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sgl-project/sglang

SGLang is a high-performance serving framework for large language models and multimodal models.

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README.md

# SGLang: Fast inference for LLMs and multimodal models

<a href="https://docs.sglang.io/">Docs</a> |
 <a href="https://cookbook.sglang.io/">Cookbook</a> |
 <a href="https://www.sglang.io/">Website</a> |
 <a href="https://lmsys.org/blog/">Blog</a> |
 <a href="https://slack.sglang.io/">Slack</a>

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. [SGLang Diffusion](https://docs.sglang.io/docs/sglang-diffusion) is its built-in image and video generation engine, included in this repository and the `sglang` Python package.

👋 Get started below, or meet the community at [SGLang Events](https://www.sglang.io/events), including meetups, developer meetings, workshops, and office hours.

## Get Started

Pull the Docker image, which includes SGLang and its dependencies:

```bash
docker pull lmsysorg/sglang:latest
```

Alternatively, install SGLang in an activated Python environment with uv:

```bash
uv pip install --prerelease=allow sglang
```

Next, launch your model:

- [Quickstart](https://docs.sglang.io/docs/get-started/quickstart): Run your first model and send a request.
- [Cookbook](https://cookbook.sglang.io/): Choose your model and hardware to get a ready-to-run launch command.

## Supported Hardware

SGLang supports a wide range of GPUs, TPUs, NPUs, CPUs, and Apple Silicon platforms.

| Platform | Representative hardware |
| --- | --- |
| [NVIDIA](https://docs.sglang.io/docs/hardware-platforms/nvidia-gpus) | B200/B300/GB200/GB300; H100/H200/H800/H20; A100; RTX 30/40/50 series, RTX 6000 Ada / PRO 6000; DGX Spark, Jetson Orin |
| [AMD](https://docs.sglang.io/docs/hardware-platforms/amd_gpu) | Instinct MI300X, MI325X, MI350X, MI355X |
| [Google TPU](https://docs.sglang.io/docs/hardware-platforms/tpu) | v6e, v7; [SGL-JAX](https://github.com/sgl-project/sglang-jax) / [SGL-torchtpu](https://lmsys.org/blog/2026-07-30-sglang-google-tpu/) |
| Intel ([GPU](https://docs.sglang.io/docs/hardware-platforms/xpu) / [CPU](https://docs.sglang.io/docs/hardware-platforms/cpu_server)) | Arc / Arc Pro B-Series GPUs, Xeon CPUs |
| [Apple Silicon](https://docs.sglang.io/docs/hardware-platforms/apple_metal) | Macs via Metal / MLX |
| [Huawei Ascend](https://docs.sglang.io/docs/hardware-platforms/ascend-npus/getting-started/installation) | A2, A3, 950PR/DT NPUs |
| [Moore Threads](https://docs.sglang.io/docs/hardware-platforms/mthreads_gpu) | MTT S5000 GPUs |

Integrations in progress: AWS Trainium, [Alibaba T-Head PPU](https://github.com/sgl-project/sglang/issues/37519), [Cambricon MLU](https://github.com/sgl-project/sglang/pull/26898), Qualcomm QAIC, MetaX, Hygon HCU/DCU, Iluvatar CoreX, and more.

See the [Cookbook](https://cookbook.sglang.io/) and platform guides for model compatibility and setup.

## SGL Ecosystem

| Area | Projects | Purpose |
| --- | --- | --- |
| Diffusion | [SGLang Diffusion](https://docs.sglang.io/docs/sglang-diffusion/installation) | Built into SGLang for image and video generation with diffusion models. |
| Audio | [SGLang Omni](https://github.com/sgl-project/sglang-omni) | Audio model serving for text-to-speech (TTS) and automatic speech recognition (ASR). |
| Speculative Decoding | [SpecForge](https://github.com/sgl-project/SpecForge) | Train draft models for speculative decoding and deploy them with SGLang. |
| RL and Post-Training | [Miles](https://github.com/radixark/miles), [slime](https://github.com/THUDM/slime), [AReaL](https://github.com/inclusionAI/AReaL), [Tunix](https://github.com/google/tunix), [verl](https://github.com/volcengine/verl) | Training frameworks that integrate SGLang for rollout generation. |
| Deployment and Orchestration | [SMG](https://github.com/smg-project/smg), [RBG](https://github.com/sgl-project/rbg), [llm-d](https://llm-d.ai/docs/dev/operations/disaggregation/sglang), [Ray Serve](https://docs.ray.io/en/latest/serve/llm/user-guides/sglang.html), [NVIDIA Dynamo](https://docs.nvidia.com/dynamo/backends/sg-lang/reference-guide) | Deploy and scale SGLang inference services with routing, load balancing, and cluster orchestration. |
| Education | [Mini-SGLang](https://github.com/sgl-project/mini-sglang), [zero-to-sglang](https://github.com/datawhalechina/zero-to-sglang), [DeepLearning.AI course](https://www.deeplearning.ai/short-courses/efficient-inference-with-sglang-text-and-image-generation/) | Learn inference engine design and efficient text and image generation through code and hands-on courses. |

