[PDF] GPU Reliability in AI Clusters: A Study of Failure Modes and Effects

Abstract: This article presents a comprehensive explanation of GPU reliability challenges in artificial intelligence clusters, addressing a critical gap in understanding how modern AI workloads affect accelerator hardware. The article establishes a detailed taxonomy of GPU failure modes specific to AI workloads, with particular attention to thermal issues, power delivery instabilities, memory subsystem degradation, and manufacturing variations. The article reveals that the sustained high-utilization characteristics of deep learning training create unique stress patterns that accelerate hardware degradation through mechanisms distinct from those observed in traditional computing workloads. The article quantifies the cascading impacts of these failures on training convergence, ... to establish a taxonomy of GPU failures in AI clusters; second, to quantify the impact of these failures on key performance indicators including training time, model convergence, and energy efficiency; and third, to evaluate mitigation strategies that enhance GPU reliability without compromising computational performance. By analyzing operational data from diverse AI … phases of AI workloads created voltage transients that exceeded design tolerances. Particularly concerning were failures during model checkpoint operations, where synchronized memory writes across multiple GPUs created power demand spikes that voltage regulators struggled to accommodate. 4.3 Memory Subsystem Failures Memory failures comprised 18% of incidents, … failures showed a strong correlation with workloads featuring high parameter counts and large batch sizes. 4.4 Manufacturing and Silicon Defects Manufacturing defects and silicon imperfections accounted for 13% of failures, typically manifesting early in operational life. These … Firmware and driver issues represented 10% of failures, despite not being hardware defects per se. Their inclusion reflects the practical operational reality that they present indistinguishably from hardware failures to users. Most prevalent were resource leaks in GPU kernel drivers during extended operation and timing-sensitive firmware bugs exposed by repetitive training patterns. … load transitions, particularly during checkpointing Independent power domains, multi-stage voltage regulation Memory subsystem failures 18% Model accuracy degradation, increased training volatility Separated memory cooling paths, memory-mirroring between paired GPUs Manufacturing and silicon defects 13% Early-life failures, particularly during tensor operations Burn-in testing with AI- specific workloads Firmware and driver- related issues 10% Training disruption requiring system reinitialization Adaptive checkpoint frequency algorithms Interconnect and communication failures 6% Synchronization issues in multi- GPU training Signal integrity optimization under thermal stress … standard deviation increasing by 23% compared to stable systems. Most concerning were "silent" performance degradations where throughput declined without triggering monitoring alerts, leading to extended periods of suboptimal operation. 5.3 Energy Consumption Implications Failed or degraded GPUs exhibited significant … C 2.3% Not specified Increased failure rates during high regional demand periods Capacity planning to avoid oversubscription 8. DISCUSSION 8.1 Key Findings Synthesis The comprehensive analysis reveals several overarching patterns in GPU reliability for AI workloads. First, failure modes in AI clusters differ substantially from those observed in traditional HPC environments, with thermal issues and memory subsystem failures dominating. Second, the sustained high-utilization patterns characteristic of deep learning workloads … see reliability decrease by 15-20% per GPU generation while maintenance costs increase by 25-30%. 8.3 Limitations of Current Approaches Current reliability approaches demonstrate several limitations. First, monitoring systems remain predominantly reactive despite the advances in predictive techniques, with 42% of failures still