Which monitoring tool would be most effective in identifying GPU utilization imbalances in an AI data center?

Using NVIDIA Data Center GPU Manager (DCGM) is the most effective method for identifying GPU utilization imbalances in an AI data center. DCGM is specifically designed to monitor the health and performance of NVIDIA GPUs in data centers. It provides comprehensive metrics related to GPU utilization, memory usage, temperature, and power consumption, allowing for real-time monitoring and reporting.

This tool enables system administrators to gain insights into the performance of individual GPUs, identify underutilized or overutilized resources, and make informed decisions to balance workloads accordingly. By leveraging the specific capabilities of DCGM, administrators can optimize GPU utilization, which is crucial for maximizing the efficiency of AI workloads that rely heavily on GPU resources.

The other methods mentioned are not as effective for the specific requirement of monitoring GPU utilization. Manual daily checks of GPU temperatures focus solely on thermal performance rather than overall utilization metrics. Setting up alerts for disk I/O performance issues addresses a different aspect of system performance, and monitoring CPU utilization does not provide the necessary insights into GPU performance itself. Each of these options overlooks the specific functions and metrics that are critical for effective GPU monitoring in an AI-focused infrastructure.