Advancing Emerging Optimizers for Accelerated LLM Training with NVIDIA Megatron

Higher-order optimization algorithms such as Shampoo have been effectively applied in neural network training for at least a decade. These methods have achieved significant success more recently when applied to leading LLMs. In particular, Muon (MomentUm Orthogonalized by Newton-Schulz) was used to train some of today’s best open source models, including Kimi K2 and GLM-5. This post explains how NVIDIA provides comprehensive support for Muon and other cutting-edge emerging optimizers and the technologies enabling them to train large-scale models. Muon training performance on NVIDIA GB300 NVL72 Table 1 summarizes training throughput of the Kimi K2 and Qwen3 30B models with Muon and the AdamW optimizer on the NVIDIA GB300 NVL72 system. With the technologies that will be introduced in the next section, the results show that there is a very small training performance loss using the Muon optimizer compared to…