Hyper Optimizing Kimi K3 in vLLM

Presents a hyper-optimized decode path for Kimi K3 in vLLM that replaces most operators with customized CuTe DSL kernels tuned for NVIDIA Blackwell (GB300) clusters of 8 or 16 GPUs, supporting decode batches from 1 to 64 and optional decode-context parallelism. The work delivers concrete gains - over 842 tokens/sec at batch 1 on 16 GB300 GPUs with DSpark (synthetic AL=6), 1.5-2× higher decode throughput across several configurations, and up to 1.8× better P90 interactivity on the AgentX workload. Microbenchmarks show per-layer kernel time reduced from about 130 µs and 23 kernels to ~69 µs and 13 kernels. Shape-tuned kernels (small-matrix vs split-K), fused attention-output projection with all-reduce, fence-less NVLink buffers, and two-stage reductions for larger batches are core performance levers, plus decode-context parallelism that splits KV caches for long contexts.

Improvements for MoE and memory-bound phases include splitting router and input-projection work across tensor-parallel ranks to eliminate redundant computation, running shared experts on separate streams to overlap work, and prefetching upcoming weights into cache via side-stream kernels. Equally important is the development process: agent-assisted kernel generation paired with a fast-track governance model that isolates model-specific changes, bounds supported hardware and workloads while preserving production features, mandates targeted numerical and end-to-end validation (including long-context and generation tests), and requires broad, reproducible performance sweeps run by contributors before maintainer review. The result is a faster, auditable decode path designed for maintainability and safer rapid contributions.