Cheng Wan
c87a2ced12
`test_bag_values_match_server_args` asserted `bag == field`. That holds today only because construction resolves in place; step 12 keeps the record raw, and the plan doc calls this test out as one that becomes **false by design** for every field resolution fills in. Rewritten against the resolved projection, which is the half that survives: the bag carries what resolution produced. The `bag == field` assertion stays as one line at the end, labelled as the tripwire -- when it starts failing for a resolution-written leaf, the flip has landed and the bag is the only place the effective value lives. The reference is an independent resolution of the same raw input (a fresh, never-published record) rather than `resolved_server_args_dict()`, which reads `vars(server_args)` back and therefore only restates the published instance. And the record goes through the real pipeline on a real mini config, published through `publish()`: the dummy-model path returns at the dummy boundary with every sampled leaf still raw, so the old comparison was raw==raw and vacuous (both Codex catches). Reproducibility (#34094) licenses the sibling as a stand-in for the pipeline output. The sample admits only leaves resolution writes on this input on both CI device shapes (attention_backend, page_size, chunked_prefill_size, mem_fraction_static), and the raw-differs guard asserts it per leaf -- a default-count threshold let supplied inputs like `model_path` (no dataclass default, so any path "differs") stand in for resolution work. Passthrough leaves (host, hicache_ratio, moe_runner_backend, model_path) move to a separate projection smoke that claims only what it checks: publish projected an unchanged field into its namespace. Between the two resolutions the test restores environ and the EnvField none-flags, so the sibling resolves the same pristine input rather than the first resolution's leftovers. And the class runs its body exactly once, like the other dual-resolve harnesses: a CI retry re-enters after the first attempt leaked process state, which is the hazard the pristine snapshot exists to rule out. docs(skill): a supplied-instance read is not automatically safe The whole-object rule said "keep the supplied-instance contract; don't rewrite the parameter reads unless the field is runtime-mutated". That is the right rule for the *object* and the wrong stopping point for the *field*: after step 12 the record carries the user's raw input, so `server_args.page_size` inside a runner-owned constructor reads the CLI default rather than the effective value. The rule now names that second case as step-12 debt with a guard attached (`test_supplied_instance_exposure_ratchet.py` fails on a new pair, so the decision is made when the read is written), and names the two shapes that stay parameter-form on purpose: a helper the resolution pipeline calls with a `resolved_view`, and a factory whose contract is "build X from the record you are handed".
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News
- [2026/07] 🔥 SGLang and Miles add day-0 support for Kimi K3 (blog).
- [2026/07] RadixArk and Google bring full SGLang features to TPUs (blog).
- [2026/07] Serving GLM5.2 NVFP4 agentic workloads with SGLang: Reaching 500 TPS in two weeks (blog).
- [2026/06] 🔥 The next generation of speculative decoding: DFlash and Spec V2 (blog).
- [2026/06] SGLang provides day-0 support for latest open models (Nemotron 3 Ultra, Nemotron 3 Super, Higgs Audio v3 TTS).
- [2026/04] 🔥 DeepSeek-V4 on Day 0: From Fast Inference to Verified RL with SGLang and Miles (blog).
- [2026/02] 🔥 Unlocking 25x Inference Performance with SGLang on NVIDIA GB300 NVL72 (blog).
- [2026/01] SGLang Diffusion accelerates video and image generation (blog).
More
- [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).
- [2025/11] SGLang Diffusion accelerates video and image generation (blog).
- [2025/10] SGLang now runs natively on TPU with the SGLang-Jax backend (blog).
- [2025/10] PyTorch Conference 2025 SGLang Talk (slide).
- [2025/10] SGLang x Nvidia SF Meetup on 10/2 (recap).
- [2025/09] Deploying DeepSeek on GB200 NVL72 with PD and Large Scale EP (Part II): 3.8x Prefill, 4.8x Decode Throughput (blog).
- [2025/09] SGLang Day 0 Support for DeepSeek-V3.2 with Sparse Attention (blog).
- [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).
- [2025/08] SGLang provides day-0 support for OpenAI gpt-oss model (instructions)
- [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).
- [2025/06] Deploying DeepSeek on GB200 NVL72 with PD and Large Scale EP (Part I): 2.7x Higher Decoding Throughput (blog).
- [2025/05] Deploying DeepSeek with PD Disaggregation and Large-scale Expert Parallelism on 96 H100 GPUs (blog).
- [2025/03] Supercharge DeepSeek-R1 Inference on AMD Instinct MI300X (AMD blog)
- [2025/03] SGLang Joins PyTorch Ecosystem: Efficient LLM Serving Engine (PyTorch blog)
- [2025/02] Unlock DeepSeek-R1 Inference Performance on AMD Instinct™ MI300X GPU (AMD blog)
- [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)
- [2024/12] v0.4 Release: Zero-Overhead Batch Scheduler, Cache-Aware Load Balancer, Faster Structured Outputs (blog).
- [2024/10] The First SGLang Online Meetup (slides).
- [2024/09] v0.3 Release: 7x Faster DeepSeek MLA, 1.5x Faster torch.compile, Multi-Image/Video LLaVA-OneVision (blog).
- [2024/07] v0.2 Release: Faster Llama3 Serving with SGLang Runtime (vs. TensorRT-LLM, vLLM) (blog).
- [2024/02] SGLang enables 3x faster JSON decoding with compressed finite state machine (blog).
- [2024/01] SGLang provides up to 5x faster inference with RadixAttention (blog).
- [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, NVIDIA, AMD, Intel, LinkedIn, Cursor, Oracle Cloud, Google Cloud, Microsoft Azure, AWS, Atlas Cloud, Voltage Park, Nebius, DataCrunch, Novita, RunPod, InnoMatrix, Modal, MIT, UCLA, the University of Washington, Stanford, UC Berkeley, Tsinghua University, Baseten, Baidu, AntGroup, Alibaba, Tencent, 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.