Mixture-of-Kittens: MoE Megakernel for NVL72s
AI accelerator systems are rapidly consolidating into scale-up architectures, where tens to thousands of GPUs communicate over high-bandwidth, single-hop fabrics. We find that existing Mixture-of-Experts (MoE) training systems, optimized for conventional scale-out networks, transfer poorly to this setting, often running slower than a naive baseline built with PyTorch and NCCL. With industry roadmaps pointing toward even larger scale-up domains, understanding the performance tradeoffs of this hardware regime is increasingly important. We present Mixture-of-Kittens (MoK), an MoE training system designed for Nvidia NVL72. MoK builds on three insights that unlock performance on scale-up domai...
| Authors | Stuart H. Sul, Nash Brown, Henry Wildermuth et al. |
|---|---|
| Primary category | cs.DC |
| Published | 28 September 2026 |
| Last updated at source | 28 September 2026 |
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