How does mixture-of-experts (MoE) architecture improve ChatGPT scalability?

MoE activates only a subset of model parameters per input, improving scalability while maintaining high capacity.

Mixture-of-Experts (MoE) architecture divides a large model into multiple expert sub-networks. A gating network dynamically selects a small subset of experts for each input token. This allows models to scale to trillions of parameters while only activating a fraction during inference.

This reduces compute cost while preserving representational power. However, it introduces routing complexity, load imbalance, and training instability if experts are not evenly utilized.

MoE is commonly used in large-scale LLM research systems to improve efficiency.