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issue: GPU acceleration not working as expected on main branch #5703
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Originally created by @kaiiquetome on GitHub (Jul 4, 2025).
Check Existing Issues
Installation Method
Git Clone
Open WebUI Version
v0.5.16
Ollama Version (if applicable)
No response
Operating System
ubuntu
Browser (if applicable)
No response
Confirmation
README.md.Expected Behavior
When running the OpenWebUI container using the main branch with --gpus all, I expected GPU acceleration to be enabled and used, resulting in faster RAG processes, especially during document ingestion
Actual Behavior
Despite using the --gpus all flag, the performance remains slow when using the main branch image. It seems like the GPU is not being utilized properly. In contrast, using the older cuda branch (v0.5.16) significantly improves performance
Steps to Reproduce
Run the container using the main branch with GPU enabled:
docker run -d -p 3000:8080 --gpus all -v open-webui:/app/backend/data --name open-webui ghcr.io/open-webui/open-webui:main
Upload one or more documents to a knowledge collection.
Observe the performance during ingestion and processing.
Repeat the same steps using the cuda branch image.
Compare the speed and GPU usage with nvidia-smi
Logs & Screenshots
With cuda
With Main
Additional Information
No response
@tjbck commented on GitHub (Jul 4, 2025):
You need to use
:cudatags images, please read the docs and existing issues and discussions before creating a new one.@kaiiquetome commented on GitHub (Jul 7, 2025):
@tjbck I understand that the cuda branch should be used for GPU support, but it is currently outdated (v0.5.16). I've already started setting up my environment using the latest version (v0.6.15), which includes several recent integrations that are important for my setup. However, if I switch to the cuda branch, I lose access to these newer features.
@CoolSpot commented on GitHub (Jul 24, 2025):
@tjbck instructions on the main page say to use
:ollamatag image for GPU acceleration (which doesn't work):@CoolSpot commented on GitHub (Jul 25, 2025):
Solution is to build your own image with both CUDA and Ollama enabled: