Which GPU metric should be monitored to proactively prevent failures during intensive training sessions?

Monitoring GPU temperature is crucial for preventing failures during intensive training sessions, as high temperatures can lead to thermal throttling or hardware damage. The GPU generates significant heat when processing complex computations, especially during deep learning tasks that require substantial resource utilization. If the temperature exceeds the manufacturer's limits, it can cause performance degradation or even result in a system crash.

Keeping an eye on the GPU temperature allows for timely intervention, such as adjusting cooling solutions or reducing workload intensity, thereby maintaining optimal performance and extending the hardware's lifespan. This proactive approach helps ensure stable operations during demanding workloads, which is vital for successful AI model training.

While power consumption and frame buffer utilization are also important metrics to monitor, they do not directly correlate with the risk of failure due to overheating. Monitoring the GPU driver version is more about ensuring compatibility and functionality than preventing performance issues related to heat. Therefore, temperature is the most critical metric in this context for preventing failures during high-demand training scenarios.