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Elliot Arledge explains why he prefers using a Kimi Linear megakernel over Qwen 3.6 for kernel performance, comparing parameter counts, layer synchronization, hidden dimensions, and architecture-specific optimizations. The discussion highlights that Kimi Linear architecture is more suitable for megakernel implementation, especially for batch-1 decode on RTX PRO 6000 Blackwell.
MusaCoder trained a 27B model that achieves 93.2 Pass@8 on KernelBench, outperforming Claude Opus (87.2), and a 5-page technical summary of its data pipeline, verifier, and RL stabilizers is provided.