SGLang 发布: v0.5.14

来源摘要

# 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