mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2026-10-17 02:10:36 +02:00
ggml-openvino: update to 2026.4.1, optimize performance, expand ops, improve device listing. (#29852)
* ggml-openvino : Qwen3.5 MoE perf (#312) Squash of ravi9/llama.cpp#312: - ggml-openvino: add detailed inference profiling (Yu, Zijun) - ggml-openvino: use remote output tensors by default (Yu, Zijun) - ggml-openvino: optimize single-sequence recurrent state (Yu, Zijun) - opt1: remove recurrent reset for single sequence, opt2: direct gdn outputs (break parallel sequence) (Yu, Zijun) - fix parallel sequences (Yu, Zijun) - ggml-openvino: simplify graph cache key (ynimmaga) - enable stateful for qwen35 single sequence (Yu, Zijun) - Fix after rebasing (Yu, Zijun) - Add k-requant option q4_asym64 (Yu, Zijun) - Fix qwen35 llama-bench -p 0 (Yu, Zijun) - Simplify RESHAPE translation (Yu, Zijun) - openvino: fuse MoE routing (Yu, Zijun) - openvino: fuse GDN qk normalization (Yu, Zijun) - openvino: enable GPU MoE fusion by default (Yu, Zijun) - ggml-openvino: add cache_only mode to import cached compiled model on disk directly (Yu, Zijun) - openvino : report the device allocation limit to ggml (Łukasz Ślusarczyk) - Fix windows build (Yu, Zijun) Co-authored-by: ynimmaga <ynimmaga@users.noreply.github.com> Co-authored-by: Łukasz Ślusarczyk <lukasz.slusarczyk@intel.com> * ggml-openvino: Update doc of compiled model cache * openvino: implement PRD-compliant device enumeration and memory reporting * openvino: fix multi-device listing issues from review - Only the device selected by GGML_OPENVINO_DEVICE reports as GPU; the other OpenVINO devices report as IGPU so llama.cpp does not offload to them. Initializing a non-selected device logs a warning. - Name devices OPENVINO<i> again and show the OpenVINO id in the description. Raw "CPU" names shadowed the ggml CPU backend. - Support GPU.N: create the OpenCL queue on OpenVINO's own context for the selected device, and replace "GPU"/"NPU" string comparisons with ggml_openvino_is_gpu()/ggml_openvino_is_npu(). - An unavailable GGML_OPENVINO_DEVICE is now an error that lists the available devices, instead of silently falling back to CPU. - Memory: cap iGPU/NPU free memory at system available memory, fall back to system memory instead of 0/0 when the plugin lacks memory properties, and ignore host USM allocations in GPU usage. - Initialize the device config once under a lock, even if OpenCL setup fails. - Fix supports_op return type for non-selected devices (build error). * openvino : take USM entry points from the selected device platform clGetExtensionFunctionAddressForPlatform was called on the first platform returned by clGetPlatformIDs. The address it returns is only valid for the platform it was queried on, and the first platform is not always the one that holds the device OpenVINO selected. On a host whose first platform comes from another vendor the lookup returns null, and then every read, write and memset on a GPU buffer fails with "clEnqueueMemcpyINTEL not available". Look both entry points up in init(), on the platform of the device OpenVINO picked, and keep them in the device config next to the command queue. Assisted-by: Claude Opus 5 * openvino: fuse MoE experts for models with a fused gate_up weight FuseMoeCompressed only matches models whose gate and up projections are separate GatherMatmul ops. gemma-4 packs both into one expert weight and splits the result after the GEMM, so its MoE block stayed unfused and ran the expert GEMMs as per-token GEMVs. Add FuseMoeCompressedFusedGateUp, which matches that shape (one GatherMatmul -> Slice/Slice -> Gelu(ERF) -> Multiply) and folds it into the same MOECompressed op, using GEMM3_SWIGLU with GEGLU_ERF. The fused weight, scale and zero point are split into gate/up halves by copying raw bytes, since a graph Slice would be rewritten to StridedSlice and constant folded, whose reference evaluator crashes on sub-byte types. gemma-4 also applies a per-expert output scale to the down projection before the router weights. MOECompressed takes only one per-expert weight, so that scale is folded into the routing weights, which is exact. The op reads the zero point straight off a weight port and needs an integer Constant there, so