It seems likely that the issue you’re experiencing with both --cpu-offload-gb and --swap-space options on a T4 GPU is related to the handling of memory and data types between the CPU and GPU. The T4 GPU, with its compute capability of 7.5, may have limitations when it comes to certain operations or optimizations that require higher compute capabilities, such as bfloat16 operations.
Here are a few steps you can take to potentially resolve the issue:
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Check Data Types: Ensure that the data types used in your model are compatible with the T4 GPU. You might need to explicitly set the data type to
float16orint8ifbfloat16is causing issues. -
Update Drivers and Libraries: Make sure that your Nvidia drivers and CUDA libraries are up to date. Sometimes, compatibility issues can be resolved with the latest updates.
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Use Tensor Parallelism: If the model is too large, consider using tensor parallelism to distribute the model across multiple GPUs. This can help manage memory usage more effectively.
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Reduce Model Size: If possible, try using a smaller version of the model or reducing the context length (
max_model_len) to decrease memory usage. -
Experiment with Different Configurations: Try different combinations of
--cpu-offload-gb,--swap-space, and--gpu-memory-utilizationto find a configuration that works for your setup.
If these steps do not resolve the issue, it might be helpful to check the vLLM GitHub issues page for any updates or similar issues reported by other users. Additionally, you can consider reaching out to the vLLM community for further assistance.
Would you like more detailed guidance on any of these steps?
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