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Monitor a GPU cluster as one system, not as a collection of isolated charts. The practical design is Kubernetes observability for metrics, logs, and traces, plus NVIDIA DCGM Exporter on every GPU node, Prometheus-compatible time-series storage, dashboards, and workload labels that connect a GPU to its node, pod, job, queue, and inference request. An AIOps layer then detects deviations from each workload’s baseline, correlates failures across hardware and software, and routes an alert with an owner and next diagnostic step. This approach shows whether a job is GPU-, memory-, CPU-, network-, scheduler-, or data-pipeline-bound, while exposing XID errors, ECC faults, thermal throttling, failed health checks, queue starvation, and inference SLO violations before they become unexplained outages.
What does AIOps observability add to GPU monitoring?
Traditional GPU monitoring answers “what is this device doing?” AIOps observability must also answer “which workload owns the device, what changed, where is the bottleneck, and what should the on-call engineer do next?” Kubernetes defines observability around metrics, logs, and traces. For AI platforms, those signals need to be joined across the GPU, host, network fabric, scheduler, storage path, training job, and inference service.
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AIOps is useful only when it changes an operational decision. Correlating a low-utilization GPU with high CPU wait, a slow data loader, and a growing queue is more valuable than adding another utilization panel. Likewise, correlating an inference latency spike with NVLink errors, pod restarts, and a cache miss rate can identify the failure domain without forcing an engineer to search five systems manually.
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How should a GPU-cluster observability architecture be laid out?
Use layered telemetry, with stable identity labels shared by every layer. The following sequence keeps hardware facts, Kubernetes context, and service behavior connected.
- Instrument the node and GPU. Install the vendor driver and DCGM-compatible components on every GPU node. Run DCGM Exporter as a DaemonSet or through the GPU Operator so each node exposes GPU metrics in Prometheus format.
- Scrape and store time series. Configure Prometheus or a managed Prometheus service to discover exporter endpoints. Retain enough history to compare a workload with its own normal behavior, not merely with a cluster-wide average.
- Collect Kubernetes state. Add node conditions, pod and container status, resource requests and limits, job completion, retries, scheduling latency, reservations, and queue information. A GPU metric without pod or job identity cannot explain ownership.
- Capture logs and traces. Preserve structured application logs, training-framework errors, health endpoints, and distributed traces. Traces are particularly important for inference because one request can cross a frontend, scheduler, queue, worker, and model-serving process.
- Add network and storage telemetry. For distributed training, collect NVLink, PCIe, InfiniBand or Ethernet throughput and errors, plus storage latency and data-loader timing. Communication and input stalls often look like “idle GPUs.”
- Correlate and act. Join dimensions such as cluster, node, GPU, namespace, pod, job, queue, model, and request. Feed the resulting signals to anomaly detection and alert routing, with a runbook URL or documented next action attached to each alert.
NVIDIA’s 2026 AI-factory guidance groups telemetry into platform health, GPU health and performance, network fabric, clusters and jobs, and inference services. A unified triage dashboard should expose a short, SLO-linked set of top-k metrics from each group rather than every available counter.
Which GPU and workload metrics should be collected?
Collect the following signals together. The exact scrape interval and retention depend on cluster size and budget; there is no universal interval or alert threshold that fits batch training, sparse models, and latency-sensitive inference.