the matcher requires one and leaves natively quantized experts (exact f16 zp) to the unfused path. gemma-4-26B-A4B on Arc B390, GGML_OPENVINO_REQUANT_KQUANT=q4_asym64_all, llama-bench -p 512 -n 128 -r 2, against a GGML_OPENVINO_MOE_OP=0 baseline: pp512 66.16 -> 1608.73 t/s, tg128 25.94 -> 26.46 t/s. Perplexity over 12 chunks is unchanged (1451.3 +/- 177.9 unfused vs 1427.6 +/- 175.1 fused). No effect without that requant option, on models with separate gate/up weights, or on CPU. test-backend-ops -b OPENVINO0 is unchanged by this commit: two MUL_MAT_ID m_v cases fail, the same two on the unmodified base. * openvino: fix rank-3 axis handling so MoE works under stateful execution Stateful execution drops the leading size-1 batch dim, so OV tensors are rank 3 while GgmlOvDecoder::get_shape/get_stride still report GGML_MAX_DIMS=4 reversed entries. Several MoE ops derive OV axis indices straight from that metadata, so they picked the wrong axis. A MoE model with GGML_OPENVINO_STATEFUL_EXECUTION=1 aborts while building the graph: Check 'is_axis_valid(axis, r)' failed at src/core/src/validation_util.cpp:336 While validating node 'opset11::TopK ... _ffn_moe_probs ...' Axis 3 out of the tensor rank range [-3, 2]. Fix idiom throughout: take the axis from the real OV rank, or shift a metadata-derived axis down by metadata_rank - actual_rank. argsort.cpp the router top-k axis is 2 on rank 3, not 3. This is the abort quoted above. add.cpp the MoE expert-sum bypass collapses the 8-ADD chain into one ReduceSum on hardcoded axis 2, which on rank 3 reduces n_embd instead of the expert axis. Now rank-2, with the following Unsqueeze at rank-3. get_rows.cpp squeezing a hardcoded {0,1} also strips the batch dim whenever it is 1, which is every decode step. Squeeze down to the trailing two dims instead. mul_mat_id.cpp pick the reshape dims by actual rank, and skip the trailing Unsqueeze that re-adds the batch dim. view.cpp the expert-plane slice had the Slice axis, dst_ov_axis, the ShapeOf+Gather index and the Reshape target all rank-4. utils.cpp process_view_input_new's "translate_view already resolved this VIEW, skip re-slicing" shortcut required equal ranks. 4 vs 3 never matched, so every resolved expert plane got re-sliced. Now compares the common trailing dims. Same axis shift for the Slice in the view-chain walker. Stateless is unchanged by construction: every edit is gated on the actual rank, so axis_shift == 0 reproduces the previous code exactly. Checked on OV-CPU by diffing greedy output against the unmodified base for dense gemma-4-E2B, granite-1b-a400m and gemma-4-26B-A4B; all identical. granite-1b-a400m on OV-CPU aborts with the error above before this change; after it, it generates and is byte-identical to stateless. Dense gemma-4-E2B is identical stateless vs stateful both before and after. test-backend-ops -b OPENVINO0 is unchanged: two pre-existing MUL_MAT_ID m_v cases fail, the same two on the unmodified base. gemma-4-26B-A4B is a poor correctness vehicle here. On OV it already drifts into degenerate repetition a few tokens in, in stateless as much as stateful, and the two modes diverge somewhere inside that degenerate region instead of matching token for token. Each mode is self-reproducible across runs. Known limitation: FuseMoeCompressedFusedGateUp does not match the rank-3 graph, so a MoE model run with GGML_OPENVINO_STATEFUL_EXECUTION=1 loses the prefill fusion while gaining decode. gemma-4-26B-A4B on Arc B390, GGML_OPENVINO_REQUANT_KQUANT=q4_asym64_all, llama-bench -p 512 -n 128 -r 2: unfused (GGML_OPENVINO_MOE_OP=0) pp512 66.16 tg128 25.94 fused, stateless (default) pp512 1608.73 tg128 26.46 fused, stateful pp512 66.18 tg128 29.91 Stateful is opt-in and off by default, and MoE did not run there at all before this, so nothing that previously worked regresses. Making the pass match rank 3 is the follow-up. * OpenVINO Backend: Upgrade graph cache to use node_idx, src_idx, node type * ggml-openvino : enable more comprehensive conv fusion * enable conv ops * Reject kernel size 0 and support IM2COL_3D * openvino : abort when the GPU remote context cannot be created init() logged the error and returned, which left the device name a GPU but remote_context empty. The remote buffer and tensor