vLLM v0.28.0 adds Kimi-K3 optimizations, DeepSeek V4 sparse MLA support
vLLM v0.28.0 release notes detail major performance optimizations for Kimi-K3 and DeepSeek V4 inference, including new kernels, speculative decoding, and memory savings. The release includes 584 commits from 270 contributors, with features such as fused FlashKDA kernels, adaptive speculative token budgets, and sparse MLA support for DeepSeek V4.
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## 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), an