๊ฐ€์ž… ํ›„ ์ดˆ๋Œ€ ๋งํฌ๋ฅผ ๊ณต์œ ํ•˜๋ฉด ๋™์˜์ƒ ์žฌ์ƒ ๋ฐ ์ดˆ๋Œ€ ๋ณด์ƒ์„ ๋ฐ›์„ ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค.

vLLM
@vllm_project
A high-throughput and memory-efficient inference and serving engine for LLMs. Join to discuss together with the community!
๊ฐ€์ž… March 2024
37 ํŒ”๋กœ์ž‰ ์ค‘    54.1K ํŒฌ
Beyond matching DeepInfer's performance, we also put vLLM to the test on @SemiAnalysis_ AgentX with real-world agentic workloads. At DeepSeek's off-peak pricing, vLLM delivers $38.96/GPU-hour revenue, with 954K tokens/s/GPU total throughput (cached+input+output) at 196 tokens/s P90 per user. This is the best DeepSeek-V4.1-Flash result on AgentX ever, powered by recent advancement in vLLM! Check out our blog for the full optimization story and how to reproduce the results ๐Ÿ‘‡ Enjoy the read.
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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, 7.9ร— our single-node result. Our engineering blog covers the work behind it:
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