Moonshot’s Kimi K3 report says sparse MoE and delta attention beat bigger dense models
SumitGup · x · 2026-07-26
Moonshot AI’s Kimi K3 technical report argues that bigger dense models are not the future, and instead highlights five design ideas: sparse routing, delta attention, attention residuals, a 1M-token context window, and efficient scaling.
- Sparse MoE routing: 896 experts exist, but only 16 are activated per token.
- Delta attention: focuses computation on what changed instead of recomputing full context.
- Attention residuals: preserve useful signal across layers.
- 1M context: aims to fit entire repositories, books, and papers in one session.
- Efficient scaling: targets frontier coding performance without frontier inference costs.
Related event: Moonshot AI Unveils Trillion-Parameter Kimi K3(4 posts)→
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