LatentMoE is a neural network architecture innovation designed to optimize Mixture-of-Experts (MoE) models by projecting token activations into a compact latent space before routing them to expert networks. This approach reduces memory bandwidth and communication overhead, enabling the use of more experts and higher routing capacity without increasing computational cost.