vLLM provides high-throughput, memory-efficient inference and serving for large language models. It targets developers who need to serve models locally or in self-hosted environments with efficient attention memory management.
Project overview
vLLM provides high-throughput, memory-efficient inference and serving for large language models. It targets developers who need to serve models locally or in self-hosted environments with efficient attention memory management.
Project type
Model Runtime · Infrastructure
Deployment
Python environment
License
Apache-2.0
Best for
Developers and AI engineers who need to run a high-throughput model serving library on their own infrastructure.
Key capabilities
Provides efficient management of attention key and value memory to optimize resource usage during inference.
Supports tensor, pipeline, data, expert, and context parallelism for distributed inference.
Generates structured outputs using xgrammar or guidance.
Includes an OpenAI-compatible API server, alongside Anthropic Messages API and gRPC support.
Supports multimodal models such as LLaVA, Qwen-VL, and Pixtral.
Limitations and risks
Minimum hardware requirements, operating systems, and model requirements are currently unknown.
Telemetry and cost dependency configurations are not documented.
Getting started
The setup is considered easy and can be installed via pip or uv using the command 'uv pip install vllm'.
Evidence and sources
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)