Reducing Hardware-Related Interruptions In AI Clusters: Strategies For Resilient GPU Infrastructure

Abstract

Hardware-related interruptions represent a significant challenge to the stability, efficiency, and scalability of artificial intelligence infrastructure. This article examines the fundamental causes of hardware failures in GPU-based AI clusters and presents comprehensive strategies for enhancing resilience, exploring device-level variability, packaging stress, workload-driven aging, and environmental factors as primary contributors to reliability issues. The article details preventive measures, including left-shifted reliability screening, error detection mechanisms, adaptive telemetry, and thermal management, while system-level resilience frameworks featuring workload-aware redundancy, automated job migration, fleet-wide error correlation, and resource disaggregation are presented as critical approaches to mitigating hardware interruptions. Empirical data demonstrates that comprehensive resilience frameworks yield 17-24% improvements in computational efficiency, 31.5% higher return on infrastructure investments, up to 78.4% reduction in unplanned job terminations, and 73.6% reductions in recovery time, collectively saving approximately $1.74M annually per 10,000-GPU cluster.