Cursor open-sources Mixture-of-Kittens MoE megakernel for NVL72s
Cursor has open-sourced Mixture-of-Kittens (MoK), a production MoE training megakernel for NVL72s that fuses all MoE communication and computation into a single deterministic kernel. The MoE layer previously consumed over half of end-to-end training time for Composer, their agentic coding model, and MoK now powers Composer training across tens of thousands of GPUs. The code is available on GitHub.
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Today, we're open-sourcing Mixture-of-Kittens (MoK), our production MoE training megakernel for NVL72s. As we have scaled the training and inference of Composer, our agentic coding model, the mixture-of-experts layer has consistently remained the major bottleneck. Depending on the workload and training configuration, it can consume more than half of end-to-end training time. MoK addresses that bottleneck by fusing all MoE communication and computation into a single, fully deterministic kernel. It now powers Composer training across tens of thousands of GPUs. You can try MoK and explore the code on GitHub. We look forward to your feedback and contributions. MoK grew out of several earlier attempts to speed up the MoE layer. Over the past year, we wrote our own MXFP8 and NVFP4 training kernels and developed the "warp decode" approach for MoE inference. But those techniques optimized only the compute portion of the layer and assumed inter-GPU communication would be handled separately. In