sglang – sgl-project

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News

• [2026/06] 🔥 The next generation of speculative decoding: DFlash and Spec V2 (blog).
• [2026/04] 🔥 DeepSeek-V4 on Day 0: From Fast Inference to Verified RL with SGLang and Miles (blog).
• [2026/06] SGLang provides day-0 support for latest open models (Nemotron 3 Ultra, Nemotron 3 Super, Higgs Audio v3 TTS).
• [2026/02] 🔥 Unlocking 25x Inference Performance with SGLang on NVIDIA GB300 NVL72 (blog).
• [2026/01] SGLang Diffusion accelerates video and image generation (blog).
• [2025/12] SGLang provides day-0 support for latest open models (MiMo-V2-Flash, Nemotron 3 Nano, Mistral Large 3, LLaDA 2.0 Diffusion LLM, MiniMax M2).
• [2025/10] SGLang now runs natively on TPU with the SGLang-Jax backend (blog).

About

SGLang is a high-performance serving framework for large language models and multimodal models.
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.
Its core features include:

• Fast Runtime: Provides efficient serving with RadixAttention for prefix caching, a zero-overhead CPU scheduler, prefill-decode disaggregation, speculative decoding, continuous batching, paged attention, tensor/pipeline/expert/data parallelism, structured outputs, chunked prefill, quantization (FP4/FP8/INT4/AWQ/GPTQ), and multi-LoRA batching.
• Broad Model Support: Supports a wide range of language models (Llama, Qwen, DeepSeek, Kimi, GLM, GPT, Gemma, Mistral, etc.), embedding models (e5-mistral, gte, mcdse), reward models (Skywork), and diffusion models (WAN, Qwen-Image), with easy extensibility for adding new models. Compatible with most Hugging Face models and OpenAI APIs.
• Extensive Hardware Support: Runs on NVIDIA GPUs (GB200/B300/H100/A100/Spark/5090), AMD GPUs (MI355/MI300), Intel Xeon CPUs, Google TPUs, Ascend NPUs, and more.
• Active Community: SGLang is open-source and supported by a vibrant community with widespread industry adoption, powering over 400,000 GPUs worldwide.
• RL & Post-Training Backbone: SGLang is a proven rollout backend used for training many frontier models, with native RL integrations and adoption by well-known post-training frameworks such as AReaL, Miles, slime, Tunix, verl and more.

Getting Started

• Install SGLang
• Quick Start
• Backend Tutorial
• Frontend Tutorial
• Contribution Guide

Benchmark and Performance

Learn more in the release blogs: v0.2 blog, v0.3 blog, v0.4 blog, Large-scale expert parallelism, GB200 rack-scale parallelism, GB300 long context.

Adoption and Sponsorship

SGLang has been deployed at large scale, generating trillions of tokens in production each day. It is trusted and adopted by a wide range of leading enterprises and institutions, including xAI, AMD, NVIDIA, Intel, LinkedIn, Cursor, Oracle Cloud, Google Cloud, Microsoft Azure, AWS, Atlas Cloud, Voltage Park, Nebius, DataCrunch, Novita, InnoMatrix, Modal, MIT, UCLA, the University of Washington, Stanford, UC Berkeley, Tsinghua University, Jam & Tea Studios, Baseten, and other major technology organizations.
As an open-source LLM inference engine, SGLang has become the de facto industry standard, with deployments running on over 400,000 GPUs worldwide.
SGLang is currently hosted under the non-profit open-source organization LMSYS.

Contact Us

For enterprises interested in adopting or deploying SGLang at scale, including technical consulting, sponsorship opportunities, or partnership inquiries, please contact us at sglang@lmsys.org.

Long-term active SGLang contributors are eligible for coding agent sponsorship, such as Cursor, Claude Code, or OpenAI Codex. Email sglang@lmsys.org with your most important commits or pull requests.

Acknowledgment

We learned the design and reused code from the following projects: Guidance, vLLM, LightLLM, FlashInfer, Outlines, and LMQL.