Boosting multimodal inference performance by >10% with a single Python dictionary

A performance investigation of SGLang serving multimodal vision-language models found that its single-threaded scheduler was spending significant CPU time repeatedly reopening CUDA IPC shared-memory handles while processing image features, delaying GPU dispatches. Profiling with py-spy identified the costly PyTorch `_new_shared_cuda` calls within multimodal input hashing, where the same GPU memory pools were reconstructed for each tensor despite remaining unchanged. Developers replaced this repeated bookkeeping with a thread-safe Python dictionary cache that opens each IPC pool handle once and reuses the associated storage. In benchmarks of Qwen2.5-VL-3B-Instruct on an H100 GPU, the optimization raised throughput from 22.2 to 25.7 requests per second, reduced mean time to first token by 13.2%, lowered mean time per output token by 17.2%, and cut mean end-to-end latency by 10.6%, with tail latency improvements as well. The change applies to multimodal models using SGLang’s CUDA IPC transport, is included in SGLang v0.5.10, and can be enabled through the `SGLANG_USE_IPC_POOL_HANDLE_CACHE=1` environment variable.

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