* create: add server-side MLX imports and drop GGUF conversion
Support safetensors imports through the MLX create pipeline both locally and on the server, including remote upload/staging, draft layer handling, cancellation propagation, transfer limits, and shared manifest/blob writing.
Limit GGUF create to wrapping existing GGUF inputs into Ollama manifests. Remove the in-tree safetensors-to-GGUF converter, server quantization path, and converter-only dependencies so GGUF conversion and quantization stay in llama.cpp tooling.
Keep the MLX path focused on supported safetensors model creation with validation before MLX work, and expose that flow without the --experimental CLI gate.
* address comments
* add client side gguf create fast path
* address comments
* rebase adjustments
Gemma3n's MobileNetV5 projector silently produces corrupted image
embeddings on the CPU backend - no error, the model just describes the
wrong image (reproduced on llama.cpp b10760; gemma4's encoder is fine on
CPU). Without this guard the existing partial-offload, limited-VRAM, and
OOM-retry fallbacks would pick the CPU projector on exactly the small
GPUs where gemma3n lands.
* Report cached prompt tokens
Add prompt_eval_cached_count to native responses and expose equivalent cached-token fields through the OpenAI- and Anthropic-compatible APIs. Keep prompt_eval_count as the logical input total while excluding cache hits from CLI and benchmark prefill rates. Surface processed and cached prompt counts in benchmark output.
Collect cache counts from llama-server and MLX, preserve coherent metrics across two-pass structured generation.
Fixes#8008
Related to #15758
* review comments
Nothing has set Grammar since the CGO engine removal took its writers
out; it survived as a read-only pass-through on the llama-server path
and a comment claiming it is set before dispatch. Remove the field and
the dead pass-through. llama-server keeps its wire-level grammar field,
which the "json" format conversion still uses.
llama-server does not currently support WebP image payloads. Detect WebP media before forwarding, and transcode it to PNG. Pass all other media through unchanged.
Replace an existing vision integration image with a lossless WebP version so we now have coverage of JPG/PNG/WebP formats.
Fixes#17753
* glimmer: implement the Muse Glimmer model
MLX model (language + vision encoder) with DFlash draft wiring, llama-server DFlash support and rope-interleave fix, renderer and parser, tokenizer fixes, and the import quantization policy.
* mlxrunner: report committed prefill chunks after the sweep and eval
The drafter's flush evaluates its report, and an eval that runs while the chunk's construction handles are still live cannot free any intermediate buffer. On media chunks that retention keeps the whole vision tower resident and grinds the Metal allocator at its limit until the request dies. Pin the report's inputs across the sweep, report after the chunk materializes, and release media items after the report so a drafter can still capture the rows its deferred flush embeds.
* ci: retry CUDA pre-release download
Remove the x/imagegen tree (MLX image generation engine, Flux2/zimage
models, cache, C bindings) and all imagegen integration points:
- server: drop imagegen routes, scheduling, and generate handling
- api/cmd/docs: remove image generation API surface and docs
- middleware/openai: remove image endpoint support
- integration: remove imagegen test suites
- x/create: adopt the rewritten create pipeline from main; drop
imagegen create path (CreateImageGenModel, IsTensorModelDir,
model_index.json detection, Flux2KleinPipeline vision hack)
- retain x/imagegen/manifest (Ollama-store safetensors manifest
loader), still used by x/mlxrunner and x/create/client
- fix Windows MLX dl.dll install, MLX CMake version path, and the
show command after removing safetensors models
* llm: allow iGPU mmproj offload with fit padding
llama.cpp's fit pass sizes text-model placement before the multimodal projector is loaded. Ollama had been avoiding that risk on non-Metal iGPUs by disabling projector offload entirely, which forces CLIP onto CPU on GB10 and Strix Halo even when the projector has ample memory available.
Let integrated GPUs use the same projector-memory check as other GPUs. When projector offload is enabled, add the estimated projector memory plus the existing 1 GiB headroom to Ollama-owned LLAMA_ARG_FIT_TARGET so fit leaves space for the later projector allocation. If Ollama/device setup already supplied a fit target, add the projector pad to it. If the user set LLAMA_ARG_FIT_TARGET explicitly, leave it exactly as provided.
Fixes#16419
* review comments
Recent upstream Pascal kernel fixes let us compile native SM60/SM61 kernels again instead of relying on PTX JIT, so allow Flash Attention auto at runtime for CC 6.x devices.
