Sparse Mixture of Experts (Sparse MoE) is a neural network architecture pattern designed to improve model efficiency by selectively activating only a subset of specialized subnetworks, known as experts, for each input token. This approach enables large language models to scale their parameter count significantly without proportionally increasing inference or training costs.