Silent Errors in Large-Scale LLM training by Cyril Meurillon & Devin O'Kelley

Cyril and Devin from NVIDIA discuss Silent Data Corruptions (SDCs), hardware faults that can cause errors in AI model training, potentially leading to catastrophic outcomes, especially in critical applications like autonomous vehicles. They explain how SDCs originate from quantum physics at the transistor level and propagate through the GPU's complex architecture, affecting AI models in unpredictable ways. NVIDIA employs a multi-tiered strategy to mitigate SDCs, including rigorous factory testing, data center validation, continuous health monitoring, and runtime detection methods like checksum verification. They emphasize a virtuous cycle of learning from failures to improve future GPU designs, aiming to enhance the reliability of data centers and AI applications.