Navigating the Complexity of LLM Training

This chapter explores the scale and intricacies involved in training frontier models, particularly the use of vast GPU clusters exceeding 100,000 units. It highlights the extensive time, cost, and effort required for both training and fine-tuning large language models, including the critical need for effective experiment tracking and model monitoring. The discussion also underscores the lessons that traditional enterprises can learn from the practices of teams specializing in LLM operations.