vLLM Delivers Day-0 Support for Kimi K3.1 Pro
vLLM Delivers Day-0 Support for Kimi K3.1 Pro
vLLM announces day-0 support for Kimi K3.1 Pro, Moonshot AI's 2.8T-parameter (104B active) multimodal MoE with a 1M-token context that adds a DSA sparse-attention indexer to all 24 full-attention layers of its KDA + MLA hybrid. The post details a packed KV cache for three kinds of state, token-sharded prefill attention (up to 1.69× faster TTFT at 340K tokens on 8× GB300), decode context parallelism with an exact top-k merge, a fused low-latency decode path, a DSpark speculator preview, AgentX benchmark results on GB300 and Rubin, zero-mismatch RL, and support on NVIDIA, AMD, TPU and Alibaba T-HEAD Zhenwu PPUs.
It is a detailed engineering account of how sparse attention, hybrid KV cache layout and parallelism choices interact, with measured numbers for anyone serving long-context MoE models.
Source: vLLM Blog · vllm.ai