Safeguarding LLM Training at Scale: Online SDC Detection and Insights from 35 Million GPU Hours

Silent Data Corruption (SDC) poses a critical threat to large-scale LLM training. Existing offline tests and online detection methods provide solutions for large-scale systems, yet they suffer from high overhead or low detection accuracy in LLM training. This paper presents AEGIS, an online SDC detection framework for large-scale LLM training. We introduce a two-stage cSensor-cVerifier abstraction that decouples SDC detection into lightweight corruption sensing and definitive corruption verification. Based on this abstraction, AEGIS co-designs new detection techniques by integrating the inherent features of LLM training with GPU characteristics, enabling practical online SDC detection. In a production deployment spanning 3.5 × 10 7 GPU-hours, AEGIS identified 18 real-world SDC incidents and 13 faulty GPUs while incurring only 0.86% performance overhead, enabling a systematic empirical characterization of SDCs in large-scale LLM training.