Mixture of Experts (MoE) is a neural network architecture that divides computation across specialized sub-networks rather than routing all inputs through the entire network. Think of it as a specialized router connecting incoming data to the most relevant processing units, similar to how a network switch directs packets to specific destinations.
The architecture consists of three key components: expert networks, a gating/routing network, and a combination mechanism. The expert networks are specialized neural sub-networks, each potentially focusing on different aspects of the task. The gating network examines each input and determines which experts should process it. The combination mechanism then integrates the outputs from the activated experts to produce the final result.
In practical implementations like DeepSeek-R1, which has 671B total parameters but activates only 37B per forward pass, the MoE architecture enables significant efficiency gains. When processing an input, the router activates only a small subset of experts (typically 1-2% of parameters), allowing the model to maintain the benefits of a large parameter count while keeping computational costs manageable.
The architecture supports sparsity through conditional computation – not all parameters need to be active for every input. This enables models to scale to much larger sizes than dense architectures while maintaining reasonable inference costs. Each expert can specialize in particular types of computation, whether that’s mathematical reasoning, language understanding, or logical deduction.
MoE architectures typically implement load balancing and capacity factors to ensure efficient utilization of experts and prevent bottlenecks. Modern implementations use sophisticated routing algorithms that consider both input characteristics and expert availability, often employing techniques like top-k routing and auxiliary load balancing losses during training.