Mixture of Experts (MoE) Explained

Short summary

Mixture of Experts (MoE) is a sparse activation architecture that routes input tokens to specialized subnetworks, allowing models to scale to trillions of parameters while keeping inference costs proportional to active parameters only. Core components include expert FFNs, a gating router, and top-k selection. Production implementations like Mixtral, DeepSeek-V3, and Switch Transformer demonstrate real-world benefits in scalability and per-request compute, though load balancing and infrastructure complexity remain challenges.

• •MoE routes tokens to specialized expert subnetworks via a gating network, activating only top-k experts per token
• •Enables trillion-parameter scale while keeping inference compute proportional to active parameters
• •Production examples include Mixtral, DeepSeek-V3, and Google's Switch Transformer

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