vLLM(@vllm_project):Scaled out with P/D disaggregation and Wide-EP on a GB300 NVL72 rack, vLLM serves V4.1-Flash on SemiAnalysis AgentX at 954K tokens/s per GPU with 196 tokens/s P90 per user, 7.9× our single-node result. Our engineering blog covers the work behind it:

vLLM

@vllm_project

A high-throughput and memory-efficient inference and serving engine for LLMs. Join to discuss together with the community!

가입 March 2024

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vLLM@vllm_project

2026.10.24 17:59

Scaled out with P/D disaggregation and Wide-EP on a GB300 NVL72 rack, vLLM serves V4.1-Flash on SemiAnalysis AgentX at 954K tokens/s per GPU with 196 tokens/s P90 per user, 7.9× our single-node result. Our engineering blog covers the work behind it: