Mixture of Experts

Mixture of Experts (MoE) is a type of AI architecture that combines multiple distinct models ("experts") and selects and integrates the optimal output from the most suitable experts for each input — achieving high overall performance. It is particularly prominent in large-scale natural language processing models and multimodal AI, where it balances processing efficiency with flexibility. The main components of a Mixture of Experts architecture are: • Multiple Experts: A collection of models, each with different parameters and specialized for specific tasks or inputs • Gate (Router): A controller that dynamically determines which experts to activate and to what degree for a given input Rather than running all experts for every input, only the most suitable subset is activated per input — maintaining high performance while keeping computational costs down. Examples of notable models using Mixture of Experts: • GShard (Google) • Switch Transformer (Google) • M6 (Huawei/Chinese model) • Reported to be part of GPT-4's architecture Major application areas: • Distributed training of very large language models • Efficient knowledge partitioning in multi-task learning • Dynamic routing to task-specific expert models • Reducing inference costs and improving scalability Mixture of Experts is considered a key technical foundation for meeting the demands of increasingly large and diverse AI models — and is one of the core architectures for building AI that is both efficient and highly capable.