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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11Monitor GPU and CPU activity against a baseline of authorized AI workloads, then investigate sustained resource use that cannot be matched to a known job, process, deployment, or user. A utilization spike is an alerting signal—not proof of cryptomining. Reliable triage combines device telemetry, process and scheduler identity, host and security logs, and cloud resource records.
Build a baseline of authorized AI work
Record which GPU, host, container, user, and scheduler job should be active for each training or inference workload. Measure representative phases—including startup, data loading, training, and idle periods—rather than relying on one system-wide utilization reading. Keep deployment and job records available so an alert can be compared with approved activity.
Baseline by server and workload: legitimate training can keep a GPU busy, while inference and data-processing jobs can produce different patterns. There is no universal GPU or CPU utilization threshold that establishes unauthorized mining.
Collect GPU and CPU telemetry
NVIDIA GPU metrics with DCGM
For NVIDIA data-center GPUs, NVIDIA Data Center GPU Manager (DCGM) can expose graphics- or compute-engine activity, streaming multiprocessor (SM) activity, memory activity, and PCIe or NVLink traffic. See NVIDIA’s DCGM feature overview and profiling documentation.
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DCGM profiling values are interval averages, not a record of the specific kernel or source line responsible for work. The profiling documentation describes a default sampling cadence of 1 Hz, with configurable query frequency; available metrics depend on hardware. Profiling counters require appropriate administrator privileges, and some counters cannot be collected together on certain GPUs. Collection may also conflict with NVIDIA developer profiling tools such as Nsight Systems or Nsight Compute.
Host CPU metrics
Choose CPU telemetry for the server platform rather than assuming a GPU manager covers every host. DCGM’s documented CPU fields—total, user, nice, system, and interrupt utilization—apply to NVIDIA Grace CPUs and begin with DCGM 3.3. NVIDIA’s GPU telemetry overview describes a Kubernetes pattern in which node_exporter supplies node-level metrics such as CPU utilization, while DCGM Exporter exposes GPU metrics to Prometheus.
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For non-Grace CPUs, use the host operating system or platform’s appropriate CPU monitoring and accounting tools. The telemetry sources here do not establish a universal cross-vendor metric mapping.
Connect utilization to a process or job
A device metric tells you that activity occurred; it does not, by itself, tell you whether that work was approved. Join GPU observations to operating-system processes and scheduler or container records before labeling activity suspicious.
- Known process ID: DCGM can report statistics for an OS process.
- Scheduler job: DCGM can report job statistics when scheduler instrumentation supplies a job ID and time boundary.
- Wider attribution: Match the result with user, container, scheduler, CPU and memory accounting, and application-level performance records.
NVIDIA’s process and job statistics documentation explains these reports and their limits: they do not replace scheduler records, CPU and memory accounting, container isolation, or application metrics. Preserve the identity and timing context alongside time-series telemetry so an investigator can trace a reading to an authorized workload.
Alert on sustained, unexplained combinations
Set alert conditions relative to each server’s baseline and approved job schedule. Treat alerts as prompts to investigate, not verdicts. Useful triggers include:
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- Sustained GPU activity when no authorized job is scheduled.
- Unexplained GPU memory or PCIe/NVLink activity that does not match a recorded workload.
- CPU use that rises without a corresponding known process or service change.
- Resource consumption or cloud billing changes without an approved deployment.
Correlate alerts with deployment history, scheduler events, process execution, security logs, and cloud resource usage. CISA recommends monitoring cloud resource use for irregular usage; its Cloud Security Technical Reference Architecture, Trusted Internet Connections (TIC) 3.0 Cloud Use Case, and logging guidance provide relevant security context. CISA also cautions that high CPU use can result from configuration or service-health problems, not only malicious activity.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choose monitoring components for your environment
For a Kubernetes cluster with NVIDIA data-center GPUs, NVIDIA documents DCGM Exporter for GPU metrics and node_exporter for node metrics, with Prometheus collecting and storing time series. The right fit still depends on the environment:
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- GPU vendor and model support, including which metrics are available.
- Host CPU platform and the metrics appropriate to it.
- Kubernetes versus bare-metal deployment.
- Whether telemetry can be tied to process or scheduler job identity.
- Retention, alerting, and security-log integration.
- Privilege requirements and potential telemetry overhead or counter conflicts.
For other accelerator vendors, non-Kubernetes hosts, or a different monitoring stack, use authoritative telemetry and process-accounting tools for that platform. The cited documentation does not establish a single setup or threshold that works across all AI servers.
Investigate suspected mining as a security incident
If activity remains unexplained after checking workload records, examine process execution, startup and persistence changes, security events, network connections, account activity, and nearby systems. Do not limit the investigation to GPU utilization: an unauthorized miner may be one component of a broader compromise.
In a specific 2022 incident, CISA reported actors installing XMRig alongside credential compromise, lateral movement, and use of Ngrok. That case illustrates why adjacent accounts and systems matter; it does not mean every miner uses XMRig or the same tactics. In the context of that incident, CISA recommended isolating affected systems and collecting and reviewing relevant logs, data, and artifacts. See its incident advisory for the case-specific details.
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