| Domain | Key signals | What the signal helps diagnose |
|---|---|---|
| GPU compute | GPU utilization and streaming-multiprocessor (SM) utilization | Whether kernels are actively using the device; divergence between the two can indicate a workload that is not exercising the expected execution units. |
| GPU memory | Framebuffer memory used and free, memory-utilization rate, memory clocks | Capacity pressure, memory-bound kernels, fragmentation, and conditions preceding an out-of-memory failure. |
| Thermal and power | Power draw, temperature, clock frequency, and throttling indicators | Thermal limits, power caps, unstable clocks, or a board that is not reaching its expected performance state. |
| Interconnect | NVLink and PCIe receive/transmit activity, errors, and link health | Device-to-device or host-to-device transfer constraints that can make multi-GPU jobs wait. |
| Hardware health | XID events, ECC errors, GPU health, and node hardware events | Driver, firmware, memory-correction, or device faults. XID errors require immediate investigation rather than passive observation. |
| Kubernetes | Node readiness, taints, pod/container status, restarts, resource requests and limits, and object events | Scheduling failures, evictions, crash loops, insufficient capacity, and mismatches between requested and allocated GPUs. |
| Jobs and queues | Duration, retries, failures, queue wait, reservation time, scheduling delay, and starvation | Capacity contention, reservation conflicts, unfairness, and jobs that are technically running but not making progress. |
| Inference | Request rate, latency percentiles, errors, queue depth, saturation, cache behavior, and health checks | Service-level objective violations, overload, cold starts, cache misses, and failures in a specific model-serving stage. |
AKS documentation lists DCGM metrics for utilization, SM utilization, framebuffer memory, memory clocks, power, temperature, NVLink, and XID errors. It also notes that low utilization can be caused by CPU or I/O bottlenecks, so utilization must never be interpreted in isolation.
How do I deploy DCGM Exporter on a Kubernetes GPU cluster?
Choose the GPU Operator when you want an integrated lifecycle for drivers and GPU components. Use a standalone DCGM Exporter DaemonSet when you need tighter control over the image, metric allow-list, labels, or upgrade schedule. In either case, validate one node before rolling out to every cluster.
- Verify prerequisites. Confirm that each node has a supported NVIDIA driver, a working device-plugin path, and the permissions required to expose DCGM data. Check that Kubernetes schedules a test pod with the expected GPU resource.
- Install the exporter on GPU nodes. Deploy the exporter as a DaemonSet with node selection that excludes CPU-only nodes, or enable the exporter component in the GPU Operator. Ensure the pod has access to the host GPU interfaces required by DCGM.
- Expose the scrape endpoint. Create the Prometheus service discovery or scrape configuration for the exporter. Confirm that each target reports the expected GPU count and identity rather than a single aggregate value.
- Enable workload attribution selectively. DCGM Exporter can use Kubernetes pod-resources integration to add pod, namespace, container, and virtual-GPU labels. Begin with the identities needed for triage. Every unique label combination creates another time series, so do not attach request IDs, unbounded user strings, or rapidly changing metadata.
- Test metric continuity. Stop and restart a test pod, reschedule it to another node, and verify that dashboards still distinguish the GPU, node, and workload. A missing scrape after rescheduling is an observability failure, not an application failure.
- Protect the control plane. Set resource requests for the exporter, monitor scrape health, and alert when targets disappear. A collector that silently stops reporting can make a healthy GPU look idle.
Useful validation checks include listing exporter pods, inspecting their logs for DCGM initialization errors, checking Prometheus target status, and querying a single GPU’s utilization while a known test workload runs. Keep the exporter version, driver version, and GPU architecture in deployment metadata so an upgrade-related change is visible during incident review.
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Define a common identity model before building dashboards. At minimum, every series should be joinable to cluster, node, gpu, namespace, pod, and workload where those dimensions exist. Add job, queue, model, or service only when the scheduler or serving layer supplies stable values.
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Training and batch jobs
Record a job identifier, owner, queue, requested GPU count, allocated GPU count, start time, completion time, retry count, and scheduler wait. Join those records with per-GPU utilization, memory, power, and interconnect activity. This reveals the difference between a job waiting for capacity, a job allocated but starved by its input pipeline, and a job that is actively computing.
Inference services
Propagate a trace or correlation identifier through the frontend, scheduler, queue, worker, and model server. Keep request rate, latency percentiles, errors, queue depth, cache behavior, and saturation metrics at the service level; use traces to locate the slow span. Do not put a unique request identifier into Prometheus labels, because that creates unbounded cardinality. Keep it in logs and traces instead.
Cross-cluster identity
Use a globally unique cluster label and consistent names for environment, region, node pool, namespace, and workload. This allows federated or distributed time-series storage to compare clusters without confusing two nodes or jobs that share a local name.
