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)

• 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: #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)
• GLM-5.1: #22543, #23037 (see cookbook)
• Qwen3.6: #23486 (see cookbook)
• MiMo-V2.5 / MiMo-V2.5-Pro: #23808, #23811, #23851, #23945, #24118 (see cookbook)
• Ling-2.6-Flash: #23947 (see cookbook)
• Mistral Medium 3.5: see cookbook
• Kimi-K2.6: #23394, #23408 (see cookbook)
• Hunyuan v3 (Tencent, preview): #23533 (see cookbook)
• FLUX.1-dev ModelOpt NVFP4 (Diffusion): #22672 (see FLUX cookbook)
• FLUX.2-small-decoder (Diffusion): #22414 (see FLUX cookbook)
• Qwen Image ModelOpt FP8 (Diffusion): #23155 (see Qwen-Image cookbook)
• LTX-2.3 / LTX-2.3 two-stage / TI2V (Diffusion): #22182, #22667, #22869 (see LTX cookbook)
• 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
• Stable Diffusion 3 medium (Diffusion): #19225
• ERNIE-Image (Diffusion): #22439
• JoyAI-Image-Edit (Diffusion): #22625

Speculative Decoding

• DFLASH speculative decoding initial support: #22077
• DFLASH enabled across additional model backends: #22358
• DFLASH speculative decoding on AMD ROCm: #22342
• Spec V2 enabled by default with overlap scheduling: #21062
• Penalty support for Spec V2 overlap scheduling: #22049
• Adaptive speculative_num_steps for EAGLE topk=1: #21599
• Allow piecewise CUDA graph with speculative decoding: #22128
• Eagle3 / DFLASH aux hidden state capture during CUDA graph init fixed: #22836
• Split accept_length into num_accepted_drafts / num_accepted_tokens: #23962
• DFLASH speculative decoding documentation: #23553

PD Disaggregation

• Decode-side radix cache support: #19746
• Incremental transfer for Mooncake transfer engine: #24257
• Allow PrefillDelayer in disaggregated-prefill mode: #23588
• NIXL: heterogeneous TP KV transfer for non-MLA models (Step 1/2 for Qwen3.5): #22145
• NIXL: Mamba state slice transfer for heterogeneous TP (Step 2/2 for Qwen3.5): #22240
• Bug fixes for IntraNode NVLink, MTP-layer KV transfer, and disagg-prefill DP rank resolution: #23252, #23539, #22901, #22990

Context Parallel & Parallelism

• All-reduce fusion support under CP: #21249
• moe_dp_size = 1 paired with arbitrary attention_cp_size: #22003
• All-reduce fusion enabled for DSA models: #22390
• Replace all-reduce + dp_scatter with reduce_scatterv for DP attention: #22642
• Step3p5: optimize all-reduce in MoE layers: #22773
• Pipeline parallelism on Intel XPU: #23472
• OpenTelemetry tracing for pipeline parallelism: #23169

LoRA

• DeepSeek-V3 MLA LoRA support and quantization-info refactor: #22323
• Kimi K2 LoRA support: #22381
• LoRADrainer to address high P99 TTFT: #17913
• Decoupled LoRA MoE backend with Marlin support: #21858
• Virtual experts for LoRA MoE (1/n): #22122, #24007
• CSGMV kernel offline auto-tuning: #20391
• Triton sgemm speedup with better grid selection: #22386
• Dual MoE CUDA graph capture for lora/nolora batches: #22809

Performance

• FA3 kernels from the kernel community: #20796
• Precompute FA3 scheduler_metadata to eliminate per-layer prepare cost: #21104
• Precompute gemma_weight to avoid redundant add on every forward: #22673
• Eliminate attention DtoD copy by passing pre-allocated output to FA: #21985
• Skip KV cache in FA backend for embedding mode: #21971
• O(1) RadixKey view for EAGLE bigram key: #23106
• PCG inductor path optimization for FP8 models: #23227
• Combo-kernels for horizontal fusion: #21977
• Optimize Gemma4 VLM with PCG and fused RMSNorm + residual add + scalar: #24048
• Restore torch.compile fusion for topk postprocessing: #21771
• Reduce unnecessary kernels and copies in the NSA indexer: #22232

Observability

• Pending token count surfaced in prefill log and get_load: #22480
• OpenTelemetry tracing for speculative decoding: #19545
• OpenTelemetry tracing for pipeline parallelism: #23169
• OpenTelemetry tracing in DiffGenerator: #21254
• Prometheus metrics endpoint for gRPC mode: #20801
• HTTP sidecar endpoints and FlushCache gRPC RPC for gRPC mode: #22500
• Raw KV cache pool token counts as Prometheus gauges: #22726

