When GPUs Fail Quietly: Observability-Aware Early Warning Beyond Telemetry (arxiv.org)

A new study introduces an innovative observability-aware framework designed to enhance early warning systems for GPU failures, particularly in high-performance computing (HPC) and AI applications. Traditional telemetry often relies solely on numeric data, which may not adequately signal impending detachment-class failures—those that occur suddenly and without clear numeric warning signs. Instead, these failures typically manifest through the loss of device metrics and degradation in monitoring integrity. The proposed framework addresses this gap by modeling both GPU utilization-aware thermal drift signatures and indicators of monitoring degradation, such as increased scrape latency and sample loss. This advancement is significant for the AI/ML community as it offers a more proactive approach to managing GPU resources, potentially reducing downtime and improving system reliability. By employing joint modeling of structural telemetry, the framework enhances early-warning lead times, allowing operators to take preventative measures before failures disrupt workloads. The study's findings, evaluated using real-world production telemetry from GWDG, underscore the need for more robust monitoring solutions in environments where efficient GPU performance is critical. Additionally, the dataset used for evaluation is publicly available, promoting further research and validation in this crucial area.