Using Muon Optimizer with DeepSpeed
DeepSpeed quietly integrating Muon Optimizer feels less like a feature checkbox and more like a tacit admission that AdamW, the long-reigning king of neural network training, might finally be showing its age. This isn’t just another optimizer plugin; it’s a validation wave from the frontier labs that actually matter—Moonshot AI, Zhipu AI, DeepSeek—who are voting with their compute budgets and paper titles. The real story isn’t in the DeepSpeed commit log; it’s in the glaring efficiency gap Muon
Analysis
DeepSpeed quietly integrating Muon Optimizer feels less like a feature checkbox and more like a tacit admission that AdamW, the long-reigning king of neural network training, might finally be showing its age. This isn’t just another optimizer plugin; it’s a validation wave from the frontier labs that actually matter—Moonshot AI, Zhipu AI, DeepSeek—who are voting with their compute budgets and paper titles. The real story isn’t in the DeepSpeed commit log; it’s in the glaring efficiency gap Muon exposes in the methods we’ve all taken for granted.
Let’s be blunt: Adam and its variants have been a magnificent, brute-force crutch. They work almost everywhere, but they’re wasteful. That dual-buffer state—storing both first and second moments—feels increasingly like a tax on scale. Muon, by contrast, is a leaner, more geometrically aware creature. Its core trick isn’t some incremental tweak to learning rate schedules; it’s the orthogonalization of the momentum matrix via Newton-Schulz iterations. This is where the real elegance lies. In standard training, gradient updates for crucial 2D weight matrices (think attention and MLP layers) are notoriously ill-conditioned—a few massive singular values dominate, drowning out the nuanced, low-rank signals that actually matter for generalization. Muon essentially performs a surgical equalization, amplifying those rare-but-vital update directions. It’s not just faster; it’s fundamentally smarter about where to spend its gradient budget.
The benchmarks are compelling, but almost beside the point. A 35% speedup on NanoGPT or hitting GPT-2 XL performance 25% faster is the kind of number that makes researchers’ eyes light up. The real headline is the memory savings. For a 1.6 trillion parameter model like DeepSeek-V4, halving the optimizer state memory for its 2D weight matrices isn’t a minor optimization—it’s a potential unlock for the next scale of model that would otherwise require a prohibitive number of accelerators. This isn’t about training a model slightly faster; it’s about whether a given architecture can feasibly be trained at all within a given cluster’s memory constraints. Muon changes the math of the entire scaling equation.
What’s fascinating is the social proof. We’re past the phase of arxiv papers and demo projects. When Zhipu AI’s GLM-5 (a 744B parameter beast) not only uses Muon but invents “Muon Split” to handle MLA architectures—orthogonalizing per attention head to close a performance gap with GQA—that’s not experimentation; that’s production engineering. This signals that Muon isn’t just a pretraining curiosity but a robust component capable of adaptation to novel, complex architectural choices. Moonshot’s use of a variant (MuonClip) in Kimi-K2 further cements this. These labs are investing engineering cycles to make Muon work with their flagship, money-making models. That tells you everything about its perceived value far more than any benchmark could.
The DeepSpeed integration itself, while technical, is the democratizing step. The challenge was real: DeepSpeed’s Zero stages handle gradients as flattened buffers, a format completely hostile to Muon’s need for 2D matrix structure. The solution—hijacking the get_flat_partition function in the DeepSpeedZeroOptimizer to apply the Muon update before flattening—is pragmatic and clever. It essentially sneaks Muon into the existing, battle-tested pipeline without requiring a full rewrite. This is how powerful ideas actually propagate: not by replacing infrastructure wholesale, but by being woven into it with surgical precision. Now, the legions of teams already locked into DeepSpeed can experiment with Muon without throwing away their orchestration.
However, let’s pump the brakes on the triumphalism. Muon’s current strength is explicitly tied to the hidden 2D weights. The notes mention that embeddings and layer norms (non-2D parameters) are not its target. This isn’t a universal Adam killer; it’s a specialist, a high-performance engine for a specific, critical part of the transformer anatomy. The art will be in the hybrid systems—knowing which parameters to hand to Muon and which to leave in the care of Adam or SGD. The early adopters are figuring this out, but for the broader community, it introduces a new layer of hyperparameter and architectural decision-making. Will we see a rise of “Muon-aware” architecture designs that maximize the proportion of eligible 2D weights? Almost certainly.
This development also subtly shifts the power dynamics of open-source AI. Tools like NanoGPT and nanochat, where Muon gained early traction, are proving grounds. When a method born in a speedrunning challenge gets adopted into the training pipeline of trillion-parameter flagship models, it demonstrates a remarkable pipeline from grassroots optimization to industrial scale. It suggests that the next great training algorithm might not come from a corporate research lab’s secret project, but from a clever tweak on a public leaderboard, validated by the community and then weaponized by the giants.
So, is the Muon Optimizer the new Adam? Not yet. Adam’s generality and simplicity are still powerful. But Muon represents a more disciplined, mathematically principled path forward for the specific, massive bottleneck of training foundation models. It’s a signal that the era of throwing ever-more memory and compute at the problem with blunt instruments is giving way to an era of precision. DeepSpeed’s support is the bridge that makes this precision accessible. The labs that have already crossed that bridge are training the next generation of AI. The rest of us are now scrambling to catch up, and the performance delta is starting to look like a canyon. The optimizer wars are no longer theoretical; they are happening in production, and Muon just fired the most convincing shot yet.
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