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Classify a bottleneck by comparing several signals over the same time window. A single low or high percentage is not sufficient evidence.
GPU-compute-bound
High GPU and SM utilization sustained for the useful portion of the job, with stable clocks and no queue or input delay, usually indicates compute saturation. Confirm that throughput or step rate scales as expected when another GPU is added; do not assume that 100% utilization automatically means good performance.
Memory-bound or memory-capacity-bound
High memory-utilization rate, near-capacity framebuffer usage, or frequent memory stalls with lower arithmetic activity points toward memory pressure. Watch memory clocks, allocation failures, and out-of-memory events. Reducing batch size, changing precision, or improving tensor reuse may help, but those are application decisions that should be made after confirming the memory signal.
CPU or data-pipeline-bound
Low GPU utilization paired with high CPU usage, high I/O wait, slow data-loader timing, storage latency, or a growing input queue indicates that the GPU is waiting for data or preprocessing. The correct response is usually to inspect workers, file systems, caching, and serialization rather than raise the GPU count.
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Network- or communication-bound
In multi-node training, low or oscillating GPU utilization combined with high NVLink, PCIe, InfiniBand, or Ethernet traffic, congestion, retransmits, or link errors suggests communication pressure. Compare step time and synchronization time across nodes; one degraded link can hold an otherwise healthy job at a barrier.
Scheduler- or capacity-bound
Long queue wait, reservation conflicts, starvation, or repeated pending states with healthy existing GPUs indicate a placement or capacity problem. Separate “no GPU available” from “GPU allocated but not used” in queue dashboards so platform and application owners receive different alerts.
Stalled or unhealthy
Flat progress, no recent logs, failed health checks, pod restarts, XID events, ECC faults, or a vanished exporter target indicate a stall or hardware problem. Capture the node, GPU, workload, and first-failure timestamp before restarting anything; a restart can erase useful evidence.
Which alerts help an on-call engineer find the cause?
Alert on a condition that has an owner and a next action. Include cluster, node, GPU, namespace, pod, job or service, time window, severity, and a direct runbook link in every notification.
| Alert family | Trigger pattern | First diagnostic action |
|---|---|---|
| Underused allocation | Sustained low GPU utilization with high CPU, I/O wait, data-loader latency, or queue delay | Inspect the input pipeline, pod CPU limits, storage latency, and scheduler events before changing GPU capacity. |
| Memory pressure | High framebuffer use, allocation failures, out-of-memory events, or rapidly rising memory consumption | Identify the owning pod and recent model or batch-size change; check limits, fragmentation, and concurrent processes. |
| Thermal or power throttling | Temperature excursion, reduced clocks, throttling flags, or abnormal power draw | Check node cooling, power policy, fan or rack alarms, and whether the issue follows the GPU or remains with the node. |
| Hardware fault | XID, ECC, failed health check, or repeated node-level hardware events | Cordon the node according to the runbook, collect driver and kernel logs, and involve the hardware owner immediately. |
| Fabric degradation | NVLink, PCIe, InfiniBand, or Ethernet errors, congestion, or abnormal receive/transmit rates | Compare all peers in the job and inspect the affected link, switch path, and host interface. |
| Queue starvation | Long wait, idle allocated GPUs, reservation conflict, or repeated scheduling failure | Compare requested versus available resources, taints, topology constraints, quotas, and reservation ownership. |
| Job instability | Repeated retries, failures, restarts, or missing progress for a defined interval | Correlate the first retry with pod events, application logs, GPU errors, and storage or network changes. |
| Inference SLO | Latency, error rate, request saturation, queue depth, or cache behavior breaches the service objective | Open the trace for a slow request, identify the slow span, and compare model-server saturation with GPU and network health. |
Use workload-aware baselines instead of universal thresholds. A sparse batch job, a memory-bound model, and a communication-heavy distributed job can all be healthy at very different utilization levels. Anomaly detection should compare the current run with comparable jobs, model versions, time windows, and hardware pools.
