Expert Parallelism

Expert parallelism is an inference and training architecture for large mixture-of-experts models. The individual expert networks are split across multiple GPUs, servers, or inference nodes instead of being fully replicated everywhere. For each token, a router selects the experts that should run; the platform then has to move the request to those experts, combine their outputs, and keep the whole path within the latency budget. This is what makes very large sparse models practical: only a small part of the model is active for a given token, while the full set of experts can remain distributed across the infrastructure. The trade-off is coordination. Memory pressure drops, but network traffic, batching, expert placement, and load balancing become first-order production concerns. In real deployments, expert parallelism is therefore not just a model-serving detail. It is the operating model that determines whether a MoE system can be fast, reliable, and cost-efficient under real workload patterns.