leavelet
85585fcf4b
Replaces the interim ack_queue_len==0 band-aid with a structural fix so the perf skip is
SAFE rather than removed.
Root cause: the radix attach/defer/prune/split probes read `_node_host_write_pending`
(membership in ongoing_write_through/pending_host_backups, which are drained ASYNCHRONOUSLY
at per-rank ack completion), and `_cp_subtree_has_unprunable_state` also read the rank-local
`lock_ref`. So per-rank ack-drain timing leaked into the match_prefix truncation ->
cache_protected_len -> logical_len/len(value) -> the attach predicate. On some ranks the
prepared backup attached, on others it was dropped to the synchronous write_backup catch-up
(catch_up_all_layers draft=True), whose ack-append is per-rank. ack_write_queue then diverged
across CP ranks, so writing_check's ack_queue_len==0 early-return diverged: a strict subset
entered the writing_check_min all_reduce while the rest advanced to recv_requests' broadcast
on the same tp_cpu_group (8 ranks; dp_size==1 resets enable_dp_attention to False) -> NCCL
deadlock (prod hang: node_id=50713, all 8 GPUs 0% util).
Fix: introduce TreeNode.cp_backup_pending, a rank-replicated marker set at the rank-replicated
prepared-backup attach (_attach_prepared_cp_backup) and the guarded write_backup registration,
cleared only at the MIN-committed commit (_commit_pending_backup) / rollback. Rewire the CP
radix probes (_cp_node_split_still_pending, _split_node guard, _cp_subtree_has_unprunable_state,
_inc_hit_count) to read this marker instead of the drain-timed dicts, and DROP the lock_ref read
in the prune probe (its only cross-rank skew is the backup lock, now covered by the marker).
_node_host_write_pending stays the drain-timed accessor used only by writing_check / eviction /
the visibility check (_node_host_write_ready). With the radix-path reads replicated by
construction, len(value)==logical_len on every rank -> the prepared backup always attaches (or
rolls back) symmetrically -> the asymmetric catch-up never fires -> ack_write_queue is symmetric
-> the existing ack_queue_len==0 skip is all-or-none (no per-tick no-op MIN, best perf).
Correctness: the marker is cleared only at/after _commit_pending_backup, which runs only under
the MIN frontier, so the MIN remains the SOLE host-visibility gate (no KV-corruption class
reopened); the marker is strictly more conservative than the old dicts (stays True until commit).
Fail-fasts: double-attach raises; _split_node propagates the marker. hi_mamba unchanged (gates
on ongoing only, no async ack skip). Single-layer-draft (c3fc3ff752) preserved: the design
touches only the attach decision; the draft notifier already fires post-MoE.
Adds test_cp_hicache_symmetric_attach.py: the probes read the replicated marker not the dicts,
the prune probe ignores lock_ref, and the marker lifecycle (attach->commit->rollback) + the
double-attach guard. All pass on the fix; all fail on the pre-fix code.
Design: docs_internal/cp_hicache_symmetric_attach_design.md. Pending: ETE (GSM8K + no-hang
flood + TTFT) on the dev-cu13 container.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
(cherry picked from commit 876a98177cd749b5a63623ba2ae8b868289b8e59)
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News
- [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).
- [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).
More
- [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).
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), 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.
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, MIT, UCLA, the University of Washington, Stanford, UC Berkeley, Tsinghua University, Jam & Tea Studios, Baseten, and other major technology organizations across North America and Asia. 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
Acknowledgment
We learned the design and reused code from the following projects: Guidance, vLLM, LightLLM, FlashInfer, Outlines, and LMQL.