paths assert only on the device being a GPU and then dereference that empty optional. Those paths have no host fallback, and a device that OpenVINO listed should have a working OpenCL context, so stop instead of continuing. An OpenCL stack that is broken as a whole is still caught earlier by the device availability check, which falls back to CPU. Assisted-by: Claude Opus 5 * openvino : fix build warnings The single-argument form of the OpenVINO RTTI macros is the intended one, but their selector macro leaves __VA_ARGS__ empty, which -Wpedantic reports on every pass and op header. Turn that warning off for this backend only, the way ggml-cuda and ggml-sycl already do for their own third-party warnings. Also drop a break and a dead assignment around a GGML_ABORT, which is noreturn. Assisted-by: Claude Opus 5 * OpenVINO Backend: Support common MTMD ops * ggml-openvino: give a reshaping view its own ov::Tensor * ggml-openvino : compute HARDSIGMOID and EXPM1 in f32 HARDSIGMOID used a 1/6 constant in the input type, which is not exact in bf16, and EXPM1 lost precision for small inputs in f16. Both now compute in f32 and convert back, except on NPU where the f32 path gives wrong results. Fixes the HARDSIGMOID/EXPM1 test-backend-ops failures on GPU. * ggml-openvino : update device selection and --list-devices Show the selecting GGML_OPENVINO_DEVICE value and active device in --list-devices, startup logs, and backend tests. Clarify OpenVINO selection uses GGML_OPENVINO_DEVICE, not -dev. * openvino : remove unreachable OpenCL queue checks A remote buffer exists only on a GPU device, and init() aborts there if the queue cannot be created, so the queue is never null at these call sites. Assisted-by: Claude Opus 5 * openvino : update OpenVINO to 2026.4.1 and GPU drivers to 26.35.39758.10 * docs : update OpenVINO validated models and GPU driver version * ggml-openvino : skip empty views when giving a reshaping view its own tensor A zero-size view can sit at the end of a GPU USM buffer (Qwen3.5 recurrent cache). Wrapping it as a remote tensor throws "shared USM buffer has smaller size (0)". Assisted-by: Claude * ggml-openvino : rebind the cached decoder when llama passes a different graph llama keeps separate graphs for batches with and without outputs. llama-server splits the prompt into chunks for context checkpoints, so a cached decoder could be reused with a graph built in other memory and bind the previous chunk's input tensors. SWA and recurrent models then lost most of the prompt in llama-cli and llama-server. Assisted-by: Claude * docs : update OpenVINO validated models Smoke test on Lunar Lake (32 GB) with the two fixes above. Re-add the Qwen3.5 and gemma models. Assisted-by: Claude --------- Co-authored-by: Yu, Zijun <zijun.yu@intel.com> Co-authored-by: ynimmaga <ynimmaga@users.noreply.github.com> Co-authored-by: Łukasz Ślusarczyk <lukasz.slusarczyk@intel.com> Co-authored-by: haarika-madaka <haarika.madaka@intel.com> Co-authored-by: Mustafa Cavus <mustafa.cavus@intel.com> Co-authored-by: Mostafa Faheem <mostafaaafaheem@gmail.com>
This commit is contained in:
Ravi Panchumarthy
co-authored by
Yu, Zijun
ynimmaga
Łukasz Ślusarczyk
haarika-madaka
Mustafa Cavus
Mostafa Faheem
Yu, Zijun
ynimmaga
Łukasz Ślusarczyk
haarika-madaka
Mustafa Cavus
Mostafa Faheem
parent
cb7934c52c
commit
b92761a515
+30
-26
@@ -52,8 +52,8 @@ Although OpenVINO supports a wide range of [Intel hardware](https://docs.openvin
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- `Q4_1`
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- `Q4_K`
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- `Q4_K_M`
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- `Q5_K` (converted to `Q8_0_C` at runtime)
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- `Q6_K` (converted to `Q8_0_C` at runtime)
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- `Q5_K` (converted to `Q8_0_C` at runtime by default)
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- `Q6_K` (converted to `Q8_0_C` at runtime by default)
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> [!NOTE]
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> Accuracy validation and performance optimizations for quantized models are a work in progress.
