vLLM is a high-throughput, memory-efficient inference and serving engine for large language models. It provides an OpenAI-compatible API server and uses PagedAttention to manage attention key and value memory efficiently.
Project overview
The project addresses high-throughput and memory-efficient inference for large language models via PagedAttention and multiple parallelism strategies.
Project type
Model Runtime · Infrastructure
Deployment
Python environment
License
Apache-2.0
Best for
Developers, AI engineers, and operations teams needing high-throughput and memory-efficient inference for large language models.
Users requiring distributed inference with tensor, pipeline, data, expert, and context parallelism.
Key capabilities
Efficient management of attention key and value memory.
Tensor, pipeline, data, expert, and context parallelism for distributed inference.
Generation of structured outputs using xgrammar or guidance.
OpenAI-compatible API server, plus Anthropic Messages API and gRPC support.
Supports multimodal models like LLaVA, Qwen-VL, and Pixtral.
Limitations and risks
An end-to-end local-only data boundary or telemetry policy is not documented.
The availability of a managed service is not documented.
Getting started
Install the library via pip or uv using 'uv pip install vllm'.
Alternatives and comparisons
Provides LLM inference via a C/C++ implementation with integer quantization across various hardware.
Offers model deployment and optimization across platforms using machine learning compilation.
README: vLLM is a fast and easy-to-use library for LLM inference and serving.
GitHub project description: A high-throughput and memory-efficient inference and serving engine for LLMs
README: Easy, fast, and cheap LLM serving for everyone
README: 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…
README: Efficient management of attention key and value memory with [**PagedAttention**](https://blog.vllm.ai/2023/06/20/vllm.html)