To ensure the health and performance of GPU resources in an AI data center, which monitoring approach is most effective?

Setting up NVIDIA DCGM (Data Center GPU Manager) health checks and alerts is the most effective approach for monitoring the health and performance of GPU resources in an AI data center. NVIDIA DCGM is specifically designed for managing the performance and health of NVIDIA GPUs, allowing for real-time monitoring of various metrics, such as GPU temperature, memory usage, and power consumption.

By utilizing DCGM’s health checks, administrators can proactively identify issues related to GPU performance or potential hardware failures. The ability to set alerts for these metrics enables timely interventions, which is crucial in AI workloads that can be sensitive to performance dips or hardware malfunctions. This proactive monitoring approach is critical to maintaining optimal performance and reliability of GPU resources.

The other options do not provide the same level of specific monitoring for GPU resources. Automatic workload restart mechanisms help manage workloads but do not offer direct insights into GPU health. Monitoring server uptime and network latency focuses primarily on the overall server environment rather than GPU-specific metrics. Lastly, reviewing system logs weekly can be beneficial for general system health but may not provide the immediate or detailed insights required to address GPU performance issues effectively.