# SGLang v0.5.14

- Product: SGLang (https://whatsnew.fyi/product/sglang)
- Vendor: LMSYS
- Date: 2026-06-26
- Version: v0.5.14
- Original notes: https://github.com/sgl-project/sglang/releases/tag/v0.5.14
- Permalink: https://whatsnew.fyi/product/sglang/releases/v0.5.14

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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- **added** — Support for GLM-5.2 model
- **added** — Support for LiquidAI LFM2.5 model
- **added** — Support for Kimi-K2.7-Code model
- **added** — Support for Poolside Laguna-M.1 model
- **added** — Support for DiffusionGemma model
- **added** — Support for Zyphra ZAYA1 model
- **added** — Support for MiMo-V2-ASR model
- **added** — Waterfill and LPLB MoE load balancing methods for DeepSeek expert parallelism
- **added** — KDA CuteDSL prefill kernel on Blackwell (SM100) with 1.08-1.52x speedup over Triton path
- **added** — Int8 checkpoint pool for Mamba radix cache to increase prefix-cache capacity for KDA and GDN models
- **added** — Linear-programming load balancer for MoE expert parallelism via --ep-dispatch-algorithm=lp option
- **added** — MSCCL++ integration with auto-tuned collectives for TP=8 single-node and TP=16 two-node
- **added** — FlashInfer fused allreduce, residual, and RMSNorm via --flashinfer-allreduce-fusion-backend option
- **added** — Data-parallel attention and MTP support for Nemotron-H hybrid model
- **added** — NVFP4 MoE quantization path for DeepSeek-V4 on Blackwell via --moe-runner-backend flashinfer_trtllm_routed
- **changed** — Speculative conv-window intermediate cache deduplicated with sliding-window layout, halving footprint
- **changed** — FP8 group quantization now emits power-of-two (UE8M0) scales directly from per-token group-quant kernel
- **changed** — MLA decode q-heads padded to 64 under attention-TP to dispatch cheaper head64 kernel
- **changed** — Breakable CUDA graph execution path now runs on AMD GPUs
- **fixed** — MHC prenorm kernel prewarmed at startup to remove first-run JIT slowdown on fresh server

#### Highlights

New Model Support: [GLM-5.2](https://docs.sglang.io/cookbook/autoregressive/GLM/GLM-5.2), [LiquidAI LFM2.5](https://docs.sglang.io/cookbook/autoregressive/LiquidAI/LFM2.5), [Kimi-K2.7-Code](https://docs.sglang.io/cookbook/autoregressive/Moonshotai/Kimi-K2.7-Code), [Poolside Laguna-M.1](https://docs.sglang.io/cookbook/autoregressive/Poolside/Laguna-M.1), [DiffusionGemma](https://docs.sglang.io/cookbook/autoregressive/Google/DiffusionGemma), Zyphra ZAYA1, MiMo-V2-ASR

**DeepSeek-V4 on GB300 since Day 0**: 5x higher throughput at the same interactivity, serving DeepSeek-V4 on NVIDIA GB300 with SGLang ([blog](https://pytorch.org/blog/serving-deepseek-v4-on-gb300-with-sglang-5x-higher-throughput-at-the-same-interactivity-since-day-0/)).

**Waterfill & LPLB MoE load balancing**: Two dispatch-time load-balancing methods for DeepEP expert parallelism: Waterfill for shared-expert dispatch and LPLB for redundant expert replicas, improving throughput for DeepSeek-V3/R1 and DeepSeek-V4 ([blog](https://www.lmsys.org/blog/2026-06-26-waterfill-lplb)).

**KDA CuteDSL prefill kernel on Blackwell (SM100)**: New CuteDSL prefill kernel for Kimi-Linear (KDA), 1.08-1.52x faster than the Triton path via a reusable scratch workspace, plus a cuda-graph padding fix ([#27488](https://github.com/sgl-project/sglang/pull/27488)); see the [Kimi-Linear cookbook](https://docs.sglang.io/cookbook/autoregressive/Moonshotai/Kimi-Linear).

**Linear-attention prefix-cache memory savings**: An int8 checkpoint pool stores recurrent states compactly in the Mamba radix cache, substantially increasing prefix-cache capacity for KDA / GDN models ([#28185](https://github.com/sgl-project/sglang/pull/28185)); the speculative conv-window intermediate cache is deduplicated with a sliding-window layout, halving its footprint with no numerical change ([#28302](https://github.com/sgl-project/sglang/pull/28302)).

**LPLB: linear-programming load balancer for MoE expert parallelism**: Balances token routing across redundant expert replicas by solving a per-layer LP; opt-in via `--ep-dispatch-algorithm=lp`, default behavior unchanged ([#24515](https://github.com/sgl-project/sglang/pull/24515)).

**MSCCL++ integration & MNNVL allreduce fusion**: MSCCL++ migrates to the upstream `mscclpp` Python package (Executor + DSL compiler) with auto-tuned collectives for TP=8 single-node and TP=16 two-node ([#22734](https://github.com/sgl-project/sglang/pull/22734)); FlashInfer fused allreduce + residual + RMSNorm re-enables an MNNVL backend behind `--flashinfer-allreduce-fusion-backend` (`auto` / `trtllm` / `mnnvl`), fixing the piecewise-CUDA-graph interaction ([#23402](https://github.com/sgl-project/sglang/pull/23402)).

**Nemotron DP attention + MTP**: Data-parallel attention for the hybrid Nemotron-H (Mamba2 + full attention + MoE), plus MTP support ([#24955](https://github.com/sgl-project/sglang/pull/24955)); see the [Nemotron 3 Ultra cookbook](https://docs.sglang.io/cookbook/autoregressive/NVIDIA/Nemotron3-Ultra).

**AMD: breakable CUDA graph on ROCm/HIP**: The breakable CUDA graph execution path now runs on AMD GPUs ([#28173](https://github.com/sgl-project/sglang/pull/28173)).

**NVFP4 MoE for DeepSeek-V4**: Adds an NVFP4 MoE quantization path for DeepSeek-V4 on Blackwell for higher MoE throughput; enable with `--moe-runner-backend flashinfer_trtllm_routed` ([#25820](https://github.com/sgl-project/sglang/pull/25820)); see the [DeepSeek-V4 cookbook](https://docs.sglang.io/cookbook/autoregressive/DeepSeek/DeepSeek-V4).

**DeepSeek-V4 decode & quantization optimizations**: FP8 group quantization now emits power-of-two (UE8M0) scales directly from the per-token group-quant kernel, dropping a separate rounding pass ([#26766](https://github.com/sgl-project/sglang/pull/26766)); MLA decode q-heads are padded to 64 under attention-TP so FlashMLA dispatches the ~2x cheaper head64 kernel instead of head128 ([#27954](https://github.com/sgl-project/sglang/pull/27

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