latent-moe

Using NVIDIA's Nemotron 3 Super and Moonshot AI's Kimi K3 as examples, this article analyzes how the LatentMoE architecture overcomes the efficiency bottleneck of traditional MoE by compressing the Expert computation dimension, and points out that this is a turning point for the next generation of MoE architectures.

Discussion of LatentMoE architecture with extreme sparsity (16/896 experts) and Kimi Delta Attention, claiming 2.5x more efficient scaling, and speculation about Kimi K3 model capabilities.

Sebastian Raschka points out the chain of inspiration from LatentMoE back to eigendecomposition through MLA, LoRA, and SVD.