Ollama for Intel GPU

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Run LLM models on your local Intel GPU using Ollama with Docker. Includes Open WebUI for a browser-based chat interface.

Screenshot

Prerequisites

• Ubuntu 24.04 or newer
• Docker and Docker Compose
• Intel GPU (tested with Intel Core Ultra 7 155H integrated Arc Graphics — Meteor Lake)

Quick start

git clone https://github.com/mattcurf/ollama-intel-gpu
cd ollama-intel-gpu
docker compose up

Then open http://localhost:3000 in your browser.

If you have multiple GPUs (integrated + discrete), set ONEAPI_DEVICE_SELECTOR=level_zero:0 in the docker-compose environment to select the intended device.

GPU backend: SYCL vs Vulkan

Ollama can accelerate inference on Intel GPUs via two backends. This repo defaults to SYCL (built from upstream llama.cpp's ggml-sycl with Intel oneAPI) for best Intel GPU performance.

Performance comparison (llama-2-7b Q4_0, llama.cpp benchmarks)

Intel GPU	Vulkan tok/s	SYCL tok/s	SYCL advantage
MTL iGPU (155H)	~8-11	16	+45-100%
ARL-H iGPU	~10-12	17	+40-70%
Arc A770	~30-35	55	+57-83%
Flex 170	~30-35	50	+43-67%
Data Center Max 1550	—	73	—

Why SYCL is faster

• oneDNN — Intel's Deep Neural Network Library for optimized GEMM (matrix multiply)
• oneMKL — Intel Math Kernel Library for optimized math operations
• Level-zero direct access — lower-overhead GPU communication than Vulkan
• Intel-specific MUL_MAT kernels — hand-tuned for MTL, ARL, Arc, Flex, PVC architectures
• FP16 compute path — optional GGML_SYCL_F16=ON for faster compute
• Multi-GPU support — --split-mode layer across multiple Intel GPUs

Why you might still use Vulkan

• Shipped in official ollama releases — no build step required
• Cross-vendor (Intel, AMD, NVIDIA)
• Simpler deployment, smaller image

To switch to Vulkan, see the Dockerfile.vulkan (if provided) or use the official ollama Docker image with OLLAMA_VULKAN=1.

Architecture

The Docker image builds in two stages:

1. Build stage (intel/oneapi-basekit:2025.1.1) — clones ollama v0.15.6 source, fetches the matching ggml-sycl backend from upstream llama.cpp (commit a5bb8ba4, the exact ggml version ollama vendors), patches two ollama-specific API divergences (batch_size parameter, GGML_TENSOR_FLAG_COMPUTE removal), and compiles libggml-sycl.so with icpx + oneAPI.
2. Runtime stage (ubuntu:24.04) — minimal image with Intel GPU drivers, the official ollama binary, and the SYCL runner + oneAPI runtime libraries.

Key components

Component	Source	Purpose
ollama binary	Official v0.15.6 release	Go server, API, model management
ggml-sycl backend	llama.cpp @ a5bb8ba4	libggml-sycl.so compiled with oneAPI
oneAPI runtime	Intel oneAPI 2025.1.1	SYCL runtime, oneMKL, oneDNN, TBB
GPU drivers	Intel compute-runtime 26.05	Level-zero, IGC, OpenCL ICD
patch-sycl.py	This repo	Patches ggml-sycl for ollama API compat
Web UI	Open WebUI	Browser-based chat interface

Configuration

Key environment variables in docker-compose.yml:

Variable	Default	Description
OLLAMA_HOST	0.0.0.0	Listen address
OLLAMA_KEEP_ALIVE	24h	Keep models loaded in memory
OLLAMA_NUM_PARALLEL	1	Parallel request handling
OLLAMA_MAX_LOADED_MODELS	1	Max models in memory
ONEAPI_DEVICE_SELECTOR	level_zero:0	Select Intel GPU device

How the SYCL build works

Ollama intentionally excludes ggml-sycl from its vendored ggml source tree (it keeps the header ggml-sycl.h but not the implementation). This repo rebuilds it by:

1. Cloning the ollama source (for the ggml build system and headers)
2. Fetching ggml-sycl from the exact llama.cpp commit that ollama vendors (a5bb8ba4) to ensure ABI compatibility
3. Applying two patches via patch-sycl.py:
   • graph_compute signature: ollama adds an int batch_size parameter
   • GGML_TENSOR_FLAG_COMPUTE: ollama removes this enum value, so the skip-check in the compute loop must be removed (otherwise ALL nodes get skipped, producing garbage output)
4. Building with Intel oneAPI icpx compiler, linking oneMKL and oneDNN

References

• Intel GPU driver installation
• llama.cpp SYCL backend docs
• Intel oneAPI base toolkit
• ollama GitHub
• Open WebUI