vLLM, a high-throughput LLM inference and serving engine
Original titlevllm-project/vllm
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Summary
vLLM is a fast and memory-efficient inference engine for large language models originally developed at UC Berkeley. Maintained by over 2,000 contributors across academic institutions and companies, it offers state-of-the-art serving throughput and efficient attention management with PagedAttention.
Original excerpt
A high-throughput and memory-efficient inference and serving engine for LLMs https://vllm.ai Easy, fast, and cheap LLM serving for everyone | Documentation | Blog | Paper | Twitter/X | User Forum | Developer Slack | 🔥 We have built a vLLM website to help you get started with vLLM. Please visit vllm.ai to learn more. For events, please visit vllm.ai/events to join us. About vLLM is a fast and easy-to-use library for LLM inference and serving. Originally developed in the Sky Computing Lab at UC Berkeley, vLLM has grown into one of the most active open-source AI projects built and maintained by a diverse community of many dozens of academic institutions and companies from over 2000 contributors. vLLM is fast with: State-of-the-art serving throughput Efficient management of attention key and value memory with PagedAttention Continuous batching of incoming requests, chunked prefill, prefix caching Fast and flexible model execution with piecewise and full CUDA/HIP graphs Quantization: FP8, MXFP8/MXFP4, NVFP4, INT8, INT4, GPTQ/AWQ, GGUF, compressed-tensors, ModelOpt, TorchAO, and more Optimized attention kernels including FlashAttention, FlashInfer, TRTLLM-GEN, FlashMLA, and Triton Optimized GEMM/MoE kernels for various precisions using CUTLASS, TRTLLM-GEN, CuTeDSL Speculative decoding including n-gram, suffix, EAGLE, DFlash Automatic kernel generation and graph-level transformations using torch.compile Disaggregated prefill, decode, and encode vLLM is flexible and easy to use with: Seamless integration with popular Hugging Face models High-throughput serving with various decoding algorithms, including parallel sampling, beam search, and more Tensor, pipeline, data, expert, and context parallelism for distributed inference Streaming outputs Generation of structured outputs using xgrammar or guidance Tool calling and reasoning parsers OpenAI-compatible API server, plus Anthropic Messages API and gRPC support Efficient multi-LoRA support for dense and MoE layers Support for