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@@ -93,12 +93,12 @@ Although, the validated models below were tested with `llama-cli` using the `Q4_
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> Extensive accuracy validation, performance optimizations, and broader architecture coverage are work in progress.
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**Legend & Test Configuration:**
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- **Status:** ✓ = Passed | ✗ = Failed or Unsupported
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- **Status:** ✓ = Passed | ~ = Accuracy issues | ✗ = Failed or Unsupported
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- **Execution Modes:**
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- **SL** = Stateless (`GGML_OPENVINO_STATEFUL_EXECUTION=0`)
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- **SF** = Stateful (`GGML_OPENVINO_STATEFUL_EXECUTION=1`)
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- Note: The NPU operates in stateless mode only.
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- **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.
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- **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 26.35.39758.10-0 | Intel NPU Driver 1.38.0.
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- See [Known Limitations](#known-limitations) for context on observed failures.
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| Model | CPU (SL / SF) | GPU (SL / SF) | NPU (SL) |
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@@ -113,14 +113,14 @@ Although, the validated models below were tested with `llama-cli` using the `Q4_
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| [bartowski/Qwen_Qwen3-1.7B-Q4_K_M](https://huggingface.co/bartowski/Qwen_Qwen3-1.7B-GGUF) | ✓ / ✓ | ✓ / ✓ | ✓ |
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| [Qwen/Qwen3-4B-Q4_K_M](https://huggingface.co/Qwen/Qwen3-4B-GGUF) | ✓ / ✓ | ✓ / ✓ | ✓ |
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| [lm-kit/Qwen3-8B-Q4_K_M](https://huggingface.co/lm-kit/qwen-3-8b-instruct-gguf) | ✓ / ✓ | ✓ / ✓ | ✓ |
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| [bartowski/Qwen_Qwen3.5-0.8B-Q4_K_M](https://huggingface.co/bartowski/Qwen_Qwen3.5-0.8B-GGUF) | ✓ / ✗ | ✓ / ✗ | ✗ |
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| [bartowski/Qwen_Qwen3.5-2B-Q4_K_M](https://huggingface.co/bartowski/Qwen_Qwen3.5-2B-GGUF) | ✓ / ✗ | ✓ / ✗ | ✗ |
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| [bartowski/Qwen_Qwen3.5-4B-Q4_K_M](https://huggingface.co/bartowski/Qwen_Qwen3.5-4B-GGUF) | ✓ / ✗ | ✓ / ✗ | ✗ |
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| [lmstudio-community/Qwen3.5-9B-Q4_K_M](https://huggingface.co/lmstudio-community/Qwen3.5-9B-GGUF) | ✓ / ✗ | ✓ / ✗ | ✗ |
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| [bartowski/Qwen_Qwen3.5-0.8B-Q4_K_M](https://huggingface.co/bartowski/Qwen_Qwen3.5-0.8B-GGUF) | ✓ / ✓ | ✓ / ~ | ✗ |
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| [bartowski/Qwen_Qwen3.5-2B-Q4_K_M](https://huggingface.co/bartowski/Qwen_Qwen3.5-2B-GGUF) | ✓ / ✓ | ✓ / ~ | ✗ |
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| [bartowski/Qwen_Qwen3.5-4B-Q4_K_M](https://huggingface.co/bartowski/Qwen_Qwen3.5-4B-GGUF) | ✓ / ✓ | ✓ / ~ | ✗ |
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| [lmstudio-community/Qwen3.5-9B-Q4_K_M](https://huggingface.co/lmstudio-community/Qwen3.5-9B-GGUF) | ✓ / ✓ | ✓ / ~ | ✗ |
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| | | | |
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| [unsloth/gemma-3-4b-it-Q4_K_M](https://huggingface.co/unsloth/gemma-3-4b-it-GGUF) | ✓ / ✓ | ✓ / ✓ | ✓ |
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| [bartowski/google_gemma-4-E2B-it-Q4_K_M](https://huggingface.co/bartowski/google_gemma-4-E2B-it-GGUF) | ✓ / ✓ | ✓ / ✓ | ✗ |
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| [bartowski/google_gemma-4-E4B-it-Q4_K_M](https://huggingface.co/bartowski/google_gemma-4-E4B-it-GGUF) | ✓ / ✓ | ✓ / ✓ | ✓ |
