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Monitor GPU and CPU activity against a baseline of authorized AI workloads, then investigate sustained usage that cannot be matched to a known job, process, or deployment. Utilization alone cannot tell you that a miner is running: correlate device metrics with scheduler and container identity, host and security logs, and cloud resource usage before drawing a conclusion.
What usage monitoring can—and cannot—tell you
GPU and CPU telemetry can reveal activity that is unusual for a server, but it cannot by itself identify cryptomining. Training, inference, data processing, and other legitimate workloads can also use substantial compute. NVIDIA notes that DCGM profiling metrics are interval averages; a high reading does not establish which workload caused it, and the metrics do not identify the kernel or source line responsible for activity.
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There is no universal utilization threshold that proves unauthorized mining. Set expectations for each server and workload, and treat unexplained or out-of-schedule activity as an investigation trigger rather than a verdict.
Build a baseline for authorized AI work
Record which GPU, host, container, user, and scheduler job should be active, and when. Measure representative phases of training and inference rather than relying on one system-wide utilization figure. Keep deployment and workload records available so an alert can be checked against approved activity.
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- Map each GPU to its host and the workloads permitted to use it.
- Record job start and end times, scheduler identity, container, user, and expected resource use.
- Capture representative behavior across workload phases, including idle periods where applicable.
- Keep deployment changes and ordinary job records alongside monitoring data.
Collect GPU and host CPU telemetry
NVIDIA data-center GPUs
NVIDIA Data Center GPU Manager (DCGM) can expose graphics-engine activity, streaming multiprocessor (SM) activity, memory-bandwidth activity, and PCIe or NVLink traffic. DCGM profiling documentation describes a default sampling cadence of 1 Hz, with configurable query frequency; available metrics vary by hardware. Profiling counters require appropriate administrator privileges, and some counters cannot be collected simultaneously on some GPUs. Collection can also conflict with NVIDIA developer profiling tools such as Nsight Systems or Nsight Compute. See NVIDIA’s DCGM feature overview and profiling documentation.
CPU and Kubernetes metrics
Choose CPU telemetry for the actual host platform; DCGM is not a general CPU monitor for every AI server. NVIDIA documents DCGM CPU monitoring for Grace CPUs, including total, user, nice, system, and interrupt utilization, starting with DCGM 3.3. In NVIDIA’s documented Kubernetes monitoring architecture, node_exporter provides node-level metrics such as CPU utilization, while DCGM Exporter exposes GPU metrics to Prometheus. See NVIDIA’s GPU telemetry overview.
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For non-NVIDIA accelerators, non-Kubernetes hosts, or other monitoring stacks, use the relevant vendor’s authoritative telemetry and the host’s process-accounting tools. The sources cited here do not establish a universal cross-vendor metric mapping.
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When a GPU reading is unexpected, identify who or what was using the device at that time. DCGM can report statistics for an operating-system process when its PID is known, or for a caller-defined scheduler job when scheduler instrumentation provides a job ID and time boundary. Join these observations with scheduler, container, user, and application records. NVIDIA’s process and job statistics documentation describes these reports.
DCGM GPU reports do not replace scheduler records or provide a complete picture of CPU and memory use, container isolation, or application performance. Keep those sources in the same investigation workflow. If GPU use is high but no expected process or job accounts for it, check whether process visibility or scheduler instrumentation is incomplete before treating the mismatch as evidence of compromise.
Choose a monitoring pattern that fits the server
For a Kubernetes cluster using NVIDIA data-center GPUs, NVIDIA documents a pattern in which DCGM Exporter supplies GPU metrics, node_exporter supplies node metrics, and Prometheus scrapes and stores the time series. The right choice for another environment depends on more than the metric names.
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| Decision | What to check |
|---|---|
| Accelerator support | Whether the telemetry tool supports the GPU or other accelerator model in use, and which metrics that hardware exposes. |
| Host support | Whether CPU metrics cover the server platform; DCGM’s documented CPU fields apply to Grace CPUs. |
| Deployment | Whether the setup matches Kubernetes, bare metal, or the environment where the workloads run. |
| Attribution | Whether telemetry can be joined to processes, scheduler jobs, containers, users, and application records. |
| Operations | How metrics are retained and alerted on, and what permissions, counter conflicts, or collection overhead apply. |
Alert on sustained, unexplained combinations
Set alerts relative to the server’s own baseline and approved job schedule rather than applying one threshold to every machine. Useful investigation triggers include sustained GPU activity outside scheduled work, unexplained device-memory or interconnect activity, CPU use that rises without a known process, or resource and billing changes without an approved deployment.
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Investigate suspected mining as a security incident
If telemetry and process records point to a miner, broaden the review beyond GPU usage. Examine process execution, startup and persistence changes, security events, network connections, account activity, and nearby systems. In a specific federal-network incident, CISA reported that actors installed XMRig and also moved laterally, compromised credentials, and used Ngrok. That 2022 case illustrates why a miner may be one sign of a wider intrusion; it does not establish that all miners use the same tools or tactics. See CISA’s incident advisory.
For an incident like the one described in that advisory, CISA recommends isolating affected systems and collecting and reviewing relevant logs, data, and artifacts. Preserve evidence and follow your organization’s incident-response process rather than deleting suspicious files before they can be examined.
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