Pangu Ultra demonstrates significant engineering advances in training massive language models efficiently on custom hardware.

• Implements depth-scaled sandwich normalization to stabilize training of very deep models
• Optimizes performance on Ascend Neural Processing Units for large-scale model training
• Scales to 135 billion parameters while maintaining training stability
• Establishes technical framework for efficient training of future massive language models

This research matters because it addresses core engineering challenges in LLM development, showing how specialized hardware and optimization techniques can support the next generation of large language models.

Pangu Ultra: Pushing the Limits of Dense Large Language Models on Ascend NPUs