# SGLang v0.5.11

- Product: SGLang (https://whatsnew.fyi/product/sglang)
- Vendor: LMSYS
- Date: 2026-05-05
- Version: v0.5.11
- Original notes: https://github.com/sgl-project/sglang/releases/tag/v0.5.11
- Permalink: https://whatsnew.fyi/product/sglang/releases/v0.5.11

What's New is an index, not a publisher: every entry below links to the vendor's own release notes, which are the authoritative source. Entries are labelled where they are hand-curated sample data, pre-releases, or drawn from a secondary source such as a developer blog.

Reuse: the summaries, labels and curation here are © What's New. Quote freely with attribution and a link back; wholesale republication of the corpus is not permitted — terms: https://whatsnew.fyi/terms. The vendors' own release notes remain their publishers'.

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- **changed** — Default CUDA version upgraded to 13.0 across SGLang, sgl-kernel, and Docker images, with PyTorch upgraded from 2.9 to 2.11
- **changed** — Speculative Decoding V2 with overlap scheduling is now the default, reducing per-step CPU cost for EAGLE/MTP/DFLASH paths
- **added** — Decode-side prefix caching now works under prefill/decode disaggregation, recovering radix-cache hit rates and TTFT savings for long shared prefixes in disaggregated deployments
- **added** — Support for Gemma 4, GLM-5.1, Qwen3.6, MiMo-V2.5/V2.5-Pro, Ling-2.6-Flash, Mistral Medium 3.5, and Kimi-K2.6 models
- **added** — DFLASH speculative decoding kernel with support across model backends and AMD ROCm
- **added** — FA3 kernels integrated alongside FA4 as a high-performance option
- **added** — LoRA support for DeepSeek-V3 and Kimi-K2, enabling adapter-based fine-tuning of MLA-based MoE models
- **added** — All-reduce and RMSNorm fusion under context parallel for end-to-end speedups
- **added** — Support for moe_dp_size = 1 paired with arbitrary attention_cp_size to independently tune MoE and attention parallelism
- **added** — FlashInferCuteDslMoE layer for the standard FP4 MoE path
- **added** — Support for Qwen3-ASR with chunk-based streaming
- **added** — Support for Voxtral (Mistral speech-to-text)
- **added** — Support for Parakeet (NVIDIA Nemotron encoder)
- **added** — Support for Moss-VL model
- **added** — Support for SequenceClassification model architecture that powers the Score API
- **added** — Support for Stable Diffusion 3 medium diffusion model
- **added** — Support for ERNIE-Image diffusion model
- **added** — Support for JoyAI-Image-Edit diffusion model
- **added** — Incremental transfer support for Mooncake transfer engine in PD disaggregation
- **fixed** — Bug fixes for Eagle3/DFLASH aux hidden state capture during CUDA graph initialization, IntraNode NVLink, MTP-layer KV transfer, and disagg-prefill DP rank resolution

#### Highlights

- **CUDA 13 + Torch 2.11**: Default CUDA version moves to 13.0 across SGLang, sgl-kernel, and Docker images, and PyTorch is upgraded from 2.9 to 2.11 — modernizing the build matrix and unlocking newer kernels: #21247, #24162, #24183, #23593 ([tracking issue #21498](https://github.com/sgl-project/sglang/issues/21498))

- **Speculative Decoding V2 by default**: Spec V2 (with overlap scheduling to hide CPU overhead) is now the default, materially reducing per-step CPU cost for EAGLE/MTP/DFLASH paths: #21062

- **Decode Radix Cache for PD Disaggregation**: Decode-side prefix caching now works under prefill/decode disaggregation, recovering radix-cache hit rates and TTFT savings for long shared prefixes in disaggregated deployments: #19746

- **Day-0 / New Model Support**: Gemma 4, GLM-5.1, Qwen3.6, MiMo-V2.5 / V2.5-Pro, Ling-2.6-Flash, Mistral Medium 3.5, and Kimi-K2.6 — with cookbook recipes for tuned deployment commands. See [docs.sglang.io/cookbook](https://docs.sglang.io/cookbook): #21952, #23808, #23811, #23851, #23947, #23486, #23394

