Cursor Open-Sources Mixture-of-Kittens (MoK): A Deterministic MoE Training Megakernel for GB300 NVL72 Racks
Cursor Research has open-sourced Mixture-of-Kittens (MoK), the mixture-of-experts training megakernel behind its Composer models. MoK fuses every MoE communication and computation step into a single deterministic kernel. Cursor team reports up to 2.37x higher throughput than the strongest public baseline. It already powers Composer training across tens of thousands of GPUs. Is it deployable Yes, but the hardware floor is high. MoK is on GitHub under Apache-2.0. It requires NVIDIA Blackwel
Mixture-of-Kittens (MoK) is an open-source training system that combines all communication and computation steps for mixture-of-experts models into a single deterministic kernel, achieving up to 2.37x higher throughput than existing public alternatives. The system addresses a critical bottleneck where the MoE layer can consume more than half of end-to-end training time by using pull-based dispatch and a ring token buffer to minimize CPU-GPU synchronization. Deployment is limited to organizations with access to specific high-end hardware, namely NVIDIA Blackwell GPUs in NVL72 racks, making it practical only for frontier labs, funded startups, GPU cloud providers, and national computing centers. The technology is relevant for training large mixture-of-experts models and on-policy reinforcement learning applications.
Explainer: What Is a Mixture of Experts Model and Why Does It Use Fewer Resources?
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