SGLang

Highlights

574 PRs from 169 contributors.

DSpark: confidence-driven speculative decoding: A new speculative algorithm. It drafts semi-autoregressively in blocks, then sizes each verify window from the draft's own confidence instead of a fixed draft length. Reaches 383.7 tok/s at accept length ~5 on DeepSeek-V4-Pro, TP8 on B300 (bs=1). Enable with --speculative-algorithm DSPARK and SGLANG_RAGGED_VERIFY_MODE=compact; tune the block with --speculative-dspark-block-size (#30261, #31434, blog).

Inkling support: A 975B-parameter multimodal MoE with a 1M-token context. It mixes sliding-window, full and Mamba2 linear attention, and adds an NVFP4 MoE, optional vision/audio towers and native MTP. On Blackwell it reaches up to 71.7k tok/s input and 171.0 tok/s per-user decode. Verified on Blackwell TP4/TP8, H200 and AMD MI350X / MI355X (#31681, blog, cookbook).

Other new models added: LongCat 2.0 FP8, JetBrains Mellum v2, Pi0.5, plus diffusion support for LongLive 2.0.

UnifiedRadixTree is now the default for SWA, Mamba and DSA models. Replay SSM and Mamba int8 checkpoints are synced onto it, and a cache hit now resets only the state it used (#30468, #30636, #30626, #31643).

GLM-5.2 DSA cache layer split under prefill CP: KV and indexer cache layers are sharded across CP ranks. Each rank owns a disjoint layer range instead of all layers. That cuts per-rank KV memory by ~74% (0.77 to 0.20 GB/rank) at 8192 tokens on GLM-5.2-FP8, 78 layers, cp_size=4. Enable with --enable-dsa-cache-layer-split, which needs --enable-prefill-cp --cp-strategy interleave (#29421).

ReplaySSM Ring Spec-Verify (GDN): Drops the per-draft SSM snapshot. Speculative scratch goes from 11.5 GB to 1.8 GB per GPU (6.4x smaller) on Qwen3.5-35B-A3B at TP1, at accuracy and throughput parity. Opt in with --enable-gdn-replayssm-spec (default off; GDN with a linear draft chain only, --speculative-eagle-topk in {None, 1}), and tune the ring via --linear-replayssm-cache-len (#28695).

Linear attention on Blackwell (SM100): The first correct KDA MTP path. Its recurrent_kda decode kernel runs at 29.6 us vs 36.8 us for Triton (ncu, B=64). The full decode path reaches parity by B=128 and 1.35x at B=256, and is slower below that (#30113). Separately, GDN/KDA CuteDSL prefill fuses state I/O into the chunk-h kernel (#30169).

QServe and FBGEMM FP8 quantization are removed: the experimental QServe (QoQ) W4A8 and FBGEMM FP8 paths are gone. --fp4-gemm-backend cutlass goes too, along with the in-tree NVFP4 JIT kernels, so NVFP4 GEMM now requires FlashInfer (#31109, #30448).

Dependencies: flashinfer 0.6.14 (#29910), CuTe DSL 4.6.0 (#31714), sgl-kernel 0.4.5 (#31496), llguidance 1.7.6 ([#31

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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).

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<summary>More</summary>

• [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).

• [2025/11] SGLang Diffusion accelerates video and image generation (blog).
• [2025/10] PyTorch Conference 2025 SGLang Talk (slide).
• [2025/10] SGLang x Nvidia SF Meetup on 10/2 (recap).
• [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).
• [2025/05] Deploying DeepSeek with PD Disaggregation and Large-scale Expert Parallelism on 96 H100 GPUs (blog).
• [2025/06] Deploying DeepSeek on GB200 NVL72 with PD and Large Scale EP (Part I): 2.7x Higher Decoding Throughput (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).

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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.

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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.