vLLM — Agents-OSS

Overview

Paged attention and continuous batching let one GPU serve far more concurrent requests than a naive loop, which matters because agents are unusually chatty. The OpenAI-compatible endpoint means most agent frameworks point at it with a base-URL change. Expect real operational work around GPU memory sizing and model loading times.

What can you do with vLLM?

Serve more concurrent agents per GPU — Paged attention and continuous batching keep many requests in flight on a single card instead of running them through a naive one-at-a-time loop.
Point existing clients at it — Most agent frameworks switch over to the OpenAI-compatible API server with a base-URL change and no client rewrite, and the same server also speaks the Anthropic Messages API and gRPC.
Run it on the hardware you have — uv pip install vllm installs the server, which runs on NVIDIA, AMD and Intel GPUs and on x86/ARM/PowerPC CPUs, with hardware plugins covering Google TPUs, Intel Gaudi, Huawei Ascend and Apple Silicon.
Shrink a model to fit the card — Quantized weights are served directly — FP8, MXFP8/MXFP4, NVFP4, INT8, INT4, GPTQ/AWQ, GGUF and compressed-tensors are all on the supported list — so the memory a model needs is something you choose rather than a fixed property of the checkpoint.
Constrain output and parse tool calls — Structured output generation runs through xgrammar or guidance, and the server ships tool-calling and reasoning parsers for the models that emit them.

Documentation

Reproduced from the vllm-project/vllm README, published under Apache-2.0. Read the original ↗

🔥 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 NVIDIA GPUs, AMD GPUs, Intel 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 Hugging Face, 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.

Getting Started

Install vLLM with uv (recommended) or pip:

uv pip install vllm

Or build from source for development.

Visit our documentation to learn more.

Contact Us

For technical questions and feature requests, please use GitHub Issues
For discussing with fellow users, please use the vLLM Forum
For coordinating contributions and development, please use Slack
For security disclosures, please use GitHub’s Security Advisories feature
For collaborations and partnerships, please contact us at collaboration@vllm.ai

Media Kit

If you wish to use vLLM’s logo, please refer to our media kit repo