SGLang Documentation

SGLang is a fast serving framework for large language models and vision language models. It makes your interaction with models faster and more controllable by co-designing the backend runtime and frontend language. The core features include:

• Fast Backend Runtime: Provides efficient serving with RadixAttention for prefix caching, jump-forward constrained decoding, continuous batching, token attention (paged attention), tensor parallelism, FlashInfer kernels, chunked prefill, and quantization (INT4/FP8/AWQ/GPTQ).

• Flexible Frontend Language: Offers an intuitive interface for programming LLM applications, including chained generation calls, advanced prompting, control flow, multi-modal inputs, parallelism, and external interactions.

• Extensive Model Support: Supports a wide range of generative models (Llama 3, Gemma 2, Mistral, QWen, DeepSeek, LLaVA, etc.) and embedding models (e5-mistral), with easy extensibility for integrating new models.

• Active Community: SGLang is open-source and backed by an active community with industry adoption.

Getting Started

• Install SGLang

Backend Tutorial

• Backend: SGLang Runtime (SRT)

Frontend Tutorial

• Frontend: Structured Generation Language (SGLang)

References

• Sampling Parameters in SGLang Runtime
• Guide on Hyperparameter Tuning
• How to Support a New Model
• Contributor Guide
• Choices Methods in SGLang
• Benchmark and Profiling
• Troubleshooting