What is vllm?

vllm is a personal fork of vLLM, an open source library that makes it fast and cheap to run and serve large language models. The original vLLM project started at UC Berkeley and has grown into a widely used piece of AI infrastructure with contributions from thousands of people across many companies and universities. This fork carries the same README and features as the original project. The core idea behind vLLM is serving many requests to a large language model at once without wasting memory or slowing everything down. It manages the memory used to remember previous conversation context in a more efficient way than older approaches, batches incoming requests together on the fly, and can start generating a reply before it has finished reading the entire prompt. It supports many ways of shrinking a model to use less memory and run faster, and includes fast, optimized building blocks for the heavy math the model performs. It works with over 200 different model architectures available on Hugging Face, covering standard chat models, mixture of expert models, models that mix attention with newer state space approaches, and models that also handle images alongside text. It also supports models used for search and classification tasks, not just chat. Practically, it can be installed with a single command using the uv or pip Python package tools, and it exposes an interface compatible with OpenAI's API along with support for Anthropic's API style, so existing tools built for those services can often point at vLLM with little change. It runs on NVIDIA and AMD graphics cards as well as regular CPUs, and has plugins for other specialized AI hardware from several manufacturers. This project is aimed at developers and infrastructure engineers who need to deploy large language models themselves rather than call a hosted API, and it assumes comfort with Python and command line tools. It is an actively maintained open source project that welcomes outside contributions.