GPU utilization is a metric that indicates how actively a GPU is being used to execute workloads over a given period, typically expressed as a percentage.
Because GPUs represent one of the most expensive components in modern AI infrastructure, both in upfront cost and in the power and cooling required to run them, GPU utilization is one of the most closely watched metrics in AI data center operations. Low utilization can indicate that expensive GPU hardware, and the infrastructure supporting it, is being underused while continuing to consume space, power, and cooling resources.
How Is GPU Utilization Measured?
GPU utilization can be tracked at several levels of granularity:
- Compute utilization. The percentage of time the GPU is actively executing compute workloads. Depending on the monitoring tool, more granular metrics such as SM activity, tensor-core activity, or other hardware-counter metrics can provide additional insight into how the GPU is being used.
- Memory utilization and memory usage. Memory utilization indicates how actively the GPU's device memory is being accessed, while memory usage indicates how much of the GPU's available memory is allocated. High memory usage can constrain workloads even when compute resources remain available.
- Cluster-level utilization. Utilization aggregated across GPUs in a cluster, which can help reveal whether workloads are evenly distributed or concentrated on a subset of available hardware.
- Time-based utilization. Utilization trends over hours, days, or weeks, which help distinguish between GPUs that are briefly idle between jobs and GPUs that are chronically underused.
Tools like NVIDIA's nvidia-smi, DCGM (Data Center GPU Manager), and cluster orchestration platforms provide the raw telemetry, while monitoring dashboards aggregate this data across the fleet.
Why Low GPU Utilization Happens
Underutilized GPUs are a common and costly problem, often caused by:
- Data pipeline bottlenecks. GPUs sit idle waiting for data to be loaded or preprocessed if storage or networking can't feed data fast enough to keep up with GPU processing speed.
- Poor job scheduling. Inefficient workload scheduling can leave GPUs allocated to a project but not actively processing, or can concentrate jobs on some nodes while others remain idle.
- Over-provisioning. Reserving GPU capacity for anticipated future workloads that haven't yet materialized.
- Abandoned or forgotten jobs. GPUs left allocated to completed, stalled, or otherwise inactive workloads can continue consuming power and occupying capacity without delivering useful work.
- Mismatched workload-to-hardware fit. Running workloads that don't fully leverage a GPU's architecture, or using higher-end GPUs for tasks that don't require that level of performance.
Why GPU Utilization Matters to Data Center Teams
While GPU utilization is often framed as an IT or data science concern, it has direct implications for data center operations:
- Capacity planning accuracy. Understanding actual GPU utilization, alongside the physical capacity and power requirements of deployed GPU systems, helps teams more accurately forecast when additional GPU capacity and the infrastructure to support it will be needed.
- Power consumption correlation. GPU utilization and power draw can be correlated to help explain power consumption patterns, although power draw varies with workload characteristics, GPU architecture, clock speeds, memory activity, and other factors.
- Deferred infrastructure spend. Improving utilization of existing GPU capacity can delay the need for costly new high-density rack buildouts or additional liquid cooling infrastructure.
- Stranded capacity identification. Chronically underutilized GPUs represent a form of stranded capacity, similar in concept to stranded power capacity, that could be reallocated to other workloads or teams.
Connect GPU Utilization to Physical Infrastructure with DCIM Software
GPU utilization data typically lives in IT and data science monitoring tools, but the space, power, and cooling consequences of that utilization play out in the physical data center. Data Center Infrastructure Management (DCIM) software helps connect the two.
DCIM software can complement GPU utilization monitoring by providing:
- Rack and device-level power monitoring. Measured power consumption data at the rack PDU and device level, which can be correlated against GPU utilization data to understand the relationship between compute load and actual power draw.
- Capacity planning based on real consumption. Automatic power budgeting based on measured load, rather than device nameplate ratings, can reduce unnecessary power-capacity reservations and help teams make better use of existing infrastructure.
- What-if analysis. Model the physical infrastructure impact of adding, removing, or relocating GPU-equipped systems before making changes, helping teams evaluate available power, space, and cooling capacity.
- Digital twin visibility. A complete model of GPU-equipped infrastructure, including relevant liquid-cooling infrastructure, helps teams evaluate physical capacity and plan deployments.
Want to see how Sunbird's world-leading DCIM software can help you correlate GPU utilization with your physical infrastructure capacity? Get your free test drive now.