vLLM

vLLM Skip to content You are viewing the latest developer preview docs. Click here to view docs for the latest stable release. Welcome to vLLM ¶ Easy, fast, and cheap LLM serving for everyone Star Watch Fork 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. Where to get started with vLLM depends on the type of user. If you are looking to: Run open-source models on vLLM, we recommend starting with the Quickstart Guide Build applications with vLLM, we recommend starting with the User Guide Build vLLM, we recommend starting with Developer Guide For information about the development of vLLM, see: Roadmap Releases 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 NVIDIA GPUs, AMD GPUs, and x86/ARM/PowerPC CPUs. Additionally, diverse hardware plugins such as Google TPUs, Intel Gaudi, IBM Spyre, Huawei Ascend, Rebellions NPU, Apple Silicon, MetaX GPU, and more. vLLM seamlessly supports 200+ model architectures on HuggingFace, including: Decoder-only LLMs (e.g., Llama, Qwen, Gemma) Mixture-of-Expert LLMs (e.g., Mixtral, DeepSeek-V3, Qwen-MoE, GPT-OSS) Hybrid attention and state-space models (e.g., Mamba, Qwen3.5) Multi-modal models (e.g., LLaVA, Qwen-VL, Pixtral) Embedding and retrieval models (e.g., E5-Mistral, GTE, ColBERT) Reward and classification models (e.g., Qwen-Math) Find the full list of supported models here . For more information, check out the following: vLLM announcing blog post (intro to PagedAttention) vLLM paper (SOSP 2023) How continuous batching enables 23x throughput in LLM inference while reducing p50 latency by Cade Daniel et al. vLLM Meetups April 9, 2026 Back to top