The development of large language models (LLMs) has required a significant shift in computation scale, moving from many smaller models trained on a few GPUs to fewer, very large models. This change has necessitated a reevaluation of hardware, software, and network infrastructure to support generative AI (GenAI) at scale. Challenges such as hardware reliability, fast recovery on failure, efficient data preservation, and optimal GPU connectivity have been addressed through innovations in both training software and hardware, as well as data center deployment strategies. The future will bring even more significant challenges, pushing the boundaries of AI research and development.
https://engineering.fb.com/2024/06/12/data-infrastructure/training-large-language-models-at-scale-meta/