Cheng Wan
7071cfb873
publish(role=...) has recorded provenance since the namespace split; this wires the enforcement the role was reserved for. SGLANG_ROLE_NAMESPACES selects the mode: - off (default): no bookkeeping; the mode gate in config_bag stays a dead-branch-prunable check under dynamo (bag reads run inside compiled forwards — pinned by a fullgraph test). - record: audit mode — collect (role, namespace) pairs per process and persist each new pair immediately to SGLANG_ROLE_NAMESPACES_OUT (worker teardown skips atexit), plus a per-process stderr summary at exit. - enforce: a bag read outside the role's ROLE_NAMESPACE_SETS entry fails closed with an actionable error; None entries mean full tree. Sets are filled only where audits back them: dp_controller reads only exec (record-mode plain + DP-attention smokes agree with the module's static read set — the elastic-EP gate). tokenizer observed zero bag reads (per-instance managers read self.server_args by design) but keeps the full tree until the multi-tokenizer disagg shape (TokenizerWorker's get_disagg read) is audited; encoder / expert_backup / weight_cache_daemon likewise await their deployment shapes. Verified end-to-end: DP-attention smoke under enforce boots and serves with zero violations.
Blog | Documentation | Roadmap | Join Slack | Weekly Dev Meeting | Slides
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).
More
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[2025/09] Deploying DeepSeek on GB200 NVL72 with PD and Large Scale EP (Part II): 3.8x Prefill, 4.8x Decode Throughput (blog).
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[2025/09] SGLang Day 0 Support for DeepSeek-V3.2 with Sparse Attention (blog).
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[2025/08] SGLang x AMD SF Meetup on 8/22: Hands-on GPU workshop, tech talks by AMD/xAI/SGLang, and networking (Roadmap, Large-scale EP, Highlights, AITER/MoRI, Wave).
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[2025/11] SGLang Diffusion accelerates video and image generation (blog).
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[2025/10] PyTorch Conference 2025 SGLang Talk (slide).
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[2025/10] SGLang x Nvidia SF Meetup on 10/2 (recap).
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[2025/08] SGLang provides day-0 support for OpenAI gpt-oss model (instructions)
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[2025/06] SGLang, the high-performance serving infrastructure powering trillions of tokens daily, has been awarded the third batch of the Open Source AI Grant by a16z (a16z blog).
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[2025/05] Deploying DeepSeek with PD Disaggregation and Large-scale Expert Parallelism on 96 H100 GPUs (blog).
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[2025/06] Deploying DeepSeek on GB200 NVL72 with PD and Large Scale EP (Part I): 2.7x Higher Decoding Throughput (blog).
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[2025/03] Supercharge DeepSeek-R1 Inference on AMD Instinct MI300X (AMD blog)
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[2025/03] SGLang Joins PyTorch Ecosystem: Efficient LLM Serving Engine (PyTorch blog)
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[2025/02] Unlock DeepSeek-R1 Inference Performance on AMD Instinct™ MI300X GPU (AMD blog)
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[2025/01] SGLang provides day one support for DeepSeek V3/R1 models on NVIDIA and AMD GPUs with DeepSeek-specific optimizations. (instructions, AMD blog, 10+ other companies)
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[2024/12] v0.4 Release: Zero-Overhead Batch Scheduler, Cache-Aware Load Balancer, Faster Structured Outputs (blog).
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[2024/10] The First SGLang Online Meetup (slides).
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[2024/09] v0.3 Release: 7x Faster DeepSeek MLA, 1.5x Faster torch.compile, Multi-Image/Video LLaVA-OneVision (blog).
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[2024/07] v0.2 Release: Faster Llama3 Serving with SGLang Runtime (vs. TensorRT-LLM, vLLM) (blog).
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[2024/02] SGLang enables 3x faster JSON decoding with compressed finite state machine (blog).
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[2024/01] SGLang provides up to 5x faster inference with RadixAttention (blog).
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[2024/01] SGLang powers the serving of the official LLaVA v1.6 release demo (usage).
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
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.