Should I use managed GPU monitoring or run the stack myself?
Managed services reduce platform maintenance; self-managed components provide deeper control. Compare the two designs against your failure domains and operating model rather than choosing on convenience alone.
| Decision area | Managed collection | Self-managed DCGM, Prometheus, and dashboards |
|---|---|---|
| Setup and upgrades | Provider integrates exporter, storage, and parts of the collection lifecycle. GKE documentation lists supported Kubernetes versions, system-metrics collection, managed Prometheus collection, and managed GPU drivers as prerequisites. AKS offers managed GPU metrics in preview with DCGM Exporter and Azure Managed Prometheus, also subject to documented prerequisites. | Your team owns drivers, exporter images, scrape configuration, dashboards, recording rules, and upgrade testing. |
| Metric customization | Convenient defaults, but metric availability and labels follow provider-supported versions and collection behavior. | Full control over DCGM fields, allow-lists, labels, scrape intervals, retention, and alert routing. |
| Operational ownership | Less infrastructure to operate, with provider-specific support boundaries. | Responsibility remains with the platform team, including scrape health and cardinality incidents. |
| Multi-cluster view | Often integrates with the provider’s cross-project or cross-subscription monitoring, subject to its topology. | You design federation or distributed storage and enforce consistent cluster and workload labels. |
| Cost and cardinality | Charges depend on ingestion, retention, queries, and provider-specific metrics behavior. | Infrastructure and engineering costs are yours; high-cardinality labels, short scrape intervals, and long retention increase storage and query load. |
| Best fit | Teams prioritizing rapid adoption and a supported cloud path. | Teams needing custom DCGM fields, hybrid or on-premises coverage, specialized retention, or unified control across providers. |
In both models, verify that the service covers every failure domain you operate. A managed GPU metric stream does not automatically include scheduler queueing, storage latency, fabric errors, or application traces. Conversely, a self-managed exporter does not provide value if no one owns target health, upgrades, and incident response.
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How should dashboards and SLOs be designed?
Build dashboards around decisions, not hardware inventory. A useful top-level view has four layers.
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- GPU health: utilization, SM activity, memory, clocks, power, temperature, throttling, XID, and ECC state by node and device.
- Jobs and queues: queue wait, reservation time, running versus allocated GPUs, progress rate, retries, failures, and starvation.
- Inference: request rate, latency percentiles, errors, queue depth, saturation, cache behavior, and trace links for slow requests.
Define SLOs that match the service. Training platforms may track successful job completion, queue-wait objectives, and capacity utilization. Inference platforms need latency, success rate, availability, and saturation objectives. Record the hardware pool, model version, and workload class with each SLO so a change in model or GPU generation does not create a misleading baseline.
Use recording rules for expensive aggregations such as per-workload GPU-hours or queue percentiles. Keep high-resolution data for incident windows and downsampled history for capacity planning. Review the top-k metrics after incidents; remove panels and alerts that never lead to an action.
What are the common failure modes and recovery steps?
The exporter has no data
Check DaemonSet scheduling, node selectors, container logs, driver compatibility, permissions, and Prometheus target status. If only one node is missing, compare its driver, GPU count, and taints with a healthy peer. If all targets vanish, investigate the scrape configuration or monitoring control plane before blaming the GPUs.
Utilization is low but the job is slow
Inspect CPU throttling, I/O wait, data-loader latency, storage throughput, queue depth, and synchronization time. Compare a slow worker with its peers. Increasing GPU allocation without removing the upstream stall can increase cost while leaving step time unchanged.
A job fails with out-of-memory events
Identify the pod and GPU, inspect framebuffer usage over time, and correlate the failure with batch size, model version, precision, and concurrent processes. Verify Kubernetes limits and reservations, then apply the application team’s approved memory mitigation. Do not treat a one-time peak as proof of a leak without historical evidence.
An XID or ECC alert fires
Preserve the event timestamp, GPU identity, node logs, driver information, and affected workload. Follow the hardware runbook, which may require cordoning and draining the node, collecting diagnostics, or replacing the device. Repeatedly restarting the pod can hide a persistent hardware fault.
