Utilization describes how much of an available resource is used over a defined interval. For GPUs, vendor tools may report the percentage of a sampling period during which at least one kernel was running. This is useful, but it is not identical to the share of theoretical computing capacity being delivered or the efficiency of the model itself.
An AI server may sit idle between requests, then reach high activity during busy periods. Keeping GPUs busy can improve the economics of purchased or reserved hardware. Yet pushing utilization too high can increase queues and hurt response times, especially for interactive inference workloads that must respond immediately.
Different metrics answer different questions. GPU activity, memory usage, power consumption, tokens per second and request throughput can move independently. A displayed 90 percent GPU utilization does not prove that code is optimized or that the processor is producing 90 percent of its theoretical output.
Track utilization together with P95 latency, throughput and cost per completed request. Use representative traffic over time, and leave enough spare capacity to absorb peaks rather than optimizing a single percentage in isolation.