From Detection to Recovery: Operational Analysis on LLM Pre-training with 504 GPUs

The report presents an operational analysis of a 63-node NVIDIA B200 cluster utilizing 504 GPUs for large-scale AI training, based on 55 days of Prometheus data and 73 days of operational logs. Key findings include the identification of a storage I/O bottleneck in production, the performance metrics of checkpoint events showing significant read/write bandwidth utilization, and a notable improvement in auto-retry success rates compared to manual attempts. This analysis underscores the importance of multi-signal detection and robust monitoring in optimizing distributed training environments, providing valuable insights for practitioners managing large-scale LLM training.