Comprehensive guide and practical tutorial on MoE model training using Megatron-LM

On 2026-11-06 03:01, a post titled "MoE 模型训练全攻略：基于 Megatron-LM 的实操指南" appeared on Reddit's LocalLLaMA forum, offering developers a hands-on guide to training mixture-of-experts models on the Megatron-LM framework. The handbook is aimed at people who have already trained a dense model and want to train an MoE next, as well as those who are curious how MoE training works. The post is structured as a manual, providing code configurations based on Megatron-LM along with recommended practices for each component.

The guide is organized into two chapters. Chapter 2 serves as a Megatron-LM runbook, walking through testing the hardware, building the image, training a dense baseline model, and then running a first MoE on a single node so it can be compared against that baseline. Chapter 3 takes the MoE layer apart, covering the router, routing, load balancing, experts, block layout, training, and systems considerations. For each part, the author provides the corresponding Megatron-LM code, notes on what to watch for, their own recommendation, and an overview of the current frontier. Overall, the guide is designed to help developers move smoothly from dense models to MoE training. As of now, this is the only report available, and there have been no further developments.