Scaling Llama4 Training to 100K by Saif Hasan

In this monologue, Saif Hasan discusses the challenges and solutions related to scaling pre-training to 100,000 GPUs for large language models (LLMs). He explains the typical training lifecycle, highlighting the increasing initialization time and hardware failures that lead to inefficient GPU utilization. Saif then details two key approaches to optimize effective training time: scaling collective initialization through TCP-based ring formation and bidirectional all-gather, and implementing fault-tolerant training using model replicas and a global coordinator to allow training to continue despite individual GPU failures. He also touches on the use of an emulation framework to overcome GPU resource limitations and the surprises encountered during large-scale testing, emphasizing the importance of rapid recovery and innovative training paradigms for future LLM development.