## Development and Contributing

Contributions are welcome, from bug fixes and documentation to model support and performance improvements.

### Development setup

Start from the `lmsysorg/sglang:dev` Docker image, which provides development tools and most dependencies. Clone or mount your SGLang checkout inside the container, then install it in editable mode from the repository root so tests use your local Python changes:

```bash
pip install -e "python"
```

In an activated virtual environment, you can use `uv pip install --prerelease=allow -e "python"` instead. See the [development guide](https://docs.sglang.io/docs/developer_guide/development_guide_using_docker) for container setup and testing.

### Contribute

1. Fork the repository and create a branch for your changes. For larger changes, discuss your proposal in a [GitHub issue](https://github.com/sgl-project/sglang/issues) or on [Slack](https://slack.sglang.io/).
2. Make your changes, run the relevant tests, and add regression coverage for fixes or new behavior. Run `pre-commit run --all-files` before submitting.
3. Open a pull request describing the change and how you tested it. Include benchmarks or accuracy evaluations when relevant.

See the [contributor guide](https://docs.sglang.io/docs/developer_guide/contribution_guide) for formatting, testing, and pull request instructions. Documentation contributors can start with the [docs guide](docs/README.md).

## Community and Sponsorship

SGLang is hosted by [LMSYS](https://lmsys.org/about/), a non-profit open-source organization.

- **Community discussions:** Join [Slack](https://slack.sglang.io/) for technical questions and development discussions.
- **Events:** Find meetups, workshops, and office hours on [SGLang Events](https://www.sglang.io/events).
- **Updates:** Follow [X](https://x.com/lmsysorg) and [LinkedIn](https://www.linkedin.com/company/sgl-project/) for project updates, and the [LMSYS Blog](https://lmsys.org/blog/) for release announcements and technical articles.
- **Project resources:** Explore the [documentation](https://docs.sglang.io/), [Cookbook](https://cookbook.sglang.io/), [roadmap](https://roadmap.sglang.io/), [release notes](https://github.com/sgl-project/sglang/releases), [issue tracker](https://github.com/sgl-project/sglang/issues), and [contributor guide](https://docs.sglang.io/docs/developer_guide/contribution_guide).
- **Contact Us:** For enterprise adoption and deployment, technical consulting, sponsorship, or partnership inquiries, please contact [sglang@lmsys.org](mailto:sglang@lmsys.org).
- **Contributor sponsorship:** Long-term active SGLang contributors are eligible for coding agent sponsorship, including Cursor, Claude Code, or OpenAI Codex. To apply, email [sglang@lmsys.org](mailto:sglang@lmsys.org) with links to your key commits or pull requests.

## Trusted by Industry and Research

SGLang serves production workloads across AI labs, cloud platforms, enterprises, and universities.

## Acknowledgment
We learned the design and reused code from the following projects: [Guidance](https://github.com/guidance-ai/guidance), [vLLM](https://github.com/vllm-project/vllm), [LightLLM](https://github.com/ModelTC/lightllm), [FlashInfer](https://github.com/flashinfer-ai/flashinfer), [Outlines](https://github.com/outlines-dev/outlines), and [LMQL](https://github.com/eth-sri/lmql).

Repository info

Default branch: main
Latest version: v0.5.21
Created: 2024-01-08
Last activity: 2026-10-14
License: Apache-2.0
Open issues: 5.8k
Watchers: 185

Topics

attention blackwell cuda deepseek diffusion glm gpt-oss inference llama llm minimax moe qwen qwen-image reinforcement-learning transformer vlm wan

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