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  • SGLang Documentation home page
  • User Guide
  • Hardware
  • Cookbook
  • SGLang Diffusion
  • Quickstart
  • Installation
  • Examples

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GeezerVison is showing docs.sglang.io — Welcome to SGLang - SGLang Documentation

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Performance & Runtime

Designed for low-latency, high-throughput inference with RadixAttention, prefix caching, and multi-GPU parallelism.

Models & Ecosystem

Broad support for Llama, Qwen, DeepSeek, and more. Compatible with Hugging Face and OpenAI APIs.

Extensive Hardware Support

Native support across Hardware Platforms including NVIDIA, AMD, Intel Xeon, Google TPU, Ascend NPU, and Moore Threads MUSA accelerators.

Community & Training

Open-source with widespread adoption, powering 400k+ GPUs and integrated with major RL frameworks.
SGLang powers large-scale production deployments, generating trillions of tokens each day across more than 400,000 GPUs worldwide. It is hosted under the non-profit open-source organization LMSYS.

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Get Started

SGLang is an inference framework meant for production level serving. It is designed to deliver low-latency and high-throughput inference across a wide range of setups, from a single GPU to large distributed clusters.

Quickstart

Start a model server and send a request to verify it is running.

Examples

Stream replies, request tool calls, and process multiple prompts from Python.
For environment setup and other installation methods, see the Installation guide.

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News and latest blogs

XM-Sparse: Pushing the Throughput Frontier of Sparse Attention with Netpreme X-Mem\u2122
HiCache Across Multi-Parallelism and Distributed Serving
SGLang security update: improving request isolation
miniTE Technical Report: Stable and Efficient Reinforcement Learning with Native NVFP4
More Than DeepInfer: What SGLang Delivers
Announcing the SGLang AWS Trainium Plugin Beta

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Community and resources

  • Slack: Technical questions and development discussions.
  • Events: Meetups, workshops, and office hours | Developer meetings.
  • Updates: X | LinkedIn | LMSYS Blog.
  • Contribute: Contributor guide | Roadmap.
  • Release notes: Changes in each release.

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