vLLM 发布: v0.28.0

来源摘要

# v0.28.0 ## Highlights This release features 584 commits from 270 contributors (76 new)! * **Kimi-K3 performance push**: a major optimization effort for Kimi-K3 across the stack — Decode Context Parallel (DCP) support (#50484), fused FlashKDA decode and prefill kernels (#50654, #51311, #52458), SiTU activation support for MegaMoE (#50510), GEMM-RS for sequence parallelism (#52079), combined all-gathers with 1.5~3x kernel-level speedup (#51070), an adaptive speculative token budget delivering ~60% better DSpark TTFT (#51725), and optional shared-expert sharding saving ~17 GiB of memory per GPU (#50912). Kimi-K3 also now runs on ROCm with the V2 model runner (#51653). * **DeepSeek V4**: sparse MLA now works end-to-end for plain decode, MTP, and DSpark speculative decoding (#51538), joined by AMD Quark NVFP4 support (#47972), reasoning-effort prompts and mappings (#50580), sparse top-k metadata kernel optimizations (#52084, #51967), narrowed eager CUDA graph regions (#51430, #52401), and ROCm enablement on gfx11 and gfx950 (#47017, #52212). * **Speculative decoding advances**: DFlash2 with local convolution and a candidate selector (#52816), DSpark confidence-scheduled verification (#47808), and async scheduling auto-enabled for draft models (#48341). * **Model Runner V2 maturation**: E/P/D disaggregation (#38390), weight offloading (#51413), multi-layer MTP KV cache support (#50062), encoder CUDA graphs (#49852), decoder token-wise pooling (#50931) plus Transformers pooling models (#52425), attention-free models (#52374), and `thinking_token_budget` support (#46727). * **Tiered KV cache offloading**: disk offloading support (#49644), out-of-tree secondary tier managers via `module_path` (#51007), partial secondary-tier load results (#50321), tiering metrics (#48798), and