Andriy Oblivantsev
c56646e7e7
Build ggml-sycl from upstream llama.cpp (commit a5bb8ba4, matching ollama's vendored ggml) using Intel oneAPI 2025.1.1 in a multi-stage Docker build. Patch two ollama-specific API divergences via patch-sycl.py: added batch_size parameter to graph_compute, removed GGML_TENSOR_FLAG_COMPUTE skip-check that caused all compute nodes to be bypassed. Tested: gemma3:1b — 27/27 layers on GPU, 10.2 tok/s gen, 65.3 tok/s prompt eval. Co-authored-by: Cursor <cursoragent@cursor.com>
125 lines
5.0 KiB
Markdown
125 lines
5.0 KiB
Markdown
# Ollama for Intel GPU
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[](
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Run LLM models on your local Intel GPU using Ollama with Docker.
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Includes [Open WebUI](https://github.com/open-webui/open-webui) for a
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browser-based chat interface.
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## Screenshot
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## Prerequisites
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* Ubuntu 24.04 or newer
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* Docker and Docker Compose
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* Intel GPU (tested with Intel Core Ultra 7 155H integrated Arc Graphics — Meteor Lake)
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## Quick start
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```shell
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git clone https://github.com/mattcurf/ollama-intel-gpu
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cd ollama-intel-gpu
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docker compose up
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```
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Then open http://localhost:3000 in your browser.
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> If you have multiple GPUs (integrated + discrete), set
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> `ONEAPI_DEVICE_SELECTOR=level_zero:0` in the docker-compose environment
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> to select the intended device.
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## GPU backend: SYCL vs Vulkan
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Ollama can accelerate inference on Intel GPUs via two backends.
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This repo defaults to **SYCL** (built from upstream llama.cpp's ggml-sycl
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with Intel oneAPI) for best Intel GPU performance.
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### Performance comparison (llama-2-7b Q4_0, llama.cpp benchmarks)
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| Intel GPU | Vulkan tok/s | SYCL tok/s | SYCL advantage |
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|---------------------|-------------|------------|----------------|
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| MTL iGPU (155H) | ~8-11 | **16** | +45-100% |
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| ARL-H iGPU | ~10-12 | **17** | +40-70% |
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| Arc A770 | ~30-35 | **55** | +57-83% |
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| Flex 170 | ~30-35 | **50** | +43-67% |
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| Data Center Max 1550| — | **73** | — |
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### Why SYCL is faster
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* **oneDNN** — Intel's Deep Neural Network Library for optimized GEMM (matrix multiply)
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* **oneMKL** — Intel Math Kernel Library for optimized math operations
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* **Level-zero direct access** — lower-overhead GPU communication than Vulkan
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* **Intel-specific MUL_MAT kernels** — hand-tuned for MTL, ARL, Arc, Flex, PVC architectures
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* **FP16 compute path** — optional `GGML_SYCL_F16=ON` for faster compute
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* **Multi-GPU support** — `--split-mode layer` across multiple Intel GPUs
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### Why you might still use Vulkan
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* Shipped in official ollama releases — no build step required
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* Cross-vendor (Intel, AMD, NVIDIA)
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* Simpler deployment, smaller image
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To switch to Vulkan, see the `Dockerfile.vulkan` (if provided) or use the
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official ollama Docker image with `OLLAMA_VULKAN=1`.
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## Architecture
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The Docker image builds in two stages:
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1. **Build stage** (`intel/oneapi-basekit:2025.1.1`) — clones ollama v0.15.6
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source, fetches the matching `ggml-sycl` backend from upstream llama.cpp
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(commit `a5bb8ba4`, the exact ggml version ollama vendors), patches two
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ollama-specific API divergences (`batch_size` parameter, `GGML_TENSOR_FLAG_COMPUTE`
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removal), and compiles `libggml-sycl.so` with `icpx` + oneAPI.
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2. **Runtime stage** (`ubuntu:24.04`) — minimal image with Intel GPU drivers,
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the official ollama binary, and the SYCL runner + oneAPI runtime libraries.
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### Key components
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| Component | Source | Purpose |
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|-----------|--------|---------|
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| ollama binary | Official v0.15.6 release | Go server, API, model management |
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| ggml-sycl backend | llama.cpp @ `a5bb8ba4` | `libggml-sycl.so` compiled with oneAPI |
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| oneAPI runtime | Intel oneAPI 2025.1.1 | SYCL runtime, oneMKL, oneDNN, TBB |
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| GPU drivers | Intel compute-runtime 26.05 | Level-zero, IGC, OpenCL ICD |
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| patch-sycl.py | This repo | Patches ggml-sycl for ollama API compat |
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| Web UI | Open WebUI | Browser-based chat interface |
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## Configuration
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Key environment variables in `docker-compose.yml`:
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| Variable | Default | Description |
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|----------|---------|-------------|
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| `OLLAMA_HOST` | `0.0.0.0` | Listen address |
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| `OLLAMA_KEEP_ALIVE` | `24h` | Keep models loaded in memory |
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| `OLLAMA_NUM_PARALLEL` | `1` | Parallel request handling |
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| `OLLAMA_MAX_LOADED_MODELS` | `1` | Max models in memory |
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| `ONEAPI_DEVICE_SELECTOR` | `level_zero:0` | Select Intel GPU device |
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## How the SYCL build works
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Ollama intentionally excludes `ggml-sycl` from its vendored ggml source tree
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(it keeps the header `ggml-sycl.h` but not the implementation). This repo
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rebuilds it by:
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1. Cloning the ollama source (for the ggml build system and headers)
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2. Fetching `ggml-sycl` from the **exact llama.cpp commit** that ollama
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vendors (`a5bb8ba4`) to ensure ABI compatibility
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3. Applying two patches via `patch-sycl.py`:
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- **`graph_compute` signature**: ollama adds an `int batch_size` parameter
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- **`GGML_TENSOR_FLAG_COMPUTE`**: ollama removes this enum value, so the
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skip-check in the compute loop must be removed (otherwise ALL nodes
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get skipped, producing garbage output)
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4. Building with Intel oneAPI `icpx` compiler, linking oneMKL and oneDNN
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## References
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* [Intel GPU driver installation](https://dgpu-docs.intel.com/driver/client/overview.html)
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* [llama.cpp SYCL backend docs](https://github.com/ggml-org/llama.cpp/blob/master/docs/backend/SYCL.md)
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* [Intel oneAPI base toolkit](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit.html)
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* [ollama GitHub](https://github.com/ollama/ollama)
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* [Open WebUI](https://github.com/open-webui/open-webui)
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