                            // bin kernel uses slightly different image format
                            image_format_buf_src1 = {CL_R, CL_FLOAT};
                            image_desc_buf_src1.image_width = static_cast<size_t>(ne00 * max_post_router_tile * n_tile_size);
@@ -20,13 +20,11 @@ kernel void kernel_moe_reorder_b(

    uint router_idx = router[post_router_idx];

-    // Padded slots need not be written at all. The MoE GEMMs accumulate per output
-    // column and scatter only the real columns, so whatever sits in a padded slot
-    // never reaches dst
-    if (router_idx == 0xFFFFFFFF) {
-        return;
+    float4 out = (float4)(0);
+    if (router_idx != 0xFFFFFFFF) {
+        ushort activation_idx = router_idx / map_ratio;
+        out = src[activation_idx * K / 4 + k_4];
    }

-    ushort activation_idx = router_idx / map_ratio;
-    dst[post_router_idx * K / 4 + k_4] = src[activation_idx * K / 4 + k_4];
+    dst[post_router_idx * K / 4 + k_4] = out;
 }
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
Alde Rojas	3a3f76f2e8	cont : rename common_schema to common_chat_schema	2026-09-12 13:23:12 -05:00
Alde Rojas	a1e2fc9e8b	cont : restore (min\|max)Length to imply string type	2026-09-12 13:23:12 -05:00
Alde Rojas	2372064fea	cont : make "{}" mean any object for json_object as well	2026-09-12 13:23:12 -05:00
Alde Rojas	cc420dafab	cont : update docs	2026-09-12 13:23:12 -05:00
Alde Rojas	55d9cf0722	cont : remove dead code	2026-09-12 13:23:12 -05:00
Alde Rojas	c8c25a0022	cont : add tests for empty $ref	2026-09-12 13:23:12 -05:00
Alde Rojas	f0fd3a81c0	cont : properly handle empty tool parameters	2026-09-12 13:23:12 -05:00
Alde Rojas	50d886a8a5	cont : fix may_be_string	2026-09-12 13:23:12 -05:00
Alde Rojas	aca246997a	refactor : rename common_schema_builder parse_* methods to build_*	2026-09-12 13:23:12 -05:00
Alde Rojas	7e0f376cf3	tests : take the schema label as const char * to satisfy gcc dangling-reference	2026-09-12 13:23:11 -05:00
Alde Rojas	bdc3a47794	tests : fix gcc dangling-reference warning in test-json-schema	2026-09-12 13:23:11 -05:00
Alde Rojas	301b0eb5f3	refactor : rename common_schema_parse to common_schema_from_json	2026-09-12 13:23:11 -05:00
Alde Rojas	5abafdeff4	cont : clean up	2026-09-12 13:23:11 -05:00
Alde Rojas	c3f104c68a	cont : clean up	2026-09-12 13:23:11 -05:00
Alde Rojas	1dbf3eef57	cont : reduce test cases	2026-09-12 13:23:11 -05:00
Alde Rojas	797b72981f	cont : move enums under common_schema and add type enum	2026-09-12 13:23:11 -05:00
Alde Rojas	e0fc22575d	cont : cleanup	2026-09-12 13:23:11 -05:00
Alde Rojas	78b4c52c06	cont : pass common_schema through the json-schema-to-grammar builder	2026-09-12 13:23:11 -05:00
Alde Rojas	a838fb4d6d	cont : simplify schema resolution	2026-09-12 13:23:11 -05:00
Alde Rojas	a4d976df93	cont : remove common_chat_tool_parameters	2026-09-12 13:23:11 -05:00
Alde Rojas	5204e2104a	cont : cleanup	2026-09-12 13:23:11 -05:00
Alde Rojas	426d070db0	common/schema : implement type/kind resolution	2026-09-12 13:23:11 -05:00
Alde Rojas	9e63e52cfc	common : use common_trie	2026-09-12 13:23:10 -05:00
Alde Rojas	4882c77330	common : refactor json-schema-to-grammar to use common_schema	2026-09-12 13:23:10 -05:00
Alde Rojas	4935133ab8	common : reduce optimizations	2026-09-12 13:23:10 -05:00
Alde Rojas	5088b6d623	common : implement a json schema optimizer	2026-09-12 13:23:10 -05:00
Alde Rojas	8ff43c23e0	common : implement common_schema types	2026-09-12 13:23:10 -05:00