The compute capability of large GPU fleets presents unprecedented opportunities to innovate and provide value to customers in record time. Yet these advancements come with a variety of challenges. At scale, teams are juggling heterogeneous hardware, fast‑moving software stacks, tight power envelopes, and spiky, multitenant workloads. A single hotspot, misconfigured driver, or subtle hardware fault can ripple, causing throttled jobs, missed SLAs and wasted spend.  As well, the complexity and number of components involved in large-scale clusters can be daunting, so it’s essential to maintain visibility into the day-to-day operations and understand the operational state at any given time. Monitoring GPU utilization and identifying bottlenecks during job execution becomes more difficult. Identifying areas of low utilization and migrating workloads to them is one of the best ways to ensure the highest return on investment.   For these reasons, GPU‑aware monitoring is essential at scale.…