# SGLang v0.5.17

- Product: SGLang (https://whatsnew.fyi/product/sglang)
- Vendor: LMSYS
- Date: 2026-08-08
- Version: v0.5.17
- Original notes: https://github.com/sgl-project/sglang/releases/tag/v0.5.17
- Permalink: https://whatsnew.fyi/product/sglang/releases/v0.5.17

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** — Kimi K3 day-0 support with DCP, DSpark speculative decoding, chunked-prefill PP with TP decode, KDA-aware prefix caching, HiCache L2 over DCP, LoRA on quantized weights, and reasoning, tool-call and OpenAI-compatible serving
- **added** — MiniMax-H3 video generation model support on SGLang-Diffusion with text-to-video-and-audio, first/last-frame conditioning, and image/video/audio reference conditioning
- **added** — EmbeddingGemma and LFM2.5 embedding models support
- **added** — Initial support for Rust frontend migrating network ingress and tokenized request handling to GPU scheduler from Python
- **added** — DCP communication backends with a2a, fi_a2a, and ag_rs options plus q-replicate for full-head Q projection
- **added** — DWDP for MoE prefill strategy that prefetches peer expert weights over NVLink P2P and computes all experts locally
- **added** — Session-reference-aware Unified Radix Cache for agentic and RL-rollout workloads with session_id tracking and /close_session endpoint
- **added** — SM90 FP8 MegaMoE for DeepSeek-V4 with DeepGEMM A2A path and FP8 expert weight preparation
- **added** — Cookbook recipes for Laguna-S-2.1 family and Inkling-Small models
- **changed** — Large-MoE model loading performance improved by copying oversized or non-contiguous CPU weight views into contiguous storage before H2D transfer, achieving 5.6x faster loading for DeepSeek-V4-Pro and 7.8x for GPT-OSS-20B
- **changed** — DeepSeek-V4 memory footprint on AMD reduced by removing unnecessary expert padding and moving HIP compress-state pool into memory_saver KV_CACHE region
- **changed** — Engine recovery performance improved with weight-cache daemon holding weights per GPU for faster restart recovery
- **added** — nvidia/MiniMax-M3-NVFP4 model support

#### Highlights

*582 PRs from 194 contributors.*

**Kimi K3 day-0 support**: A 2.8T-parameter multimodal LatentMoE (896 experts, top-16, routed in a 3584-dim latent space) with a 1M-token context, 69 KDA linear-attention layers interleaved with 24 MLA layers, and a MoonViT3d vision tower, shipping as a native MXFP4 checkpoint. SGLang serves it from day 0 with DCP, DSpark speculative decoding, chunked-prefill PP with TP decode, KDA-aware prefix caching, HiCache L2 over DCP, LoRA on the quantized weights, and reasoning, tool-call and OpenAI-compatible serving, verified on NVIDIA GB300 and AMD MI35x ([#32541](https://github.com/sgl-project/sglang/pull/32541), [#32828](https://github.com/sgl-project/sglang/pull/32828), [#32890](https://github.com/sgl-project/sglang/pull/32890), [#33025](https://github.com/sgl-project/sglang/pull/33025), [#33112](https://github.com/sgl-project/sglang/pull/33112), [blog](https://www.lmsys.org/blog/2026-07-27-kimi-k3-day0-support), [cookbook](https://docs.sglang.io/cookbook/autoregressive/Moonshotai/Kimi-K3), [roadmap](https://github.com/sgl-project/sglang/issues/32607)).

**MiniMax-H3 day-0 support**: MiniMax's video generation model that produces a video and a synchronized stereo audio track in one request, served natively on SGLang-Diffusion across all three public task profiles: text-to-video-and-audio (`t2va`), first/last-frame conditioning (`fl2va`), and image/video/audio reference conditioning (`ref2va`, which also covers video-to-video). Verified on B200 (TP2 + Ulysses4), H100 (TP2 + Ulysses2), AMD MI300X and MI355X (Ulysses1/2/4/8), and 2x RTX 5090 with layerwise offload ([#33275](https://github.com/sgl-project/sglang/pull/33275), [cookbook](https://docs.sglang.io/cookbook/diffusion/MiniMax/MiniMax-H3)).

**Other new models added**: [EmbeddingGemma](https://docs.sglang.io/cookbook/autoregressive/Google/EmbeddingGemma) and [LFM2.5](https://docs.sglang.io/cookbook/autoregressive/LiquidAI/LFM2.5) embedding models, nvidia/MiniMax-M3-NVFP4, plus cookbook recipes for Poolside's [Laguna-S-2.1](https://docs.sglang.io/cookbook/autoregressive/Poolside/Laguna-S-2.1) family and [Inkling-Small](https://docs.sglang.io/cookbook/autoregressive/ThinkingMachines/Inkling-Small).

**Initial support for the Rust frontend**: Migrates the front half of the server, everything from network ingress up to the point a tokenized request is handed to the GPU scheduler, from Python to a multi-threaded Rust implementation ([#29799](https://github.com/sgl-project/sglang/pull/29799)).

**DCP communication backends and q-replicate (Helix)**: The DeepSeek-MLA decode context-parallel path gains pluggable comm backends. `a2a` exchanges packed attention output plus fp32 LSE in a single NCCL collective per layer, with fp8 KV carried as uint8 byte transport; `fi_a2a` delegates the cross-rank exchange to the FlashInfer MNNVL kernel on GB200. `--dcp-replicate-q-proj` projects full-head Q locally and skips the per-layer Q head-dim all-gather. Select with `--dcp-comm-backend {ag_rs, a2a, fi_a2a}` ([#21637](https://github.com/sgl-project/sglang/pull/21637)).

**DWDP for MoE prefill**: A new prefill parallelism strategy that prefetches peer expert weights over NVLink P2P and computes all experts locally, removing EP all-to-all token dispatch. On 4x B200 with gpt-oss-120b, prefill-only, DWDP4 reaches **1.92x over DEP4** at MNT 32K / ISL 32K, and **506K vs 329K tok/s (1.54x)** at saturation (CONC=128, ISL=8K). Enable with `--dwdp-size`; the authors mark it early-development ([#29778](https://github.com/sgl-project/sglang/pull/29778)).

**Session-reference-aware Unified Radix Cache**: For agentic and RL-rollout workloads, requests can carry a stable `session_id` so eviction knows which prefixes an active session still references, instead of evicting purely by cache policy. Release the references with `/close_session`. Opt in with `--enable-session-radix-cache` ([#29173](https://github.com/sgl-project/sglang/pull/29173)).

_[Truncated at 4000 characters — full notes: https://github.com/sgl-project/sglang/releases/tag/v0.5.17]_