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A social media post summarizing academic literature on megakernels, shared by user @charles_irl.
Cursor open-sources Mixture-of-Kittens (MoK), a deterministic MoE training megakernel for NVIDIA Blackwell GPUs that fuses computation and communication, delivering up to 2.37x speedup over baseline implementations.
Cursor is open-sourcing Mixture-of-Kittens (MoK), a production MoE training megakernel for NVL72s that fuses communication and computation, delivering a 1.41x end-to-end training throughput improvement for their Composer model.
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.
AutoMegaKernel is an open-source agent harness that compiles any HuggingFace model into a single persistent megakernel, fusing the entire forward pass into one GPU launch to reduce overhead. It achieves up to 1.33x speedup over CUDA-graphed cuBLAS on inference-class GPUs like L4 and L40S, while proving schedules deadlock- and race-free.
Luce Megakernel demonstrates that NVIDIA's efficiency gap is a software problem, achieving 1.8x throughput on an RTX 3090 compared to llama.cpp and matching Apple M5 Max efficiency at lower cost.
This paper introduces Ada-MK, an adaptive MegaKernel optimization method that uses automated DAG-based search to eliminate runtime branching and reduce shared memory usage for LLM inference. It demonstrates significant throughput improvements on NVIDIA Ada GPUs by integrating with TensorRT-LLM, achieving up to 23.6% faster performance than vanilla TensorRT-LLM in commercial advertising systems.