Building upon the architectural designs and training dynamics covered previously, this chapter concentrates on the practical necessity of scaling Mixture of Experts models for large-scale applications. The sheer size and unique sparse activation patterns of MoE models present distinct challenges compared to dense architectures, often rendering standard data parallelism insufficient on its own.

Here, you will learn distributed training strategies specifically adapted for MoEs. We will examine Expert Parallelism, a technique where individual experts within an MoE layer are distributed across different processing units. You will learn how to integrate this with existing Data Parallelism and Pipeline Parallelism strategies to manage both computational load and memory footprint effectively.

A significant portion of this chapter addresses the communication overhead inherent in MoE training, particularly the All-to-All communication step required to route token representations ($x$) to their assigned experts ($E_j$) possibly residing on different devices. Conceptually, this involves mapping tokens based on gating decisions ($g(x)$):

$$x_{\text{on device } i} \xrightarrow{g(x)} E_{j (\text{on device } k)}$$

We will discuss methods to optimize these communication patterns, such as computation-communication overlap. Additionally, we will touch upon software libraries and frameworks developed to facilitate the implementation of distributed MoE training. Practical exercises will involve configuring a distributed setup for an MoE model.