SGLang Documentation
by LMSYS / sgl-project
Learn to serve LLMs fast in production with RadixAttention and prefix caching.
Overview
SGLang is a high-performance, open-source serving framework for large language and multimodal models, maintained by the sgl-project community under the non-profit LMSYS organization. Its documentation covers deploying and operating an inference server that emphasizes RadixAttention (automatic KV-cache reuse across requests via a radix tree), prefix caching, continuous batching, and multi-GPU/multi-node parallelism to deliver fast, high-throughput inference from a single GPU to distributed clusters. It exposes OpenAI- and Hugging Face-compatible APIs, supports a broad model set (Llama, Qwen, DeepSeek, and multimodal models) across NVIDIA, AMD, Intel, TPU, and other accelerators, and includes a structured-generation frontend language for constrained/JSON outputs and complex prompting. The docs walk through installation, launching a production-style server, quantization, structured outputs, and performance tuning — aimed at engineers optimizing self-hosted LLM serving.
At a Glance
- Topic
- Frameworks
- Level
- Advanced
- Format
- Documentation
- Cost
- Free
- Duration
- Docs, self-paced
- Provider
- LMSYS / sgl-project
- Hands-on
- Yes — code/exercises
- Certificate
- None
What You’ll Learn
- ✓Launch a production-style, OpenAI-compatible LLM inference server with SGLang
- ✓Use RadixAttention and prefix caching to reuse KV cache and raise throughput
- ✓Tune continuous batching, quantization, and multi-GPU parallelism for low latency
- ✓Serve Llama, Qwen, DeepSeek, and multimodal models across different accelerators
- ✓Generate structured/JSON-constrained outputs with SGLang's frontend language
Highlights
- •One of the fastest open-source LLM inference servers; powers very large deployments
- •RadixAttention KV-cache reuse is a distinguishing performance feature
- •OpenAI/Hugging Face API compatibility for easy integration
Who It’s For
Best For
- ✓ML/infra engineers self-hosting and optimizing LLM inference
- ✓Teams needing high-throughput, low-latency serving at scale
- ✓Engineers comparing serving frameworks (SGLang vs vLLM)
Prerequisites
- •Comfortable with Python, GPUs/CUDA, and command-line deployment
- •Understanding of LLM inference basics (KV cache, batching, tokenization)
FAQ
What is SGLang Documentation?
Official documentation for SGLang, an open-source, high-performance serving framework for large language and multimodal models from LMSYS. For AI engineers who need low-latency, high-throughput inference in production, with OpenAI-compatible APIs and support for Llama, Qwen, and DeepSeek.
Is SGLang Documentation free?
SGLang Documentation is free to access.
What level is SGLang Documentation for?
SGLang Documentation is aimed at a advanced audience. Recommended background: Comfortable with Python, GPUs/CUDA, and command-line deployment, Understanding of LLM inference basics (KV cache, batching, tokenization).
How long does SGLang Documentation take?
Expect roughly Docs, self-paced. Most learners work through it at their own pace.
What will I learn from SGLang Documentation?
You'll learn: Launch a production-style, OpenAI-compatible LLM inference server with SGLang; Use RadixAttention and prefix caching to reuse KV cache and raise throughput; Tune continuous batching, quantization, and multi-GPU parallelism for low latency; Serve Llama, Qwen, DeepSeek, and multimodal models across different accelerators; Generate structured/JSON-constrained outputs with SGLang's frontend language.