COAT (Compressing Optimizer States and Activations for FP8 Training) significantly reduces memory requirements when training large language models by optimizing both computation and storage.

Key Innovations:

• Extends FP8 optimization to both optimizer states and activations
• Achieves substantial memory footprint reduction compared to existing approaches
• Maintains model accuracy while improving computational efficiency
• Enables training of larger models with limited hardware resources

Engineering Impact: This framework represents a crucial advancement for organizations looking to train large language models with limited computing resources, potentially democratizing access to state-of-the-art AI development capabilities.

COAT: Compressing Optimizer states and Activation for Memory-Efficient FP8 Training