SGLang-Diffusion

• New model support: LTX-2.3 (#22182, #22667, #22869), ERNIE-Image (#22439), FLUX.2-small-decoder (#22414), JoyAI-Image-Edit (#22625), FLUX.1-dev ModelOpt NVFP4 (#22672), Qwen Image ModelOpt FP8 (#23155), Stable Diffusion 3 medium (#19225)
• ModelOpt diffusion FP8 support for Flux1/Flux2 and Wan2.2: #22365
• Standalone Rollout API + Denoising Environment Backpass + SP-Aligned Log-Prob for T2I post-training: #22604
• Disaggregated diffusion: #21701
• Dynamic batching v0: #18764
• CPU platform support for SGLang Diffusion: #20816
• AITER backends in Flux 2 pipeline (AMD): #22802
• LTX-2 feed-forward tensor parallelism optimization: #23221
• In-memory loading for URL/base64 image inputs (default): #23118
• Mixed-resolution benchmark support: #20863
• Auto-enable best parallel setting if unspecified: #22763

AMD

• MiniMax-M2.5 optimizations (aiter biased grouped topk; fused FP8 KV cache write): #23611, #23620
• Fused QK Gemma norm kernels (4 → fewer kernels): #23575
• Fused all-reduce + RMSNorm simplification: #21986
• GLM-5 / GLM-5.1 MXFP4 nightly accuracy + perf benchmarks (MI30x / MI35x): #21773, #22336
• MTP for GLM-5-mxfp4: #23219
• Aiter v0.1.12.post1 upgrade: #22264
• DFLASH speculative decoding enabled on ROCm: #22342
• Fix --page-size > 1 memory access fault with speculative decoding: #23596

NPU / Ascend

• Ascend backend supports Qwen3 MoE attention CP: #21685
• GLM-4.5V and GLM-4.7-Flash NPU support / fixes: #22961, #22509
• MTP for Qwen3.5: #20918
• TP communications compression for Qwen3 on NPU: #20520
• Add support-new-models documentation for NPU: #23824
• GGUF quantization for Ascend NPU (dense + MoE): #17883

CPU

• GPTQ / AWQ 4-bit quantization on CPU: #22685
• gemma4_rmsnorm_cpu kernel: #22842
• Qwen3.5 model optimization for CPU: #19484
• Apply routed scaling factor on output for biased grouped topk fusion: #22413
• Fix extend_attention_cpu / flash_attn_varlen_func NaN for large seq: #22434

Quantization

• MXFP4 quantized dense models on AMD CDNA2/CDNA3 GPUs: #19143 (later reverted in #23031, follow-up forthcoming)
• NVFP4 KV cache: quantization strategy abstraction and kernel: #21954
• DeepSeek-R1-0528-w4a8 + DeepEP Low-Latency FP8 dispatch: #22316
• MXFP8 sm100 path cleanup: #21881
• GLM-5/5.1 MXFP4 checkpoint inference compatibility fix: #22543

Dependencies

• Torch upgraded 2.9 → 2.11: #21247
• Default CUDA bumped to 13.0 across sglang, sgl-kernel, and Docker images: #21498 (tracking), #24162, #24183, #23593, #23119
• Flashinfer 0.6.7.post2 → 0.6.8.post1: #23281
• sgl-kernel bumped to 0.4.1.post1: #23720, #23733
• sgl-kernel bumped to 0.4.2: #24170
• Aiter v0.1.12.post1 (AMD): #22264

Security

• Fix for CVE-2026-5760: #23660
• Fix Trivy CVEs and cubin download 403s in Docker image: #22322

All PRs included in this release: https://github.com/sgl-project/sglang/compare/v0.5.10.post1...v0.5.11

