Developer Frameworks & Infrastructure
vLLM
High-throughput and memory-efficient LLM serving engine
Open sourceRuns on your deviceEditors pick
GitHub stars
96.9K
96,943 stars
Community forks
24.8K
Open collaboration
License
Apache-2.0
Read the full license
Platforms
macOS - Windows - Linux
Also Web
Last activity
Nov 15
Repository push date
Best for
Production open-weights LLM serving
Host models like Llama 3 or Qwen 2.5 with enterprise-grade throughput and OpenAI-compatible API endpoints.
Pricing checked Sep 2026
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About vLLM
vLLM is an open-source serving engine engineered for high-throughput LLM inference and deployment. Built around PagedAttention memory management, vLLM drastically reduces memory fragmentation in key-value (KV) caches, enabling significantly higher batch sizes and throughput compared to traditional HuggingFace Transformers pipelines.
It provides seamless integration with OpenAI-compatible HTTP endpoints, quantization backends (AWQ, GPTQ, FP8), and distributed tensor parallelism for multi-GPU setups. vLLM has become the industry benchmark for hosting open-weights models like Llama 3, Qwen 2.5, and Mistral in production environments.
- State-of-the-art inference throughput powered by PagedAttention memory management
- Native OpenAI-compatible HTTP server for drop-in replacement of commercial endpoints
- Extensive support for AWQ, GPTQ, and FP8 quantization algorithms
- Active open-source ecosystem with rapid adoption of new model architectures
- Requires dedicated GPU hardware and CUDA environment management
- Setup and tuning for multi-node distributed serving can be complex
- Memory usage needs careful benchmarking when serving extremely long context windows
pip install vllmHigh-throughput and memory-efficient LLM serving engine. Installs via pip for local or cluster deployment.
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