Training a Mixture of Experts model is one part of the process. Deploying these models for production inference introduces a distinct set of problems, primarily related to their large memory footprint and the computational patterns of sparse activation. While an MoE model's sparse nature keeps the training compute ($FLOPs$) manageable relative to its parameter count, this same property presents unique difficulties for low-latency serving.

This chapter provides practical methods for optimizing MoE models for inference. We will cover techniques for managing high memory usage, including offloading inactive experts to CPU memory or NVMe. You will learn to implement specialized batching strategies suited for sparse computation, apply model compression through quantization and distillation, and use speculative decoding to accelerate token generation. By the end, you will be able to construct an efficient inference pipeline for large sparse models.