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38,037 stars · Python

How to use SGLang: what you can do with it

What is SGLang? SGLang is an open-source inference framework for large language, vision-language and diffusion models, optimized for agentic workloads, large-scale serving and RL rollouts. It runs on NVIDIA, AMD, Google TPU, Huawei Ascend and other hardware, ships SGLang Diffusion for image and video generation, and plugs into RL frameworks such as verl and slime for rollout generation. Launch commands per model and hardware live in its Cookbook.

How do I use it? Pull the image: docker pull lmsysorg/sglang:latest, or install with uv pip install --prerelease=allow sglang; then open the Cookbook and choose your model and hardware to get a launch command; launch the model and follow the Quickstart to send your first request. Full steps below.

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What do you want to do?

3 use cases

Quick start with SGLang

  1. Check you have what it needs: A supported GPU or accelerator (NVIDIA, AMD, TPU and others); Docker, or a Python environment with uv.
  2. Open sgl-project/sglang on GitHub and read the README and license.
  3. Pull the image: docker pull lmsysorg/sglang:latest, or install with uv pip install --prerelease=allow sglang
  4. Open the Cookbook and choose your model and hardware to get a launch command
  5. Launch the model and follow the Quickstart to send your first request
  6. Check the hardware platform guide for your GPU before tuning

These steps come from our top use case, Serve an open model on your own GPUs, which also has a copy-paste agent prompt.

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SGLang alternatives · Best AI repos for Software and DevOps

About this repo

  • Who it is forML engineers and teams who serve open models on their own GPUs and need high throughput.
  • Why it is hot38k stars, talked about in 2 AI sources we track
  • LanguagePython
  • Sourcegithub.com/sgl-project/sglang · Back to Hot repos

Requirements

  • A supported GPU or accelerator (NVIDIA, AMD, TPU and others)
  • Docker, or a Python environment with uv

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Questions about SGLang

What is SGLang?

SGLang is an open-source inference framework for large language, vision-language and diffusion models, optimized for agentic workloads, large-scale serving and RL rollouts. It runs on NVIDIA, AMD, Google TPU, Huawei Ascend and other hardware, ships SGLang Diffusion for image and video generation, and plugs into RL frameworks such as verl and slime for rollout generation. Launch commands per model and hardware live in its Cookbook. It is an open-source project on GitHub: https://github.com/sgl-project/sglang.

How do I use SGLang?

Pull the image: docker pull lmsysorg/sglang:latest, or install with uv pip install --prerelease=allow sglang; then open the Cookbook and choose your model and hardware to get a launch command; launch the model and follow the Quickstart to send your first request. The use cases on this page give the full steps and a copy-paste agent prompt for each job.

Who is SGLang for?

ML engineers and teams who serve open models on their own GPUs and need high throughput.

What can I do with SGLang?

3 use cases so far, for example: Serve an open model on your own GPUs; Generate images and video with SGLang Diffusion; Use SGLang as the rollout engine for RL post-training. Each has steps and a copy-paste agent prompt.

What do I need to run SGLang?

A supported GPU or accelerator (NVIDIA, AMD, TPU and others); Docker, or a Python environment with uv.

What are alternatives to SGLang?

Other hot AI repos in Software and DevOps: Deep Infer, Harness2, Orca, Opencode, Graphify.

Is SGLang safe to run?

Deduped does not own, audit or vouch for it. Read the README and license, run it on your own systems first and review every command before you run it.