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CUDA IPC

1 post about CUDA IPC. Every summary links to the original.

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Boosting multimodal inference performance by >10% with a single Python dictionary
Multimodal vision-language models pose new challenges for inference engines like SGLang. Profiling with py-spy revealed that the scheduler’s process_input_requests consumed about 13% of CPU time, with a quarter of that spent on repeated calls to torch.UntypedStorage._new_shared_cuda for CUDA IPC handle book-keeping. The solution was a simple Python dictionary cache for pool handles, eliminating redundant opens. On a Qwen2.5-VL-3B-Instruct benchmark with a single H100, throughput increased 16.2% from 22.2 to 25.7 requests per second, mean time-to-first-token dropped 13.2%, and mean time-per-output-token dropped 17.2%. The improvement is merged in SGLang v0.5.10 and applies to any multimodal model using SGLang’s CUDA IPC transport.

The best engineering reads, once a week.

Topic

1 post about CUDA IPC. Every summary links to the original.

Paid picks we use ourselves. Links carry a referral code — a subscription through one earns us a commission at no extra cost to you.

Newest first

Modal
Boosting multimodal inference performance by >10% with a single Python dictionary
Multimodal vision-language models pose new challenges for inference engines like SGLang. Profiling with py-spy revealed that the scheduler’s process_input_requests consumed about 13% of CPU time, with a quarter of that spent on repeated calls to torch.UntypedStorage._new_shared_cuda for CUDA IPC handle book-keeping. The solution was a simple Python dictionary cache for pool handles, eliminating redundant opens. On a Qwen2.5-VL-3B-Instruct benchmark with a single H100, throughput increased 16.2% from 22.2 to 25.7 requests per second, mean time-to-first-token dropped 13.2%, and mean time-per-output-token dropped 17.2%. The improvement is merged in SGLang v0.5.10 and applies to any multimodal model using SGLang’s CUDA IPC transport.

The best engineering reads, once a week.