The performance of Mixture-of-Experts (MoE) models is often attributed to the sheer number of experts, but a more critical factor is the stability of the routing mechanism. This analysis explores how MoE scaling involves a delicate balance between capacity, computation, memory, and communication. While increasing the FFN size in dense transformers is straightforward, it imposes a hard constraint: every token must pass through the same parameters. MoE offers a path to break this constraint, but only if the router can consistently direct tokens to the right experts. Unstable routing can lead to load imbalance, wasted computation, and degraded performance. For engineering teams, this means focusing on router design and training stability as much as on expert count. The insights are particularly relevant for those building or fine-tuning large-scale models where efficiency and scalability are paramount. Understanding these trade-offs can guide better architectural decisions and resource allocation.
A deep dive into Mixture-of-Experts architecture, arguing that routing stability is the key to scaling efficiency, not just adding more experts.