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

This post details an optimization in SGLang's scheduler for multimodal inference. By profiling with `py-spy`, the team identified that expensive book-keeping around shared GPU memory handles during multimodal request processing was a bottleneck. They implemented a Python dictionary cache for these handles, reducing host overhead and improving throughput by 16.2% and reducing mean end-to-end latency by 10.6% on a Qwen2.5-VL-3B-Instruct workload. This optimization was upstreamed to SGLang.