How Meta keeps its AI hardware reliable

What this post added

This post details Meta's methodologies for detecting and mitigating hardware faults, particularly silent data corruptions (SDCs), within its AI infrastructure. It highlights the unique challenges SDCs pose for AI training (e.g., NaN propagation, corrupted gradient variance) and inference workloads, and introduces novel detection mechanisms like Fleetscanner, Ripple, and Hardware Sentinel to ensure the reliability of AI hardware at scale.