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| [bartowski/google_gemma-4-E2B-it-Q4_K_M](https://huggingface.co/bartowski/google_gemma-4-E2B-it-GGUF) | ✓ / ✓ | ✓ / ~ | ~ |
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| [bartowski/google_gemma-4-E4B-it-Q4_K_M](https://huggingface.co/bartowski/google_gemma-4-E4B-it-GGUF) | ✓ / ✓ | ✗ / ✗ | ✓ |
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| [bartowski/gemma-4-12B-it-Q4_K_M](https://huggingface.co/bartowski/gemma-4-12B-it-GGUF) | ✓ / ✓ | ✓ / ✓ | ✗ |
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| | | | |
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| [bartowski/Phi-3-mini-4k-instruct-Q4_K_M](https://huggingface.co/bartowski/Phi-3-mini-4k-instruct-GGUF) | ✓ / ✓ | ✓ / ✓ | ✓ |
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@@ -134,9 +134,9 @@ Although, the validated models below were tested with `llama-cli` using the `Q4_
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| [bartowski/DeepSeek-R1-Distill-Llama-8B-Q4_K_M](https://huggingface.co/bartowski/DeepSeek-R1-Distill-Llama-8B-GGUF) | ✓ / ✓ | ✓ / ✓ | ✓ |
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| [bartowski/DeepSeek-R1-Distill-Qwen-7B-Q4_K_M](https://huggingface.co/bartowski/DeepSeek-R1-Distill-Qwen-7B-GGUF) | ✓ / ✓ | ✓ / ✓ | ✓ |
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| | | | |
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| [ibm-granite/granite-4.0-350m-Q4_K_M](https://huggingface.co/ibm-granite/granite-4.0-350m-GGUF) | ✓ / ✓ | ✓ / ✓ | ✓ |
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| [ibm-granite/granite-4.0-350m-Q4_K_M](https://huggingface.co/ibm-granite/granite-4.0-350m-GGUF) | ✓ / ✓ | ~ / ~ | ✓ |
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| [ibm-granite/granite-4.0-micro-Q4_K_M](https://huggingface.co/ibm-granite/granite-4.0-micro-GGUF) | ✓ / ✓ | ✓ / ✓ | ✓ |
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| [ibm-granite/granite-4.0-1b-Q4_K_M](https://huggingface.co/ibm-granite/granite-4.0-1b-GGUF) | ✓ / ✓ | ✓ / ✓ | ✗ |
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| [ibm-granite/granite-4.0-1b-Q4_K_M](https://huggingface.co/ibm-granite/granite-4.0-1b-GGUF) | ✓ / ✓ | ~ / ~ | ~ |
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| [ibm-research/granite-3.2-8b-instruct-Q4_K_M](https://huggingface.co/ibm-research/granite-3.2-8b-instruct-GGUF) | ✓ / ✓ | ✓ / ✓ | ✓ |
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| [HuggingFaceTB/smollm2-1.7b-instruct-q4_k_m](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct-GGUF) | ✓ / ✓ | ✓ / ✓ | ✓ |
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@@ -244,8 +244,8 @@ chmod +x build-llamacpp-ov.sh
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# ============================================
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set -euo pipefail
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OPENVINO_VERSION_MAJOR="2026.4"
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OPENVINO_VERSION_FULL="2026.4.0.22959.99c81491cc3"
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OPENVINO_VERSION_MAJOR="2026.4.1"
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OPENVINO_VERSION_FULL="2026.4.1.22982.07f9c262b05"
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SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
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OPENVINO_INSTALL_DIR="/opt/intel/openvino_${OPENVINO_VERSION_MAJOR}"
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@@ -342,7 +342,7 @@ echo " ./build/ReleaseOV/bin/llama-cli -m model.gguf"
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```
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> [!NOTE]
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> 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.
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> The script pins OpenVINO `2026.4.1` via the `OPENVINO_VERSION_MAJOR` / `OPENVINO_VERSION_FULL` variables at the top — edit them to track a different release.
