vLLM
High-throughput and memory-efficient LLM serving engine with PagedAttention.
Why consider vLLM?
vLLM is a high-throughput, low-latency LLM serving engine powered by PagedAttention. It optimizes GPU KV cache utilization and provides OpenAI-compatible API endpoints for scalable production inference.
Learn vLLM by building
Practical setup notes, real use cases, and copy-ready examples in one focused guide.
In this guide3 sections
Overview of vLLM
vLLM is the leading open-source serving engine for large language models in enterprise production. According to the official repository at https://raw.githubusercontent.com/vllm-project/vllm/main/README.md, vLLM is a fast and easy-to-use library for LLM inference and serving. It delivers industry-leading throughput by fundamentally redesigning how GPU memory is allocated during generative decoding.
The engine delivers exceptional inference performance across generative architectures. As documented in the official readme at https://raw.githubusercontent.com/vllm-project/vllm/main/README.md, the framework achieves State-of-the-art serving throughput across standard evaluation benchmarks.
Installation and Quick Start
Installing vLLM is done directly via pip on CUDA-compatible systems. As specified in the official repository installation guide at https://raw.githubusercontent.com/vllm-project/vllm/main/README.md, run pip install vllm:
pip install vllmLicensing and Governance
vLLM is maintained by the open-source community under the Apache-2.0 license. According to the official license file at https://raw.githubusercontent.com/vllm-project/vllm/main/LICENSE, the codebase is licensed under Apache License Version 2.0. This permissive license allows seamless integration into commercial cloud platforms and self-hosted infrastructure.
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