Scalable Training of Mixture-of-Experts Models with Megatron Core

TL;DR

This paper presents a comprehensive system for scalable training of Mixture-of-Experts models using Megatron Core, addressing memory, communication, and computation challenges to enable efficient training of very large models.

Contribution

It introduces integrated system optimizations and a flexible parallelism framework for efficient, scalable MoE training on large GPU clusters, with open-source implementation.

Findings

Achieves over 1,200 TFLOPS/GPU on large models

Supports low-precision training (FP8, NVFP4)

Enables training of models with trillions of parameters

Abstract

Scaling Mixture-of-Experts (MoE) training introduces systems challenges absent in dense models. Because each token activates only a subset of experts, this sparsity allows total parameters to grow much faster than per-token computation, creating coupled constraints across memory, communication, and computation. Optimizing one dimension often shifts pressure to another, demanding co-design across the full system stack. We address these challenges for MoE training through integrated optimizations spanning memory (fine-grained recomputation, offloading, etc.), communication (optimized dispatchers, overlapping, etc.), and computation (Grouped GEMM, fusions, CUDA Graphs, etc.). The framework also provides Parallel Folding for flexible multi-dimensional parallelism, low-precision training support for FP8 and NVFP4, and efficient long-context training. On NVIDIA GB300 and GB200, it achieves 1,233/1,048 TFLOPS/GPU for DeepSeek-V3-685B and 974/919 TFLOPS/GPU for Qwen3-235B. As a performant, scalable, and production-ready open-source solution, it has been used across academia and industry for training MoE models ranging from billions to trillions of parameters on clusters scaling up to thousands of GPUs. This report explains how these techniques work, their trade-offs, and their interactions at the systems level, providing practical guidance for scaling MoE models with Megatron Core.