SGLang
High-performance serving framework for LLMs and multimodal models with advanced optimizations and structured generation support.
28.9K+
GitHub Stars
4.8/5
Rating
2023
Founded
Overview
SGLang is a fast serving framework designed for large language models and vision language models, featuring advanced optimizations like RadixAttention for KV cache reuse and efficient structured generation. Developed by researchers from UC Berkeley, it delivers up to 25x performance improvements on cutting-edge hardware and has become popular for its balance of speed, flexibility, and ease of use.
The Verdict
Who Should Use SGLang?
Best For
- High-throughput production LLM serving at scale
- Applications requiring structured output generation
- Teams needing advanced KV cache optimizations
- Multimodal applications (text, image, video)
Not Ideal For
- Beginners seeking the simplest setup experience
- Use cases not requiring maximum performance
What's Great
- Inference framework with a fast execution runtime
- RadixAttention for efficient KV cache reuse
- Native support for structured generation (JSON, regex)
- Multimodal support (text, image, video, audio)
- Day-0 support for latest open models
Watch Out For
- Rapidly evolving with frequent breaking changes
- Documentation lags behind feature development
- Smaller ecosystem compared to vLLM
Pricing
View all features & details
Key Features
- RadixAttention for automatic KV cache reuse
- Constrained decoding for structured outputs
- Multimodal model support (LLaVA, Qwen-VL, etc.)
- Tensor parallelism and pipeline parallelism
- OpenAI-compatible API server
- 25x faster on NVIDIA GB300 NVL72
Platforms
- NVIDIA GPUs (CUDA)
- AMD GPUs (ROCm)
- Docker deployment
- Kubernetes
How It Compares
| Feature | SGLang | vLLM | TGI |
|---|---|---|---|
| Performance | Excellent | Excellent | Very Good |
| Structured Gen | Native | Limited | Basic |
| Multimodal | Strong | Good | Basic |
| Best For | Advanced features | Stability | HuggingFace |
User Reviews
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