vLLM v0.6.0 发布,吞吐量提升最高 2.7x、TPOT 最多降低 5x
vLLM 发布 v0.6.0,相比 v0.5.3 吞吐量提升 1.8-2.7x,其中 Llama 8B 的吞吐量提升 2.7x、TPOT 加快 5x,Llama 70B 的吞吐量提升 1.8x、TPOT 减少 2x。主要优化包括拆分 API server 与推理引擎、多步批量调度、异步输出处理、对象缓存和非阻塞数据传输。多步调度目前会造成 token 间延迟波动,并可能在低请求率下提高 TTFT。

has highest throughput on ShareGPT and Decode-heavy dataset (though the throughput is only marginally higher than TensorRT-LLM), but the throughput of vLLM is lower on Prefill-heavy dataset. Related Posts DeepSeek-V4.1-Flash on vLLM: 5x Agentic Throughput Since Day 0 Within three weeks of release, vLLM made DeepSeek-V4.1-Flash 1.9x faster at low concurrency and lifted its throughput 5x on SemiAnalysis AgentX, with SWA bounded replay, CUDA graphs, DeepSeek's new kernels, and vLLM kernel fusions.
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