GPU Temperature (A) should be monitored most closely to prevent failures during intensive training.

Overheating is a primary cause of GPU hardware failure, especially under sustained high workloads

like deep learning. Excessive temperatures can degrade components or trigger thermal shutdowns.

NVIDIA’s System Management Interface (nvidia-smi) tracks temperature, with thresholds (e.g., 85-

90°C for many GPUs) indicating risk. Proactive cooling adjustments or workload throttling can

prevent damage.

Power Consumption(B) is related but less direct—high power can increase heat, but temperature is

the failure trigger.

Frame Buffer Utilization(C) reflects memory use, not physical failure risk.

GPU Driver Version(D) affects functionality, not hardware health.

NVIDIA recommends temperature monitoring for reliability (A).

Reference:NVIDIA GPU Monitoring Guide; nvidia-smi documentation on nvidia.com.