DMuon: Efficient Distributed Muon Training with Near-Adam Overhead

2026-06-25 · Source: Machine Learning · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Expert, quick

Summary

DMuon is an open-source distributed implementation of the Muon optimizer, designed to address the inefficiency of matrix-orthogonalization-based optimizers in modern distributed deep learning environments. While optimizers like Muon offer strong convergence and are compelling for large, heterogeneous models, their matrix-level updates and Newton-Schulz iterations make vanilla implementations over 2x slower than standard forward/backward passes. DMuon integrates as a drop-in module without framework modifications, achieving significant performance gains. It delivers a 1.48x-3.01x speedup in end-to-end step time and a 6.85x-163.00x speedup in optimizer-step time across embodied foundation model and large language model (LLM) training workloads, bringing per-step latency close to AdamW levels for efficient scaling.

Key takeaway

For MLOps Engineers or AI Scientists scaling large language models or embodied foundation models, DMuon offers a critical performance improvement. If your current distributed training setup struggles with the overhead of matrix-orthogonalization-based optimizers, integrating DMuon as a drop-in module can drastically reduce per-step latency to near-AdamW levels. This enables more efficient model scaling and faster experimentation cycles without requiring complex framework modifications.

Key insights

DMuon efficiently scales matrix-orthogonalization optimizers for distributed deep learning, achieving near-AdamW performance.

Principles

• Matrix-aware updates improve convergence for large models.
• Distributed training infrastructure favors element-wise optimizers.
• Optimizers can integrate without framework-level changes.

Method

DMuon integrates as a drop-in module into existing training pipelines, optimizing matrix-level updates to reduce the overhead of Newton-Schulz iterations in distributed environments.

In practice

• Apply DMuon for LLM training.
• Use DMuon for embodied foundation models.
• Integrate into existing PyTorch/TensorFlow pipelines.

Topics

• Distributed Training
• Deep Learning Optimizers
• Muon Optimizer
• Large Language Models
• Foundation Models
• Performance Optimization

Best for: AI Engineer, NLP Engineer, Computer Vision Engineer, AI Scientist, Machine Learning Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning.