Which GPU monitoring metric is critical for identifying resource contention in a multi-tenant AI cluster?

The critical GPU monitoring metric for identifying resource contention in a multi-tenant AI cluster is GPU Utilization Across Jobs. This metric provides insights into how effectively the GPU resources are being allocated and used among various jobs running concurrently. In a multi-tenant environment, where multiple workloads may be competing for the same GPU resources, monitoring GPU utilization allows administrators to see if any particular job is monopolizing GPU resources or if the overall GPU usage is not reaching its potential due to contention.

By analyzing GPU Utilization Across Jobs, it becomes possible to identify if one job is overpowering others, leading to underperformance for less utilized jobs. This can help inform decisions regarding resource allocation, job scheduling, and adjustments needed to achieve better balance and efficiency within the cluster.

In contrast, while metrics like GPU Temperature, Memory Bandwidth Utilization, and Network Latency are important for assessing other aspects of GPU performance and overall system health, they do not provide direct insights into how workloads are competing for GPU resources. Temperature monitoring helps prevent overheating, bandwidth utilization monitors memory access efficiency, and latency impacts network communication; however, they do not typically reveal the contention dynamics that arise from running multiple jobs on a shared GPU infrastructure. Therefore, GPU Utilization Across Jobs is the most relevant