LLMs & AI Infra LLM Serving stable

SGLang

Fast serving framework for LLMs and vision-language models

8.0K stars 150 contributors Since 2024
Website → GitHub

High-performance serving framework using RadixAttention for prefix caching, compressed finite state machines for structured output, and multi-modal support.

License
Apache-2.0
Min RAM
8 GB
Min CPUs
4 cores
Scaling
horizontal
Complexity
advanced
Performance
enterprise grade
Self-hostable
K8s native
Offline
Pricing
fully free
Docs quality
good
Vendor lock-in
none

Use cases

  • Structured JSON output from LLMs at scale
  • Vision-language model serving
  • Prefix caching for repeated prompt patterns

Anti-patterns / when NOT to use

  • Newer project — less battle-tested
  • Smaller community than vLLM
  • Documentation still maturing

Integrates with

Replaces / alternatives to

  • Proprietary inference endpoints

Technical specs

Language
Python
API type
REST
Protocols
HTTP
Deployment
pipdocker
SDKs
python

Community

GitHub stars 8.0K
Contributors 150
Commit frequency daily
Plugin ecosystem none
Backing UC Berkeley
Funding vc_backed

Release

Latest version
Last release
Since 2024

Best fit

Team size
smallmediumenterprise
Industries
general

Tags

  • llm-serving
  • radix-attention
  • structured-output
  • multi-modal
  • prefix-caching
  • openai-compatible