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ollama-intel-gpu
Docker app from SpaceInvaderOne's Repository
Overview
Readme
View on GitHubOllama for Intel Arc GPUs (IPEX-LLM)
Docker image that runs Ollama on Intel Arc GPUs using Intel IPEX-LLM. Drop-in replacement for the standard Nvidia-based Ollama container — same API on port 11434.
Supported GPUs: Intel Arc B580, A770, A750, A380, and other Arc series.
Install on Unraid
Via Community Applications (Recommended)
- Open the Apps tab in Unraid
- Search for ollama-intel-gpu
- Click Install
- Configure the settings:
- Model Storage — where models are saved on disk (default:
/mnt/user/appdata/ollama-intel-gpu) - Ollama API Port — default
11434 - GPU Device Selector — leave as
level_zero:0unless you have multiple Intel GPUs
- Model Storage — where models are saved on disk (default:
- Click Apply
The container will start with /dev/dri passed through automatically for GPU access.
Verify It's Working
Go to the Unraid Docker tab, click the container icon, and select Logs. You should see:
oneAPI device name: Intel(R) Graphics [0xe212]
discovered 1 Level-Zero memory modules
inference compute id=0 library=oneapi name="Intel(R) Graphics [0xe212]" total="15.9 GiB"
Pull a Model
Open the Unraid Docker tab, click the container icon, select Console, then run:
ollama run llama3.1:8b "Hello!"
Use with Open WebUI
If you're running Open WebUI on Unraid, point it at this container:
- Set OLLAMA_BASE_URL in Open WebUI to
http://<UNRAID_IP>:11434 - The
OLLAMA_ORIGINSvariable is set to*by default in the template to allow cross-origin requests
Install on Linux
Requirements
- Intel Arc GPU (B580, A770, A750, or other Arc series)
/dev/dridevice nodes available on the host- Host kernel must have the i915 driver loaded
Docker Run
docker run -d \
--name ollama-intel-gpu \
--device=/dev/dri \
-p 11434:11434 \
-v ollama-data:/root/.ollama \
spaceinvaderone/ollama-intel-gpu:latest
Then pull and run a model:
docker exec -it ollama-intel-gpu ollama run llama3.1:8b "Hello!"
Verify GPU Detection
docker logs ollama-intel-gpu
Look for oneAPI device name: Intel(R) Graphics and library=oneapi in the output.
Environment Variables
| Variable | Default | Description |
|---|---|---|
OLLAMA_HOST |
0.0.0.0:11434 |
API listen address |
ONEAPI_DEVICE_SELECTOR |
level_zero:0 |
Which Intel GPU to use |
OLLAMA_NUM_PARALLEL |
1 |
Parallel requests (keep at 1 for 12 GB cards) |
OLLAMA_NUM_CTX |
4096 |
Context window size in tokens |
OLLAMA_KEEP_ALIVE |
10m |
How long to keep model in VRAM (-1 = forever) |
OLLAMA_ORIGINS |
(unset) | Set to * for Open WebUI / CORS access |
Testing the API
# Health check
curl http://localhost:11434/
# List models
curl http://localhost:11434/api/tags
# Generate
curl http://localhost:11434/api/generate -d '{
"model": "llama3.1:8b",
"prompt": "Hello!",
"stream": false
}'
What's Inside
- Base: Ubuntu 24.04
- IPEX-LLM: v2.3.0 nightly (Ollama portable build optimised for Intel GPUs)
- Intel GPU drivers: IGC v2.8.3, Compute Runtime 25.09.32961.7, Level-Zero Loader v1.21.9
Install ollama-intel-gpu on Unraid in a few clicks.
Find ollama-intel-gpu in Community Apps on your Unraid server, review the template, and click Install. Unraid handles the Docker app or plugin setup from the published template.
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spaceinvaderone/ollama-intel-gpuRuntime arguments
- Web UI
http://[IP]:[PORT:11434]/- Network
bridge- Shell
bash- Privileged
- false
- Extra Params
--device=/dev/dri
Template configuration
Path on the host for persistent model storage. Models are large (4-20 GB each).
- Target
- /root/.ollama
- Default
- /mnt/user/appdata/ollama-intel-gpu
- Value
- /mnt/user/appdata/ollama-intel-gpu
Port for the Ollama API.
- Target
- 11434
- Default
- 11434
- Value
- 11434
Allowed origins for CORS. Set to * to allow Open WebUI and other frontends to connect.
- Default
- *
- Value
- *
Select which Intel GPU to use. Use level_zero:0 for the first GPU. Change only if you have multiple Intel GPUs.
- Default
- level_zero:0
- Value
- level_zero:0
Number of parallel inference requests. Set to 1 for 12 GB VRAM cards (B580). Increase only if you have more VRAM.
- Default
- 1
- Value
- 1
Context window size in tokens. Larger values use more VRAM. Default 4096 is a good balance for 12 GB cards.
- Default
- 4096
- Value
- 4096
How long to keep a model loaded in VRAM after the last request. Use 5m for 5 minutes, -1 for forever, 0 to unload immediately.
- Default
- 5m
- Value
- 5m