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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesGPU utilization is a useful activity signal, not a verdict on whether a GPU is truly spare. To find underused AI capacity, measure device activity over a representative workload cycle, pair it with memory and power data, identify the process or pod using the device, and check whether the scheduler has allocated or queued work for it.
What GPU utilization does—and does not—tell you
A utilization percentage describes activity reported by a particular tool at a particular device level and time. It does not, on its own, tell you whether memory is occupied, which workload owns the GPU, whether the device is allocated to a job, or whether work is waiting to run. NVIDIA reports utilization, power, clocks, temperature, and memory as distinct signals; treat them as separate measurements rather than substitutes. NVIDIA nvidia-smi documentation
Before comparing readings, record the GPU model, driver and runtime, monitoring utility and version, host or cluster, device identity, and whether the GPU is partitioned or shared. Keep device-level activity distinct from memory occupancy and from scheduler requests or allocations. If comparing NVIDIA and AMD devices, first confirm that the metric definitions, sampling behavior, supported hardware, and device granularity are comparable.
Take a local GPU reading
NVIDIA: sample device and process activity
On a supported NVIDIA system, use nvidia-smi dmon for recurring device readings. The documented default sampling cycle is one second on supported configurations. Select the metric groups you need and add timestamps or CSV output when they help with later correlation. For process-level statistics, use nvidia-smi pmon where supported; its per-process utilization values are averages since the previous cycle. A missing or unsupported value is unknown, not zero. NVIDIA nvidia-smi documentation
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#1 Best Overall
- System Compatibility Note: This 2-slot card measures 271 x 112 x 39 mm and requires a single 12V-2x6-pin power connector. Please verify chassis and PSU compatibility before purchase.
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- Professional Intel Arc Pro B70 GPU: Built on the Intel Xe2-HPG architecture, it features 32 Xe cores and 256 XMX engines, designed to accelerate AI, rendering, and complex visualization workloads.
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There is an important limitation for NVIDIA Multi-Instance GPU (MIG) environments: nvidia-smi documentation says querying GPU, memory, encoder, decoder, JPEG, and OFA utilization through dmon is not currently supported on MIG-enabled GPUs. Do not read an unavailable field as 0%. Check which entity levels and metrics your deployed DCGM Exporter version supports, and label results as physical-GPU or instance-level readings. NVIDIA nvidia-smi documentation NVIDIA Install DCGM Exporter
AMD: select signals with AMD SMI
On AMD systems, amd-smi monitor can report selected signals such as graphics utilization and clock, memory utilization and clock, VRAM used and total, power, temperature, and encoder or decoder activity. The AMD SMI Release 24.6.3.0 guide, for ROCm 6.2.4, documents watch intervals and JSON, CSV, or file output. These command details are version-specific; check the documentation for the AMD SMI and ROCm version deployed before relying on them. AMD SMI Release 24.6.3.0 documentation
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- PLEASE NOTE: Exporting an NVIDIA RTX Pro 6000 GPU outside the US requires strict adherence to the U.S. Export Administration Regulations (EAR) and issuance of an export license from the Bureau of Industry and Security (BIS). Compliance and Know Your Customer (KYC) screening may be required as a condition of order acceptance. [NVIDIA Blackwell Streaming Multiprocessor] The new SM features increased processing throughput, and new neural shaders that integrate neural networks inside of programmable shaders | DLSS 4: Multi Frame Generation ensures ultra-smooth frame pacing for lifelike simulations.
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- [Universal MIG] Divide a single RTX PRO 6000 Blackwell into multiple isolated instances, each with dedicated resources, allowing for concurrent execution of multiple workloads, optimized GPU utilization, and secure isolation of different applications or users. [WARRANTY] 3 YR Manufacturer's Warranty. Bulk OEM Packaging. Retail Packaging is NOT included.
Collect a useful time series
A single snapshot can miss bursty inference, batch boundaries, data-loading pauses, scheduled jobs, or daily demand changes. Capture a time series that spans the workload’s meaningful cycle, and retain labels that let you identify the device and workload. There is no universal observation window: choose one that fits the workload and the decision you need to make.
For ongoing NVIDIA fleet monitoring, DCGM Exporter exposes selected DCGM fields in Prometheus format. It can run as a systemd service, an OCI container, or a Kubernetes DaemonSet. The installation guide names DCGM_FI_DEV_GPU_UTIL for GPU utilization and DCGM_FI_DEV_FB_USED for framebuffer memory used. Collection cadence is controlled by --collect-interval; the guide gives a default of 30,000 milliseconds. Confirm the support matrix and configured collector fields for your installed version: not every field is exposed automatically in every configuration. NVIDIA Install DCGM Exporter
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- System Compatibility Note: 2-slot card, 271x112x39mm, single 8-pin power, 200W TDP. Verify chassis clearance and PSU capacity before purchase.