Fixes#16591Fixes#16754
* llm: fix ollama ps double-counting mmap'd weights on partial offload
With mmap enabled, llama-server reports each CPU_Mapped model buffer as the
file-offset span of its CPU-resident tensors. During partial offload that span
covers nearly the whole file because the first and last tensors stay on CPU, so
the parsed buffer sizes count the offloaded weights twice and ollama ps shows
roughly 2x the real size with a false CPU/GPU split. Model weights can never
exceed the model file on disk, so trim the excess over the file size from the
mmap-backed portion when computing MemorySize. This makes the reported size
independent of use_mmap; VRAM accounting and scheduler placement are unchanged.
* llm: exclude repacked model buffers from the mmap overlap trim
The trim that corrects mmap double-counting computed the overlap from all
model buffers, including real copies such as CPU_REPACK. On a CPU-only
repacked model that inflated the excess and trimmed the repack out,
undercounting by the repack size (llama3.2 reported ~1918 MiB instead of
~3218 MiB).
Compute the overlap from file-backed buffers only: mmap views and direct
device copies, whose spans can overlap the file on partial offload.
Repacked or host-pinned CPU copies are separate allocations that never
overlap the on-disk weights, so leave them intact. Adds a CPU_Mapped +
CPU_REPACK regression test and corrects the Metal case to the real total.
* llm: size mmproj offload by projector memory
Replace the blanket 10 GiB VRAM cutoff with a projector tensor-size estimate plus backend headroom, while preserving the existing CPU-only, partial text offload, shared-memory GPU, and startup OOM retry gates.
This is a stopgap until fit accounts for mmproj memory directly.
The same limited-vram path appears in the qwen3.5 vision hang report: the logs show --no-mmproj-offload on a 7.5 GiB RTX 5050 with about 6.4 GiB free while llama-server estimates the inline mmproj at about 962 MiB.
Fixes#16496Fixes#16570
* review comments
This PR separates prompt caching from the public shift request option for native llama-server requests.
Previously, shift controlled two different mechanisms:
context shifting / overflow behavior
per-request llama-server cache_prompt
That meant callers could not request shift: false without also disabling prompt caching.
Fixes#16635
llama.cpp b9478 added a default 30s SSE ping that emits colon-only comment frames (":\n\n") while streamed requests are idle; Ollama treated non-data SSE lines as JSON, so skip SSE comments in completion and chat streams.
llama-server model loads could time out after the fixed load duration even while tensor-loading progress dots were still being emitted, so track raw runner output activity and use OLLAMA_LOAD_TIMEOUT as a stall deadline.
Fixes#16416Fixes#16412
* llama-server followups
Misc fixes for #16031
- Add back dropped ROCm build flag for multi-GPU support on windows
- Fix amdhip64_*.dll version detection for "latest" selection
- Fix embeddings API for consistent normalize behavior with prior versions
* ci: set up for automated llama.cpp update testing
* reduce batch for fa-disabled, and constrained vram
* mlx: fix v3 load bug on m5
Imagegen was incorrectly loading v3 first. This DRYs out the loading code so imagegen gets the same new v4/v3 selection logic.
* fix reload bug on embedding models
* bump version
* steer user how to enable iGPU when disabled
* broad lint fixes to sidestep CI scope glitch
* runner: Remove CGO engines, use llama-server exclusively for GGML models
Remove the vendored GGML and llama.cpp backend, CGO runner, Go model
implementations, and sample. llama-server (built from upstream llama.cpp via
FetchContent) is now the sole inference engine for GGUF-based models.
(Safetensor based models continue to run on the new MLX engine.) This allows
us to more rapidly pick up new capabilities and fixes from llama.cpp as they
come out.
On windows this now requires recent AMD driver versions to support ROCm v7 as
llama.cpp currently does not support building against v6.
* llama/compat: load Ollama-format GGUFs in llama-server
Squashed from upstream/jmorganca/llama-compat on 2026-04-29.
Source tip: 0c33775d37.
