🟩NVIDIA Developer Blog•Stalecollected in 60m
Megatron Advances Optimizers for Faster LLM Training
💡Boost LLM training with Shampoo/Muon in Megatron—powers Kimi K2, GLM-5
⚡ 30-Second TL;DR
What Changed
Integrates Shampoo optimizer, effective for over a decade in neural nets
Why It Matters
These optimizers enable faster, more efficient LLM training, benefiting researchers and developers building competitive open-source models. It democratizes access to state-of-the-art training techniques via NVIDIA's framework.
What To Do Next
Integrate Muon optimizer into your Megatron pipeline to speed up LLM training on multi-node clusters.
Who should care:Researchers & Academics
Key Points
- •Integrates Shampoo optimizer, effective for over a decade in neural nets
- •Supports Muon (MomentUm Orthogonalized by Newton-Schulz) for top LLMs
- •Powers training of open-source models like Kimi K2 and GLM-5
- •Enhances LLM training speed via higher-order optimization in Megatron
🧠 Deep Insight
AI-generated analysis for this event.
🔑 Enhanced Key Takeaways
- •Shampoo utilizes preconditioning matrices to approximate second-order optimization, significantly reducing the number of training steps required compared to standard Adam-based approaches.
- •Muon specifically targets the weight matrices of linear layers by applying Newton-Schulz iterations to enforce orthogonality, which has been shown to improve convergence stability in high-parameter models.
- •The integration within Megatron-LM leverages custom CUDA kernels to mitigate the high computational overhead typically associated with calculating and inverting large preconditioning matrices.
📊 Competitor Analysis▸ Show
| Feature | NVIDIA Megatron (Shampoo/Muon) | DeepSpeed (Microsoft) | MosaicML Composer |
|---|---|---|---|
| Primary Optimization | Higher-order (Shampoo/Muon) | Adam/AdamW variants | Standard/Custom optimizers |
| Hardware Focus | NVIDIA GPU-specific (CUDA) | Agnostic (GPU/TPU/NPU) | Agnostic |
| Key Advantage | Extreme throughput for massive models | Ease of use/Memory efficiency | Training stability/Efficiency |
🛠️ Technical Deep Dive
- •Shampoo implementation: Uses Kronecker-factored preconditioning to approximate the inverse Hessian, reducing memory complexity from O(N^2) to O(N).
- •Muon implementation: Performs a Newton-Schulz iteration to compute the matrix inverse square root, ensuring the update direction remains orthogonal.
- •Memory overhead: Requires additional memory for storing preconditioning statistics; Megatron manages this via periodic updates rather than per-step updates to balance speed and accuracy.
- •Integration: Implemented as a drop-in optimizer class within the Megatron-LM training loop, compatible with existing Tensor Parallelism and Pipeline Parallelism configurations.
🔮 Future ImplicationsAI analysis grounded in cited sources
Standard AdamW will be deprecated as the default optimizer for foundation model pre-training.
The superior convergence rates of higher-order methods like Shampoo and Muon provide a clear efficiency advantage that outweighs the added implementation complexity.
Training costs for trillion-parameter models will decrease by at least 20% within 18 months.
The reduction in total training steps enabled by these optimizers directly translates to lower GPU-hour consumption for large-scale model development.
⏳ Timeline
2019-02
Initial publication of the Shampoo optimizer research paper.
2019-09
NVIDIA releases the first version of Megatron-LM for large-scale transformer training.
2024-10
Emergence of Muon optimizer research for neural network training.
2026-04
NVIDIA officially integrates Shampoo and Muon into the Megatron-LM framework.
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Original source: NVIDIA Developer Blog ↗