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

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 workloadaware 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. [ABSTRACT FROM AUTHOR]

Copyright of Journal of International Crisis & Risk Communication Research (JICRCR) is the property of Journal of International Crisis & Risk Communication Research and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)