In modern LLM design, dense architectures are becoming commercially unviable for massive deployments. Mixture-of-Experts (MoE) addresses this bottleneck by dividing the network into specialized 'expert' feed-forward blocks. A dynamic routing layer directs tokens only to the relevant experts, significantly reducing active parameter count per forward pass.
This sparse activation model allows a model with 400 billion parameters to only execute, for example, 90 billion parameters per token. The hardware savings are immediate: lower latency, higher throughput, and reduced power consumption at the GPU level. MoE has effectively decoupled model size from execution cost.
However, MoE introduces massive engineering challenges in memory management. Because the entire model must reside in VRAM, hosting an MoE model requires high-capacity GPU setups, even if only a fraction of parameters are active at once. The focus of hosting providers has shifted from computing power to memory bandwidth optimization.
