vLLM Delivers Day-0 Support for Kimi K3.1 Pro

Kimi K3.1 Pro is Moonshot AI's most capable open-weight model: a 2.8T-parameter (104B active) multimodal MoE with a 1M-token context that matches the strongest closed-source models on coding, agentic and knowledge benchmarks. The main change is attention: K3.1 Pro adds Sparse Attention (DSA) to all 24 full-attention layers for efficiency. vLLM supports it on day 0 on NVIDIA GPUs, AMD GPUs, Google TPUs and Alibaba T-Head Zhenwu PPUs. Sparse attention on a hybrid model: K3.1 Pro keeps K3's KDA + MLA hybrid and adds a DSA indexer to all 24 MLA layers. At long context, a decode step reads about a quarter of the bytes per layer that dense MLA reads. One KV cache for three kinds of state: vLLM packs each block's MLA latent, indexer keys and KDA state together. The packed cache runs with FlashInfer and FlashMLA sparse kernels with FP8 or NVFP4 KV and optional MXFP4 index keys, and P/D disaggregation transfers it unchanged. Token-sharded prefill attention: up to 1.69× faster time to first token (TTFT) than head-sharded prefill at 340K tokens on 8× GB300. Decode context parallelism (DCP) with a sparse indexer: an exact top-k merge with ~10× less traffic makes the attention module 1.3–1.5× faster…