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</details>
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@@ -372,8 +372,8 @@ REM ============================================
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REM llama.cpp OpenVINO Build Script (Ninja)
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REM ============================================
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set "OPENVINO_VERSION_MAJOR=2026.4"
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set "OPENVINO_VERSION_FULL=2026.4.0.22959.99c81491cc3"
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set "OPENVINO_VERSION_MAJOR=2026.4.1"
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set "OPENVINO_VERSION_FULL=2026.4.1.22982.07f9c262b05"
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set "SCRIPT_DIR=%~dp0"
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set "VCPKG_DIR=C:\vcpkg"
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@@ -552,7 +552,7 @@ endlocal
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```
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> [!NOTE]
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> 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**.
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> The script pins OpenVINO `2026.4.1` 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**.
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</details>
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@@ -625,7 +625,7 @@ $env:GGML_OPENVINO_DEVICE = "NPU"
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build\ReleaseOV\bin\llama-cli.exe -m "C:\models\Llama-3.2-1B-Instruct-Q4_K_M.gguf" -c 512
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```
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> [!NOTE]
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> On systems with multiple GPUs, use `GPU.0` or `GPU.1` to explicitly target specific GPU. See [OpenVINO GPU Device](https://docs.openvino.ai/2026/openvino-workflow/running-inference/inference-devices-and-modes/gpu-device.html) for more details.
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> On systems with multiple GPUs, use `GPU.0` or `GPU.1` to explicitly target specific GPU. A device that is not available is an error (no fallback to CPU), and the error message lists the available OpenVINO devices with their names. Run `llama-cli --list-devices` to see the valid values: each OpenVINO device shows the `GGML_OPENVINO_DEVICE=<value>` to set, and `(selected)` marks the active one. Select the OpenVINO device with this variable, not with `-dev`. See [OpenVINO GPU Device](https://docs.openvino.ai/2026/openvino-workflow/running-inference/inference-devices-and-modes/gpu-device.html) for more details.
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### 5. Docker Build
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@@ -713,12 +713,13 @@ Boolean flags follow a uniform convention: set to a **positive integer** (e.g. `
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| Variable | Type | Default | Description |
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|-----------------------------------|-----------|------------|-------------------------------------------------------------------------------------------------------------|
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| `GGML_OPENVINO_DEVICE` | String | `CPU` | Specify the target device (CPU, GPU, NPU). On systems with multiple GPUs, use `GPU.0` or `GPU.1` to explicitly target specific GPU. See [OpenVINO GPU Device](https://docs.openvino.ai/2026/openvino-workflow/running-inference/inference-devices-and-modes/gpu-device.html). When set to **NPU**, static compilation mode is enabled for optimal performance. |
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| `GGML_OPENVINO_CACHE_DIR` | String | `not set` | Directory for OpenVINO model caching (recommended: `/tmp/ov_cache`). Enables model caching when set. **Not supported on NPU devices.** |
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| `GGML_OPENVINO_COMPILED_MODEL_CACHE_DIR` | String | `not set` | Directory for the frontend compiled-model cache. When set, OpenVINO compiled models are exported as blobs and imported on later runs to skip weight requantization, graph conversion, and compilation for matching single-graph models. |
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| `GGML_OPENVINO_DEVICE` | String | `CPU` | Specify the target device (CPU, GPU, NPU). On systems with multiple GPUs, use `GPU.0` or `GPU.1` to explicitly target specific GPU. A device that is not available is an error (no fallback to CPU), and the error message lists the available OpenVINO devices with their names. See [OpenVINO GPU Device](https://docs.openvino.ai/2026/openvino-workflow/running-inference/inference-devices-and-modes/gpu-device.html). When set to **NPU**, static compilation mode is enabled for optimal performance. |
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| `GGML_OPENVINO_CACHE_DIR` | String | `not set` | Directory for OpenVINO's separate plugin cache. On NPU, this sets `NPUW_CACHE_DIR`. |