New Contributors

• @AethoceSora made their first contribution in https://github.com/sgl-project/sglang/pull/23426
• @AlbeeSo made their first contribution in https://github.com/sgl-project/sglang/pull/23710
• @alec-flowers made their first contribution in https://github.com/sgl-project/sglang/pull/24090
• @AlonKejzman made their first contribution in https://github.com/sgl-project/sglang/pull/23753
• @amacaskill made their first contribution in https://github.com/sgl-project/sglang/pull/22537
• @AndyLi429 made their first contribution in https://github.com/sgl-project/sglang/pull/21685
• @Baichuan7 made their first contribution in https://github.com/sgl-project/sglang/pull/23060
• @ccullen-cert made their first contribution in https://github.com/sgl-project/sglang/pull/23660
• @ChangLiu0709 made their first contribution in https://github.com/sgl-project/sglang/pull/22908
• @charlotte12l made their first contribution in https://github.com/sgl-project/sglang/pull/21983
• @chenkaiyue made their first contribution in https://github.com/sgl-project/sglang/pull/17195
• @chx96642264 made their first contribution in https://github.com/sgl-project/sglang/pull/22705
• @ColinZ22 made their first contribution in https://github.com/sgl-project/sglang/pull/22543
• @cyyc0310 made their first contribution in https://github.com/sgl-project/sglang/pull/22920
• @divyamagrawal06 made their first contribution in https://github.com/sgl-project/sglang/pull/23325
• @dyhsup made their first contribution in https://github.com/sgl-project/sglang/pull/22439
• @egvenediktov made their first contribution in https://github.com/sgl-project/sglang/pull/20520
• @erikwijmans made their first contribution in https://github.com/sgl-project/sglang/pull/21974
• @fengli1702 made their first contribution in https://github.com/sgl-project/sglang/pull/19143
• @fergusfinn made their first contribution in https://github.com/sgl-project/sglang/pull/21035
• @fortunecookiee made their first contribution in https://github.com/sgl-project/sglang/pull/20960
• @gxlvera made their first contribution in https://github.com/sgl-project/sglang/pull/19225
• @he-yufeng made their first contribution in https://github.com/sgl-project/sglang/pull/20739
• @Henson-Zh-Ali made their first contribution in https://github.com/sgl-project/sglang/pull/20522
• @icepoint666 made their first contribution in https://github.com/sgl-project/sglang/pull/22592
• @iridiumine made their first contribution in https://github.com/sgl-project/sglang/pull/20918
• @is-not made their first contribution in https://github.com/sgl-project/sglang/pull/18349
• @JasonHe-WQ made their first contribution in https://github.com/sgl-project/sglang/pull/21944
• @jh-nv made their first contribution in https://github.com/sgl-project/sglang/pull/21254
• @jiangyinzuo made their first contribution in https://github.com/sgl-project/sglang/pull/23169
• @JieTang66 made their first contribution in https://github.com/sgl-project/sglang/pull/23983
• @JoyFuture made their first contribution in https://github.com/sgl-project/sglang/pull/23808
• @jthakurH made their first contribution in https://github.com/sgl-project/sglang/pull/16793
• @kangyifei made their first contribution in https://github.com/sgl-project/sglang/pull/23241
• @kingkingleeljj made their first contribution in https://github.com/sgl-project/sglang/pull/20967
• @kkyyxhll made their first contribution in https://github.com/sgl-project/sglang/pull/23062
• @KrishnanPrash made their first contribution in https://github.com/sgl-project/sglang/pull/22175
• @lahmuller made their first contribution in https://github.com/sgl-project/sglang/pull/22625
• @lixuwei2333 made their first contribution in https://github.com/sgl-project/sglang/pull/22247
• @lkhl made their first contribution in https://github.com/sgl-project/sglang/pull/22431
• @loading66 made their first contribution in https://github.com/sgl-project/sglang/pull/22700
• @luccafong made their first contribution in https://github.com/sgl-project/sglang/pull/24165
• @mingyue300 made their first contribution in https://github.com/sgl-project/sglang/pull/21723
• @minosfuture made their first contribution in https://github.com/sgl-project/sglang/pull/23419
• @mispa-ms made their first contribution in https://github.com/sgl-project/sglang/pull/23097
• @mlleo made their first contribution in https://github.com/sgl-project/sglang/pull/23537
• @Napkin-AI made their first contribution in https://github.com/sgl-project/sglang/pull/23572
• @nvpohanh made their first contribution in https://github.com/sgl-project/sglang/pull/22852
• @officialasishkumar made their first contribution in https://github.com/sgl-project/sglang/pull/22600
• @opherlieber made their first contribution in https://github.com/sgl-project/sglang/pull/22547
• @ranjiewen made their first contribution in https://github.com/sgl-project/sglang/pull/21698
• @RichardoMrMu made their first contribution in https://github.com/sgl-project/sglang/pull/19545
• @robellliu-dev made their first contribution in https://github.com/sgl-project/sglang/pull/20835
• @SammLSH made their first contribution in https://github.com/sgl-project/sglang/pull/22089
• @Seven-Streams made their first contribution in https://github.com/sgl-project/sglang/pull/21722
• @shenxiul made their first contribution in https://github.com/sgl-project/sglang/pull/23327
• @siju-samuel made their first contribution in https://github.com/sgl-project/sglang/pull/23472
• @stepinto made their first contribution in https://github.com/sgl-project/sglang/pull/23478
• @tfhddd made their first contribution in https://github.com/sgl-project/sglang/pull/22029
• @vvagaytsev made their first contribution in https://github.com/sgl-project/sglang/pull/22363
• @WangHao-hw made their first contribution in https://github.com/sgl-project/sglang/pull/22778
• @Wen-xuan-Xu made their first contribution in https://github.com/sgl-project/sglang/pull/22923
• @xiaobochen-amd made their first contribution in https://github.com/sgl-project/sglang/pull/22626
• @yaya159456 made their first contribution in https://github.com/sgl-project/sglang/pull/21694
• @YMbmzy made their first contribution in https://github.com/sgl-project/sglang/pull/22049
• @yuki-brook made their first contribution in https://github.com/sgl-project/sglang/pull/18016
• @Zaire404 made their first contribution in https://github.com/sgl-project/sglang/pull/22982
• @ZeyuanChen2000 made their first contribution in https://github.com/sgl-project/sglang/pull/21543
• @zhaozx-cn made their first contribution in https://github.com/sgl-project/sglang/pull/22266
• @zhsurpass made their first contribution in https://github.com/sgl-project/sglang/pull/22697
• @zsj555 made their first contribution in https://github.com/sgl-project/sglang/pull/23454

Full Changelog: https://github.com/sgl-project/sglang/compare/v0.5.10.post1...v0.5.11