FrontierAugust 4, 2026via MarkTechPost
Cursor Open-Sources Mixture-of-Kittens (MoK): A Deterministic MoE Training Megakernel for GB300 NVL72 Racks
Why it matters
A significant optimization in frontier model training (MoE efficiency gains), but with a harsh availability gate: the kernel requires Blackwell-class GPUs that most practitioners cannot access. Relevant to labs training large models and to the broader frontier-lab race on training efficiency; less immediately actionable for most practitioners.
Key signals
- Cursor Research open-sourced Mixture-of-Kittens (MoK)
- MoE training megakernel achieves 2.37x speedup vs. public baseline
- Fuses MoE communication and computation into single deterministic kernel
- Requires Blackwell SM100 or SM103 GPUs (NVL72 racks only)
- Powers Cursor's Composer models
- Published August 4, 2026
The hook
Cursor open-sources MoK, a 2.37x faster MoE training kernel—but only for teams with NVL72 racks.
Cursor Research has open-sourced Mixture-of-Kittens (MoK), the MoE training megakernel behind its Composer models. MoK fuses all mixture-of-experts communication and computation into a single deterministic kernel, and runs up to 2.37x faster than the strongest public baseline on GB300 NVL72 racks. I…