Summary
The rapid growth of large language models (LLMs) has enabled major advances in artificial intelligence (AI), including systems that assist with writing, coding, education, and decision-making. However, training these models demands enormous computing resources, creating significant challenges across multiple dimensions, including model quality, training time, energy efficiency, and reliability. Although many optimization techniques have been proposed, most focus on only one or a few aspects of training, leaving their overall impact on total training efficiency unclear. This project addresses t
