How Mixture of Experts Is Making Transformers More Efficient

Mixture of Experts (MoE) is a neural network architecture that scales up model capacity without proportionally increasing computational cost. In Transformer models, MoE replaces some feedforward layers with multiple expert networks and a gating mechanism that selects which experts to activate for each input token. This allows the model to specialize different parameters for different types of data, improving efficiency and performance. MoE has been successfully applied in large language models like Mixtral 8x7B and GPT-4, enabling them to handle diverse tasks while keeping inference costs manageable.