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NVIDIA Data Center GPU Manager (DCGM) is host-level software for monitoring and managing NVIDIA data-center GPUs. It provides telemetry, health checks, diagnostics, profiling, accounting, and configuration functions. For Prometheus monitoring, DCGM Exporter exposes selected DCGM fields at an HTTP endpoint; Prometheus scrapes that endpoint and stores the resulting time series. DCGM itself is not a dashboard or a central metrics database.
How DCGM monitoring works
- Device access: DCGM runs on a GPU host and uses NVIDIA’s management software to access supported device data. The NVIDIA driver and platform services continue to control the hardware.
- Field monitoring: DCGM watches selected fields for supported entities, such as GPUs and other hardware. Its fields and available entities depend on the device, driver, software versions, and configuration. NVIDIA describes DCGM as a host-level library and agent in its overview.
- Reading the data: Administrators and integrating applications can access DCGM through command-line tools and public APIs. DCGM can run as a standalone host engine or be embedded in an application.
- Prometheus integration: DCGM Exporter makes configured fields available in Prometheus metric format through an HTTP
/metricsendpoint. The default listener is:9400. Prometheus scrapes the endpoint; it is responsible for collecting and storing the time series. See NVIDIA’s DCGM Exporter installation guide.
The exporter can start an embedded host engine or connect to a separately managed one. NVIDIA documents tested DCGM and exporter release pairings; mismatched versions are not tested or supported. Check the current DCGM documentation and supported hardware and software guidance before deployment.
What DCGM can monitor and manage
Telemetry and GPU behavior
Depending on device support and configuration, DCGM can expose fields such as GPU clocks, temperature, power, energy, activity, and traffic. A field’s presence in a configuration does not guarantee that it will be emitted: hardware, driver and DCGM versions, permissions, entity selection, and deployment all affect availability. NVIDIA maintains a DCGM Exporter metrics reference.
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DCGM can check GPU health while workloads run and manage responses to selected event classes. NVIDIA describes its background health checks as non-invasive. These checks are distinct from active diagnostic tests.
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Active diagnostics
Diagnostics run tests to help validate GPU operation when a device can be allocated for testing. Depending on the tests, they can consume compute, memory, PCIe, NVLink, CPU, power, or cooling capacity, so they are not equivalent to passive monitoring on a busy production GPU. They report test results; they do not repair faults or determine whether hardware qualifies for an RMA. See NVIDIA’s diagnostics overview and diagnostics reference.
Profiling
DCGM profiling uses hardware-counter interval metrics to answer broad utilization questions, such as whether compute engines, memory, or interconnects are active. These metrics are not kernel traces: they do not identify a source line, kernel, or instruction. For details, see NVIDIA’s profiling documentation.
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Accounting and integration
DCGM also provides process and accounting data, along with APIs that management software and scripts can use to integrate GPU information into other systems.
NVSwitch and other entities
DCGM includes functions related to NVSwitch and other supported hardware entities. NVIDIA notes that Fabric Manager remains responsible for NVSwitch fabric configuration and lifecycle management.
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Choosing a DCGM and exporter deployment
These approaches are operating choices, not performance rankings. Choose based on who owns the host or cluster services, how GPU access is provided, and how the deployment is maintained.
| Approach | What it means | Operational considerations |
|---|---|---|
| Standalone host engine | A separately managed DCGM service that tools or clients connect to on a host. | Account for service ownership, client connections, and host-engine version compatibility. |
| Embedded mode | DCGM runs inside an integrating process, such as the exporter. | Consider process lifecycle and configuration ownership; avoid an unintended conflict with an already-running host engine. |
| Package-managed exporter | A host service installed with system packages and managed through systemd. | Operations are handled host by host; keep package versions paired as NVIDIA documents. |
| Container exporter | An exporter container runs on each GPU host. | Check container runtime, GPU access, image version pairing, and endpoint exposure. |
| Helm deployment | The exporter is deployed to Kubernetes through a Helm chart. | Plan for cluster lifecycle, scrape configuration, and chart values. |
| GPU Operator-managed exporter | The exporter is managed within NVIDIA’s Kubernetes GPU stack. | Check whether the Operator already manages drivers, the toolkit, DCGM, and exporter lifecycle. |
NVIDIA documents exporter installation options and release considerations in its installation guide and exporter reference.
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What DCGM does not replace
- A central monitoring backend: DCGM provides host-level device data, and the exporter exposes selected fields. Prometheus or another compatible monitoring system must scrape and store the data.
- A universal metric source: Not every field is supported on every GPU, and a configured metric may not appear on a particular system.
- A code-level profiler: Profiling metrics indicate broad, interval-based activity, not the specific kernel or line of code responsible.
- Hardware repair: Diagnostics can report test findings, but they do not fix a device or decide RMA eligibility.
When DCGM is useful
DCGM is useful when operators need host-level GPU telemetry and health information, want to run diagnostics when devices can be taken for testing, or need to integrate NVIDIA GPU data with management software. In a Prometheus setup, it forms the device-data and metric-exposure layer between the GPU host and Prometheus. It is not, by itself, the dashboard, storage system, or source-level performance profiler.
Quick Recap
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