vLLM: Easy, Fast, and Cheap LLM Serving for Everyone
summary
vLLM serves open‑source LLMs with claims of production scale on NVIDIA/AMD GPUs, TPUs, Trainium and CPUs, plus OpenAI and Anthropic API compatibility and day‑zero model support. It uses PagedAttention, chunked prefill, multi‑LoRA, automatic prefix caching, a hybrid memory allocator and a KV connector for offload/compression; a nightly performance dashboard prevents regressions. Project metrics: ~60,000 stars and ~800 PRs merged per month. Distributed features include prefill/decode separation, decode context parallelism and prefix/load‑aware routing via LMD.
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chapters (7)
- 0:00Introduction to vLLM
- 3:00Using the vLLM API
- 4:40Model support and backend
- 6:33Accuracy and performance
- 8:57Technical innovations
- 13:04Distributed inference
- 18:03Q&A and conclusion
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