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| `GGML_OPENVINO_COMPILED_MODEL_CACHE_DIR` | String | `not set` | Directory for standalone compiled blobs with weights. Dynamic CPU/GPU graphs can import matching blobs on later runs. |
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| `GGML_OPENVINO_COMPILED_MODEL_CACHE_ONLY` | Boolean | `0` | Require an existing compiled blob and skip weight uploads and compilation. Requires Linux or Windows mmap loading and a full dynamic CPU/GPU graph on OpenVINO. |
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| `GGML_OPENVINO_PREFILL_CHUNK_SIZE`| Integer | `256` | Token chunk size for **NPU** prefill (NPU-only; ignored on CPU/GPU). Must be a positive integer; otherwise the default is used. |
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| `GGML_OPENVINO_NPU_COMPILE_CONFIG` | String | `not set` | NPU-only compiler mode parameters forwarded to OpenVINO as `NPU_COMPILATION_MODE_PARAMS`, for example `optimization-level=3`. |
|
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| `GGML_OPENVINO_STATEFUL_EXECUTION`| Boolean | `0` | Enable stateful KV cache for better performance. Recommended on CPU, GPU. |
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| `GGML_OPENVINO_STATEFUL_EXECUTION`| Boolean | `0` | Keep KV and supported recurrent caches inside the model. Single-slot CPU/GPU execution only. |
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| `GGML_OPENVINO_DISABLE_CACHE` | Boolean | `0` | Disable the in-process compiled-model / decoder cache (cache is on by default). Set to `1` to disable. |
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| `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. |
|
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@@ -727,8 +728,10 @@ Boolean flags follow a uniform convention: set to a **positive integer** (e.g. `
|
||||
| `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_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_asym64` (Q6_K/Q5_K only, keeps a real zero point at group 64), `q4_asym64_all` (also requantizes Q4_K), `q4_sym128` (Q6_K/Q5_K only), `q4_sym128_all` (Q4_K too, drops its per-group zero point), or `native` (no requantization). |
|
||||
| `GGML_OPENVINO_PROFILING` | Integer | `0` | `1` logs execution timing; `2` or higher also enables OpenVINO and OpenCL profiling. |
|
||||
| `GGML_OPENVINO_DEBUG_NODE` | String | `not set` | Add the named graph nodes as compiled outputs for debugging. Separate multiple names with commas. |
|
||||
| `GGML_OPENVINO_MOE_OP` | Boolean | `1` | On GPU, set to `0` to keep the unfused GatherMatmul path. |
|
||||
| `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. |
|
||||
| `GGML_OPENVINO_DEBUG_INPUT` | Boolean | `0` | Enable input debugging and print input tensor info. |
|
||||
@@ -737,8 +740,9 @@ Boolean flags follow a uniform convention: set to a **positive integer** (e.g. `
|
||||
| `GGML_OPENVINO_LOG_UNSUPPORTED_OPS`| Boolean | `0` | Log warning messages with tensor details and rejection reasons for any ops not supported by the OpenVINO backend. Emits at `WARN` level (requires `--log-verbosity >= 2`, enabled by default). |
|
||||
|
||||
> [!NOTE]
|
||||
> - `GGML_OPENVINO_STATEFUL_EXECUTION` is an **Experimental** feature to allow stateful execution for managing the KV cache internally inside the OpenVINO model, improving performance on CPUs and GPUs. Stateful execution is not effective on NPUs, and not all models currently support this feature. This feature is experimental and has been validated only with the llama-simple, llama-cli, llama-bench, and llama-run applications and is recommended to enable for the best performance. Other applications, such as llama-server and llama-perplexity, are not yet supported.
|
||||
> - `GGML_OPENVINO_STATEFUL_EXECUTION` is an **Experimental** feature for managing caches internally inside the OpenVINO model on CPUs and GPUs. Use a single slot (`-np 1`). KV caches retain the append-based state layout and sequence-axis optimization. Qwen3.5 adds recurrent cache states in their GGML layouts. Qwen3.5 requires an unsplit graph with model caching enabled and no recurrent rollback. A prompt starting at position 0 resets all states. State save/restore, sequence rewind, context shift, and mid-sequence graph replacement are unsupported. Stateful execution is not effective on NPUs.
|
||||
> - `GGML_OPENVINO_LOG_UNSUPPORTED_OPS` emits logs at `WARN` level (`GGML_LOG_WARN`), which requires application log verbosity `--log-verbosity >= 2` (or `-lv 2`).
|
||||
> - With `GGML_OPENVINO_COMPILED_MODEL_CACHE_ONLY=1`, use the same compilation settings as the export run. One directory can hold blobs for different models and settings; `GGML_OPENVINO_SPILL_DIR` does not affect the cache key and is ignored in cache-only mode. See [Compiled model cache](../../ggml/src/ggml-openvino/README.md) for the workflow and restrictions.
|
||||
|
||||
### Example Usage
|
||||
|
||||
|
||||
Reference in New Issue
Block a user