SGLang
SGLang is a high-performance serving framework for large language models and multimodal models, designed to deliver low-latency and high-throughput inference from a single GPU to large distributed clusters. It solves the problem of efficient and scalable model serving with features like prefix caching and continuous batching. It is for developers and organizations deploying LLMs in production.
✨ Key features
- Fast runtime with RadixAttention prefix caching and zero-overhead scheduler
- Supports prefill-decode disaggregation, speculative decoding, and continuous batching
- Broad model support including Llama, Qwen, DeepSeek, and diffusion models
- Runs on NVIDIA, AMD, Intel, Google TPU, and Ascend hardware
- Native RL integrations for post-training frameworks like AReaL and verl
- Open-source with active community and production-scale adoption
🎯 Use cases
- Serving large language models in production with high throughput
- Deploying multimodal models for image and video generation
- Powering reinforcement learning and post-training pipelines
- Running structured outputs and JSON decoding at scale
- Building custom serving solutions on diverse hardware
📦 Installation
🧰 Requirements: Requires Python and a supported GPU (NVIDIA, AMD, etc.) or other hardware; no specific OS or API keys mentioned in README.
For installation instructions, please refer to the official documentation: Install SGLang.
🚀 Usage
For a quick start example, see the official guide: Quick Start.
⚠️ Good to know
No limitations or project status caveats are stated in the README.
❓ FAQ
What hardware does SGLang support?
SGLang runs on NVIDIA GPUs (e.g., GB200, H100), AMD GPUs (e.g., MI300), Intel Xeon CPUs, Google TPUs, and Ascend NPUs.
Can SGLang serve multimodal models?
Yes, SGLang supports multimodal models including diffusion models for video and image generation, and models like LLaVA.
Is SGLang used in production?
Yes, SGLang is deployed at scale, generating trillions of tokens daily and powering over 400,000 GPUs worldwide.
Does SGLang integrate with RL frameworks?
Yes, SGLang is a rollout backend for RL and post-training, with native integrations for frameworks like AReaL, Miles, and verl.
📊 Repository
🤖 Overview, features, install steps and FAQ were generated from the project's README on Sep 4, 2026. Always check the original source before running commands.