- **DFLASH Speculative Decoding**: New high-throughput spec-decode kernel from the kernel community, expanded across model backends and AMD ROCm: #22077, #22358, #22342, #23553

- **FA3 Kernels from the Kernel Community**: Drop-in FA3 kernels contributed by the community, integrated alongside FA4 to give users a high-performance option that's easy to maintain: #20796

- **LoRA support for DeepSeek-V3 and Kimi-K2**: LoRA now works on the largest MLA-based MoE models, including DeepSeek-V3 MLA LoRA and Kimi K2 — enabling adapter-based fine-tuning of frontier-scale models: #22323, #22381

- **Context Parallel (CP) Enhancements**: All-reduce + RMSNorm fusion under CP for end-to-end speedups, plus support for `moe_dp_size = 1` paired with arbitrary `attention_cp_size` so MoE and attention parallelism can be tuned independently: #21249, #22003

- **FlashInfer CuteDSL MoE Runner Backend**: New dedicated `FlashInferCuteDslMoE` layer for the standard FP4 MoE path, giving an additional high-performance fused-MoE option: #21339

##### New Model Support

Entries with a published cookbook recipe come first; entries whose cookbook page is still pending are grouped at the bottom.

* Gemma 4: #21952 (and follow-ups #22079, #24048, #22842; see [cookbook](https://docs.sglang.io/cookbook/autoregressive/Google/Gemma4))
* GLM-5.1: #22543, #23037 (see [cookbook](https://cookbook.sglang.io/autoregressive/GLM/GLM-5.1))
* Qwen3.6: #23486 (see [cookbook](https://docs.sglang.io/cookbook/autoregressive/Qwen/Qwen3.6))
* MiMo-V2.5 / MiMo-V2.5-Pro: #23808, #23811, #23851, #23945, #24118 (see [cookbook](https://docs.sglang.io/cookbook/autoregressive/Xiaomi/MiMo-V2.5))
* Ling-2.6-Flash: #23947 (see [cookbook](https://docs.sglang.io/cookbook/autoregressive/InclusionAI/Ling-2.6))
* Mistral Medium 3.5: see [cookbook](https://docs.sglang.io/cookbook/autoregressive/Mistral/Mistral-Medium-3.5)
* Kimi-K2.6: #23394, #23408 (see [cookbook](https://docs.sglang.io/cookbook/autoregressive/Moonshotai/Kimi-K2.6))
* Hunyuan v3 (Tencent, preview): #23533 (see [cookbook](https://docs.sglang.io/cookbook/autoregressive/Tencent/Hunyuan3-Preview))
* FLUX.1-dev ModelOpt NVFP4 (Diffusion): #22672 (see [FLUX cookbook](https://docs.sglang.io/cookbook/diffusion/FLUX/FLUX))
* FLUX.2-small-decoder (Diffusion): #22414 (see [FLUX cookbook](https://docs.sglang.io/cookbook/diffusion/FLUX/FLUX))
* Qwen Image ModelOpt FP8 (Diffusion): #23155 (see [Qwen-Image cookbook](https://docs.sglang.io/cookbook/diffusion/Qwen-Image/Qwen-Image))
* LTX-2.3 / LTX-2.3 two-stage / TI2V (Diffusion): #22182, #22667, #22869 (see [LTX cookbook](https://docs.sglang.io/cookbook/diffusion/LTX/LTX))
* Qwen3-ASR (chunk-based streaming): #22073, #22089
* Voxtral (Mistral speech-to-text): #21635
* Parakeet (NVIDIA Nemotron encoder): #23568
* Moss-VL: #23454
* SequenceClassification model architecture (powers the Score API): #22118

_[Truncated at 4000 characters — full notes: https://github.com/sgl-project/sglang/releases/tag/v0.5.11]_