vLLM is an open source library for fast and memory-efficient LLM inference, developed at UC Berkeley. Its core innovation is PagedAttention, a memory management technique that treats the KV cache like virtual memory pages, dramatically reducing memory fragmentation and allowing more concurrent requests on the same GPU hardware compared to naive approaches. Deploying vLLM on a single A100 can serve Llama 3 70B at throughputs that previously required multiple GPUs. It supports tensor parallelism across multiple GPUs, continuous batching (processes requests as they arrive rather than waiting for batch fills), and streaming responses. The server exposes an OpenAI-compatible REST API, making migration from the OpenAI API a drop-in replacement. vLLM is the most widely adopted open source LLM serving framework among companies that self-host large models. It powers inference at major AI companies including Anyscale, Mistral AI, and many enterprise deployments. With over 25,000 GitHub stars, it's the de facto standard for production LLM serving.

What the community says

vLLM is the gold standard discussion point whenever production LLM serving comes up in ML engineering communities. The PagedAttention paper sparked significant academic and industry attention, and practitioners consistently validate the throughput improvements in real deployments. Machine learning engineers appreciate the active development pace and extensive model support. Common challenges include complex GPU driver requirements, debugging distributed tensor-parallel setups, and the fact that it's primarily designed for data center-class GPUs rather than consumer hardware.

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