SGLang: Fast inference for LLMs and multimodal models
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 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, including meetups, developer meetings, workshops, and office hours.
SGLang Summit 2026: Meet the builders of open AI
Our flagship annual gathering · November 12–13 · San Francisco
Go deep on inference, training, and AI hardware through technical talks, live demos, and hands-on workshops. Bring your deployment challenges and meet the engineers and researchers building the stack.
Get Started
Pull the Docker image, which includes SGLang and its dependencies:
docker pull lmsysorg/sglang:latest
Alternatively, install SGLang in an activated Python environment with uv:
uv pip install --prerelease=allow sglang
Next, launch your model:
- Quickstart: Run your first model and send a request.
- Cookbook: 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.
Integrations in progress: AWS Trainium, Alibaba T-Head PPU, Cambricon MLU, Qualcomm QAIC, MetaX, Hygon HCU/DCU, Iluvatar CoreX, and more.
SGLang-TorchTPU brings the SGLang serving engine to Google TPUs through PyTorch, torch_tpu, and Pallas kernels. It supports LLM and vision-language serving on TPU7x (Ironwood), including tensor and expert parallelism across hosts. SGL-JAX provides the JAX-based TPU serving path.
See the Cookbook and platform guides for model compatibility and setup.
SGL Ecosystem
| Area | Projects | Purpose |
|---|
| Diffusion | SGLang Diffusion | Built into SGLang for image and video generation with diffusion models. |
| Audio | SGLang Omni | Audio model serving for text-to-speech (TTS) and automatic speech recognition (ASR). |
| Speculative Decoding | SpecForge | Train draft models for speculative decoding and deploy them with SGLang. |
| RL and Post-Training | Miles, slime, AReaL, Tunix, verl | Training frameworks that integrate SGLang for rollout generation. |
| Deployment and Orchestration | SMG, RBG, llm-d, Ray Serve, NVIDIA Dynamo | Deploy and scale SGLang inference services with routing, load balancing, and cluster orchestration. |
| Education | Mini-SGLang, zero-to-sglang, DeepLearning.AI course | 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:
pip install -e "python"
In an activated virtual environment, you can use uv pip install --prerelease=allow -e "python" instead. See the development guide for container setup and testing.
This is the beginning of the README. Read the rest at the original. Read the full README ↗