Original source commits:
- 25223160d llama/compat: add in-memory shim so llama-server can load Ollama-format GGUFs
- 7449b539a llm,server: route Ollama-format gemma3 blobs through llama/compat
- 436f2e2b1 llama/compat: make patch-apply idempotent
- 8c2c9d4c8 llama/compat: extend gemma3 handler to cover 1B and 270M blobs
- 021389f7b llama/compat: shrink clip.cpp injection from 18 lines to 1
- 61b367ec2 llama/compat: shrink patch to pure call-site hooks (34 -> 20 lines)
- 36049361c llama/compat: simplify shim (gemma3-tested)
- 8fa664865 llama/compat: add qwen35moe text handler
- db0c74530 llama/compat: add qwen35moe vision (clip) support
- 2a388da77 llama/compat: split shared infra into a util TU
- 9a69a17dc llama/compat: document non-public API dependencies
- d0f38a915 llama/compat: add gpt-oss and lfm2 handlers
- 086071822 llama/compat: add mistral3 text handler (vision TODO)
- 63bde9ff7 llama/compat: add mistral3 vision (clip) support
- 3a57b89d5 llama/compat: apply LLaMA RoPE permute to mistral3 vision Q/K
- 99cb87439 llama/compat: add qwen35, gemma4, deepseek-ocr handlers
- 2c7850dba llama/compat: add nemotron_h_moe handler (latent FFN + MTP skip)
- 9e3b54225 llama/compat: add llama4 text + clip handlers
- 034fee349 llama/compat: add gemma4 clip handler (gemma4v projector)
- 9945c5a93 server: remove dhiltgen/* compat redirect table
- 5d4539101 llama/compat: rewrite gemma4 tokenizer model to BPE
- 7e0765327 llama/compat: add glm-ocr text handler + text-loader load-op hook
- f1bd1a25a llama/compat: add glm-ocr clip handler (glm4v projector)
- 4b5cf3420 llama/compat: collapse text-loader hook back to one new patch line
- eb4ecf4fc llama/compat: extend gemma4 clip handler to gemma4a (audio)
- a23a5e76f llama/compat: fix gemma4a per-block norm tensor mapping
- cd2dcaff4 llama/compat: add embeddinggemma handler
- 1ce8a6b26 llama/compat: add qwen3-vl + qwen2.5-vl handlers
- fd98ffa1e llama/compat: add gemma3n + glm4moelite handlers
- cc7bdf0bc llama/compat: handle null buft in maybe_load_tensor
- 0c33775d3 llama/compat: disable mmap when load_op transforms text-side tensors
* refine implementation
* ci: fix windows MLX build
* ci: fix windows llama-server build
* ci: fix windows rocm build
* ci: windows mlx tuning
Shorten long-tail on build, and get OllamaSetup.exe back under 2g limit
* ci: fix windows dependencies
* win: fix dependency gathering
* disable openmp
* win: arm64 cross-compile build
also DRY out CI steps
* scheduler improvements
* ci: improvements from #15982
* win: favor ninja for faster developer builds
* win: fix build
* win: fix arm64 cross-compile
* win: avoid spaces in compiler path
* misc discovery fixes, and bos handling
* lint fixes
* win: fix arm cross-compile build/CI bugs
* llama.cpp update
* win: handle multiple CRT dirs
* vulkan: add windows iGPU detection
* fix creation bugs for patched models, other refactoring work
* tune batch size for better performance
* ci and lint fixes
* fix repeat_last_n bug
* build: revamp build for better developer UX
* amd, sampler, qwen3next fixes
* version bump
* fix mlx build
* revamp GPU discovery
Scanning the output of llama-server is turning out to be too error prone across
llama.cpp updates, so this switches to a thin dynamic library load against the
bundled GGML libraries so more details can be gathered from the API.
* version bump
* missing file
* ci: fix cache miss on rocm build
* refine vulkan dep handling
* fix ps reporting bug on full GPU load
* improve cmake wiring for customized local builds
* version bump
* docker build arg cleanup
* improve windows exit error logs
* fix community gemma4 support and ci flakes
* fix mlx unit test
* tighten up ps logic to avoid double counting fit log lines
* version bump
* fix ps view for full gpu layer offload
* add MTP wiring for llama-server and create with GGUFs
* pick best template by capabilities
* version bump
* ci: harden apt repos
* remove unused cpu core discovery
* adjust batch default logic to reduce OOMs
* support larger tool calls
* fix audio support, template show
* qwen35 mtp patch support
* flesh out dtypes
* rocm deps
* version bump
* lint fix
* block broken gfx1150 on windows
* fix qwen3.5 moe mtp tensors in patch
* mmproj oom fallback and vulkan on by default
* qwen MTP compat fix
* version bump
* ci: fix WoA cross-compile
* ci: workaround ui tool in cross-compile
* version bump
* win: enable OpenMP for CPU builds
* build: improve developer UX
* ci: windows path workaround for CPU build
* win: fix WoA dependencies
* win: fix large offset reads for mmproj patched loads
* version bump
* fix vulkan dup detection
* add OLLAMA_IGPU_ENABLE and largely disable iGPUs by default
* opt-in MTP, win large offset, integraton fixes
* fix unit test scheduler interaction hang
* fix multi-gpu filtering
* version bump
* review comments
* fix thinking level
* fix linux rocm ordering and granite 3.3 template
* version bump
* ci fix - non-shallow MLX checkout
* bypass linux sysfs unit test on windows
---------
Co-authored-by: jmorganca <jmorganca@gmail.com>
* Update MLX and MLX-C
* Run MLX CGO work on a locked OS thread
MLX now relies on OS-thread-local execution state for streams, encoders, and caches. Add an mlxthread executor backed by runtime.LockOSThread and route runner initialization, model load, inference, status memory reads, and cleanup through the worker so Go goroutine migration cannot split MLX state across native threads.
