What GPU monitoring metric should be prioritized to confirm thermal throttling during a high-intensity AI training session?

The optimal metric to prioritize in confirming thermal throttling during high-intensity AI training is GPU temperature and thermal status. When a GPU operates under heavy load, it generates significant heat. If this heat exceeds the GPU's designed thermal thresholds, the system can initiate thermal throttling to prevent damage. This throttling leads to a reduction in clock speed and performance as a protective measure, making it essential to monitor the thermal status closely.

Monitoring GPU temperature allows you to identify whether the cooling system is effectively managing the heat output. Elevated temperatures indicate that the GPU is at risk of throttling and could impact performance crucially during AI training sessions. Thus, keeping an eye on the temperature metric is vital for ensuring optimal performance and avoiding unexpected slowdowns caused by thermal limits.

Other metrics, such as GPU clock speed or memory bandwidth utilization, can provide useful information about performance but do not directly indicate thermal conditions. CPU utilization, while relevant for overall system performance, is less indicative of the GPU's thermal state, especially in a scenario focused on GPU-intensive tasks like AI training. Hence, monitoring GPU temperature and thermal status is the most effective means of assessing potential thermal throttling.