vLLM v0.27.0 Release Notes Highlights This release features 561 commits from 242 contributors (64 new)! Kimi K3 support with a full stack landing in one release: core model files and kernels ( #50089 , #50000 ), Python ( #50093 ) and Rust ( #50104 ) frontends, AttnRes kernels ( #50090 ), DeepGEMM support ( #50458 ), compressed-tensors quantized checkpoints ( #50500 ), DSpark AR fusion ( #50242 ), and an option to shard the shared expert instead of replicating it ( #50656 ). More new models : Qwen3.5 text-only dense and MoE models ( #50210 ) with EVS video token pruning ( #48912 ), K-EXAONE-2.0-750B-A37B ( #50524 ), VaultGemma via the Transformers modeling backend ( #49803 ), and jina-embeddings-v5-text-nano ( #50688 ). PyTorch 2.13.0 upgrade along with torchvision 0.28.0 and Triton 3.7.1 ( #48155 ) — this is a breaking environment change; XPU ( #48677 ) and CPU ( #50412 ) followed to torch 2.13 as well. FlashAttention 4 integration deepens on SM100 : FP8 KV cache support ( #42569 ) and headdim-256 support ( #42669 ), backed by a new JIT warmup infrastructure ( #47451 ) and runner-owned Triton kernel warmup ( #49903 ) that remove first-request compilation stalls. DeepSeek-V4 performance push : sequence parallelism ( #46789 ), ~2x kernel improvement by skipping empty c128 launches ( #48957 ), 3.4% E2E TTFT from skipping unneeded topk/router ( #49486 ), 3.9% E2E TTFT from workspace reuse ( #49236 ), 1.88x kernel from removing a redundant full kernel ( #50298 ), adaptive topk width (1.0% E2E, #50004 ), 448 MiB GPU memory saved in the PP buffer ( #50312 ), a compact MXFP4 indexer KV cache ( #48993 ), and removal of sparse-MLA q-head padding on FlashInfer >= 0.6.14 ( #48047 ). Model Runner V2 expands to non-generative workloads : encoder-only attention ( #49331 ), sequence pooling for embedding/classification ( #48791 ), encoder token classification ( #50293 ) and token embedding ( #50574 ), BGE-M3 pooling ( #50661 ), multimodal on CPU ( #50073 ), a multi-layer MTP specu…
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