Distributed training degrades after scaling out
Compare interconnect traffic, link errors, synchronization time, and step duration across nodes. Check topology placement, PCIe and NVLink health, and the network path used by the collective library. A single congested or faulty link can dominate the job’s wall-clock time.
Inference latency rises with healthy GPU utilization
Open distributed traces and separate frontend, queue, preprocessing, model execution, postprocessing, and response time. Check cache behavior, request burstiness, queue saturation, and pod restarts. High GPU utilization can coexist with a latency SLO breach when batching or queueing is poorly tuned.
How do I control cardinality, cost, and retention?
Every distinct label set is a time series. Pod, namespace, and container labels are often useful; request IDs, timestamps, user input, and arbitrary annotations are not. Start with a small allow-list, measure active series and scrape size, and add labels only when an operator can name the decision they enable.
- Use stable identifiers for cluster, node, GPU, namespace, workload, job, queue, model, and service.
- Keep request-level identifiers in logs and traces, not metric labels.
- Aggregate per-device data for long-term capacity views while retaining detailed series for incident windows.
- Set retention according to use: high resolution for recent troubleshooting and lower resolution for trend analysis.
- Alert on scrape failures, ingestion lag, query latency, and storage growth as observability SLOs.
- Review cardinality after every new exporter label, model deployment, or multi-tenant onboarding.
There is no broadly applicable ROI percentage or independent benchmark that can predict the value of this design. The measurable outcomes are operational: shorter diagnosis time, fewer unowned alerts, earlier detection of hardware faults, clearer queue and capacity decisions, and SLO evidence tied to a specific workload.
What is a practical rollout plan?
- Map ownership. Assign owners for GPUs and drivers, Kubernetes and schedulers, network and storage, model services, and incident response.
- Instrument one GPU pool. Deploy DCGM Exporter, verify scrape continuity, and establish node and GPU identity.
- Add workload context. Join metrics to pods, jobs, queues, and services with a controlled label set.
- Choose baseline workloads. Record normal behavior for representative training, batch, and inference jobs across hardware pools.
- Implement action-oriented alerts. Start with XID and ECC faults, exporter loss, memory failures, thermal throttling, queue starvation, and inference SLO violations.
- Build linked runbooks. Include the owner, evidence to capture, safe containment, and escalation path for every alert.
- Expand failure domains. Add fabric, storage, scheduler, and trace data; test incidents that cross component boundaries.
- Review after incidents. Remove noisy rules, refine baselines, and promote the few metrics that consistently identify cause.
The result should be a repeatable path from symptom to owner: a workload-level alert opens a dashboard, the dashboard identifies the affected node or request span, and the runbook specifies the next safe action. That is the difference between GPU telemetry and AIOps observability.
Frequently Asked Questions
Can DCGM Exporter monitor GPUs outside Kubernetes?
DCGM itself is designed for data-center GPU management and monitoring, while DCGM Exporter is commonly used to expose metrics in Kubernetes. For non-Kubernetes hosts, use the host’s supported DCGM integration or another collector, then preserve the same node, GPU, workload, and cluster identity model when sending data to your time-series system.
How often should GPU metrics be scraped?
Choose the interval according to the failure you need to see and the cardinality your storage can support. Short intervals help capture rapid thermal, power, and inference changes; longer retention can use downsampled data. Benchmark ingestion and query load with representative labels instead of adopting a universal interval.
Do GPU metrics explain why a model is inaccurate?
GPU telemetry can reveal hardware faults, memory pressure, throttling, restarts, and data-pipeline stalls, but it does not explain model-quality changes by itself. Pair it with application logs, data and model version metadata, evaluation metrics, and traces when investigating accuracy regressions.
What should I do if managed monitoring omits a metric I need?
Confirm whether the omission is a provider-supported limitation, a disabled DCGM field, or a label and permissions issue. If the field is essential, run a self-managed exporter for that pool or supplement the managed service with a controlled collector, while keeping identities and alert ownership consistent.
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