- Dedicated Support: Please contact us directly through Amazon for any product questions or assistance you may require.
- 24GB GDDR6 on 192-Bit Bus: Massive 24GB memory with 456 GB/s bandwidth – ideal for LLMs, AI inference, 3D rendering, and generative design.
- Intel Xe2-HPG Architecture: Built on Intel's next-gen architecture with 20 Xe cores and 160 XMX engines for AI acceleration (197 INT8 TOPS).
- PCIe 5.0 Support: PCI Express 5.0 x16 interface for maximum bandwidth with the latest workstation platforms.
NVIDIA describes a common monitoring architecture as a collector, time-series database, and visualization layer, with Prometheus and Grafana as examples. For Kubernetes, it recommends DCGM Exporter for GPU telemetry and describes adding kube-state-metrics and node-exporter for cluster API and node context. NVIDIA About GPU Telemetry
Connect device metrics to the workload
A device chart can show low activity without revealing who holds its memory or allocation. On Kubernetes, join hardware telemetry to cluster objects and pod status, including whether GPU pods are pending or running. NVIDIA’s GPU Usage Monitor project describes combining DCGM Exporter, kube-state-metrics, Prometheus, and Grafana to surface both over-provisioning and pod starvation. This is the project’s stated purpose, not an independent benchmark of its effectiveness. NVIDIA Developer Blog: Get Real-Time Visibility into GPU Usage Across Kubernetes Clusters
Rank #4
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Workload labels may require configuration and permissions. When labels are absent, NVIDIA’s installation guide points operators to check pod-resources socket access, device ID type, service account, and RBAC. It also documents HPC job mapping and runtime container label options. Validate that the exporter’s labels actually identify the workload you expect before using them to allocate or reclaim capacity. NVIDIA Install DCGM Exporter
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Interpret utilization alongside memory and scheduling
- Low compute and low memory use: The device may be idle or lightly loaded. Confirm ownership, allocation, and a representative observation period before treating capacity as reusable.
- Low compute with substantial memory held: A model, cache, or reservation may remain resident during a quiet interval. This pattern does not establish that the workload can safely be evicted or shared.
- High compute with weak application throughput: Utilization alone cannot show whether the work is productive. Compare the GPU time series with application throughput, latency, and queue depth; these are additional operator checks, not thresholds established by the cited vendor documentation.
- Pending GPU pods or jobs: Investigate scheduling and allocation even if current device activity looks low. Check GPU requests, device allocation, labels, and placement constraints. A low utilization chart does not prove that a pending workload can use the device.
- Missing or implausible metrics: Check host GPU detection, exporter health and endpoint, selected fields, driver/DCGM compatibility, capabilities required for profiling fields, and Kubernetes pod-resources access and RBAC. NVIDIA Install DCGM Exporter
No cited source establishes a universal percentage at which a GPU counts as underused, an ideal memory headroom, or a safe sharing level. Set operational thresholds against local service goals and representative workload cycles rather than applying one fleet-wide cutoff.
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Choose a measurement approach
| Need | Local CLI | Persistent NVIDIA metrics | AMD host sampling |
|---|---|---|---|
| Fast diagnosis | nvidia-smi dmon and, where supported, pmon. NVIDIA nvidia-smi documentation |
Query the DCGM Exporter endpoint after deployment. NVIDIA Install DCGM Exporter | amd-smi monitor. AMD SMI Release 24.6.3.0 documentation |
| Fleet history and dashboards | Requires separate logging or collection. | DCGM Exporter with Prometheus and Grafana is a documented NVIDIA monitoring pattern. NVIDIA About GPU Telemetry | The consulted AMD guide documents local output and file capture; a fleet backend depends on the operator’s chosen stack. AMD SMI Release 24.6.3.0 documentation |
| Workload attribution | Process view where supported. | Kubernetes labels and job mapping require configuration. NVIDIA Install DCGM Exporter | Confirm workload attribution support in the deployed AMD SMI environment; the cited guide does not establish a general fleet attribution workflow. AMD SMI Release 24.6.3.0 documentation |
| Main caveat | Product support and MIG behavior vary. NVIDIA nvidia-smi documentation | Validate selected fields, DCGM and driver compatibility, and permissions. NVIDIA Install DCGM Exporter | Commands and options cited here are from ROCm 6.2.4 documentation. AMD SMI Release 24.6.3.0 documentation |
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