The gist

vLLM is an open-source library for fast and easy large language model inference and serving. Originally from UC Berkeley's Sky Computing Lab, it addresses inefficient LLM deployment. By using techniques like PagedAttention for memory management and continuous request batching, vLLM achieves state-of-the-art serving throughput. It is now maintained by a large community of academic and corporate contributors and supports a wide range of hardware and model architectures.

What it does

Serves large language models with high-throughput inference.
Manages GPU memory efficiently with its PagedAttention algorithm.
Processes incoming requests using continuous batching and chunked prefill.
Supports numerous quantization methods including GPTQ, AWQ, and FP8.
Provides an OpenAI-compatible API server for easy integration.
Enables distributed inference with tensor, pipeline, and data parallelism.

How it works

As a Python library, users install vLLM and use it to run an inference server for a chosen Hugging Face model. It accepts API requests and streams back generated text. The core engine uses custom CUDA kernels, PagedAttention, and continuous batching to optimize GPU utilization and throughput. It is open-source, free to use, and can be self-hosted on supported hardware like NVIDIA and AMD GPUs, with experimental support for others.

Best for

vLLM is ideal for developers and MLOps engineers deploying LLMs in production environments who need to maximize serving throughput and minimize latency on their own infrastructure.