Also stop caching default MLX streams before the runner owns the thread and add worker/threaded MLX regression tests.
* mlx: use common status writer
* mlx: bundle missing libjaccl on arm64
Inspired by #15793
* review comments
* metal: harden for ggml initialization failures
ggml_metal_device_init performs a probe to verify the tensor API compiles. On
some systems this passes, even though kernel coverage isn't complete, which
results in a later crash when compiling the real kernels. This change adds a
single retry if any of the error strings match this failure mode to disable the
tensor API. It also hardens an error case in the Go initDevices to detect
device initialization failures and panic instead of crashing later on a nil
array entry.
Fixes#15734
* review comments
* review comments
Receiving from a buffered chan error consumes the value, so only the
first caller (WaitUntilRunning, HasExited, or Close) sees the signal.
Subsequent receivers block or take the wrong branch. Replace with a
closed chan struct{} which can be received from any number of times,
and store the error in a separate field.
The MLX runner previously reported a static VRAM estimate that was
computed at load time and consisted only of the weights. This is
strictly less than the actual memory usage, as it does not include
the KV cache or compute graph.
When context length is clamped to the model's trained context length,
ollama ps now shows the actual clamped value instead of the originally
configured value.
On the llama engine, when we compute the memory layout, we reserve
a buffer to allow for some flexibility for incorrect estimates.
This is subtracted from GPU free memory and on GPUs with limited
memory, it may underflow.
Fixes#13494
* flash attn: add auto mode for llama engine
If the user does not specify fa in the environment, use auto-mode.
* review comments
* ensure kv cache quantized types have FA explicitly enabled
additional review comments
This changes the default behavior to use the Ollama engine for supported
models, while retaining the ability to disable the Ollama engine and
fall back to the Llama engine. Models in the OllamaEngineRequired list
will always run on the Ollama engine.
This PR detects embedding models and sets batch_size = context_size so the full input fits in a single batch.
Previously, if batch size was smaller than the input, tokens could be split across batches and cause a SIGTRAP crash.
This change ensures all tokens stay in one batch and prevents crashes.
Fixes: #12938#13054
Co-authored-by: Jesse Gross <jesse@ollama.com>
We now do a deeper probe of CUDA devices to verify the library version has
the correct compute capability coverage for the device. Due to ROCm also
interpreting the CUDA env var to filter AMD devices, we try to avoid setting
it which leads to problems in mixed vendor systems. However without setting
it for this deeper probe, each CUDA library subprocess discovers all CUDA GPUs
and on systems with lots of GPUs, this can lead to hitting timeouts. The fix is
to turn on the CUDA visibility env var just for this deeper probe use-case.
Model eviction happens when we have at least one other model
loaded and are unable to load all layers into VRAM. However, on
CPU-only systems we can never load layers into VRAM, so this
constantly triggered eviction.
Fixes#13227
We currently assign model layers to GPUs according to free VRAM,
which assumes that GPU performance is roughly equal. This does not
work well for mixed dGPU and iGPU systems because iGPUs typically
use system memory which is large but their performance is slow.
This instead assigns layers to dGPUs first and then iGPUs.
In the future, this could be generalized to have a more fine grained
notion of GPU performance but dGPU vs. iGPU performance is the most
extreme.
Originally, llamaServer represented old memory estimates, which
could be used with either the old or new engine. ollamaServer was
used only for the new estimates and new engine. Since these
implementations did not map directly to engine, there was engine-
specific code in common code paths.
Now that new estimates are always used for the new engine, there is
a direct mapping between server type and engine. This separates out
most of the engine-specific code into the correct implementation
to make things easier to understand.
Currently for both the old and new engines, there is code to
calculate how much memory is required for a model and lay out
the layers onto GPUs. This reuses the new engine's lay out code
for the old engine as well, bringing them closer together. The
old engine continues to use its current method of estimating
required memory.
This reduces maintainence effort and improves consistency, as new
features only need to be implemented in one place. The newer code
is also more accurate, especially with multiple GPUs.
Daniel Hiltgen