vLLM
A high-throughput LLM inference and serving library using PagedAttention for efficient memory management. vLLM is the leading open-source solution for self-hosted LLM serving, offering 10-24x higher throughput than naive implementations.
Implements
Concepts this tool claims to implement:
- Model Serving primary
Core purpose is serving LLMs efficiently. AsyncLLMEngine for production deployments. OpenAI-compatible API server.
- Inference primary
Optimized inference with PagedAttention, continuous batching, and tensor parallelism for multi-GPU.
- OpenAI API secondary
Built-in OpenAI-compatible API server. Drop-in replacement for OpenAI API with local models.
- Streaming secondary
Streaming support with Server-Sent Events. Compatible with OpenAI streaming format.
Integration Surfaces
Details
- Vendor
- vLLM Team (UC Berkeley origin)
- License
- Apache-2.0
- Runs On
- local, cloud
- Used By
- system
Links
Notes
vLLM's PagedAttention innovation dramatically improved LLM serving efficiency by managing KV-cache like virtual memory. It's the go-to choice for self-hosted LLM serving in production.