Cuda out of Memory
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- Dominant language
- Python
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Description
Hi guys,
I am trying to run Qwen 3B Instruct model on a GPU with 24 GB VRAM, but when the VLLM is creating CUDA graphs, it goes out of memory. It seems like we can set the gpu_memory_utilization config of VLLM to be around 0.7 to free up the GPU memory. Is there a way to pass this flag during backend initialization? Another interesting issue is that this happens when I run it on Databricks with a GPU of 24 GB VRAM, but when I run it on a local machine with RTX 3090, it runs fine. Not sure what the cause is. Thank you.
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First steps
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Research direction
Start by locating the backend initialization that creates the vLLM engine and check how gpu_memory_utilization can be passed. Compare the Qwen 3B Instruct run on Databricks with a 24 GB GPU against the RTX 3090 case. Done means the configuration can be supplied during initialization and the reported CUDA graph out-of-memory failure is resolved or clearly explained.
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Assessment
- Tech stack
- python
- Domain
- ai, machine-learning
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 25/100