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Reliable GPU operations require more than a utilization dashboard. Combine Kubernetes metrics, logs, and traces with NVIDIA DCGM telemetry, then attach every GPU sample to its node, pod, job, queue, and inference request. Prometheus-compatible collection and Grafana-style dashboards provide the raw visibility; AIOps adds workload baselines, cross-signal correlation, anomaly detection, ownership, and runbook links. This approach distinguishes a GPU-bound training step from a data-loader stall, fabric congestion, thermal throttling, or a scheduler problem. You can run the stack yourself with DCGM Exporter, Prometheus, and alerting components, or use managed integrations in Google Kubernetes Engine (GKE) and Azure Kubernetes Service (AKS). The right design covers GPU, host, network, scheduler, storage, and service-level objectives without creating unmanageable metric cardinality.
What does AIOps observability add to GPU monitoring?
AIOps observability turns telemetry into an operational decision. Kubernetes defines observability around metrics, logs, and traces; GPU operations need those signals joined to workload identity and analyzed across failure domains.
A dashboard may show that a GPU is at 20% utilization. Correlation explains whether the cause is CPU starvation, storage latency, a slow data loader, a full queue, communication overhead, or an intentionally sparse model. An AIOps workflow establishes a baseline for that workload, detects a meaningful deviation, groups related symptoms, identifies the likely owner, and points the on-call engineer to the next diagnostic step.
The signal chain
- Collection: Kubernetes components, nodes, containers, applications, DCGM Exporter, network devices, schedulers, and inference services expose telemetry.
- Storage: A time-series system retains metrics at a resolution and duration appropriate to training and inference investigations.
- Correlation: Labels and trace context connect GPU devices to clusters, nodes, namespaces, pods, jobs, queues, model versions, and requests.
- Detection: Rules handle known failure modes; anomaly detection compares behavior with a workload’s normal baseline.
- Action: Alerts include ownership, severity, time window, impact, and a runbook or remediation link.
How should a Kubernetes GPU observability architecture be built?
Use Kubernetes observability as the foundation, then add GPU-specific telemetry and workload context. The minimum useful architecture has a DCGM collector on every GPU node, a Prometheus-compatible scraper and time-series store, dashboards, alert rules, logs, traces, and an AIOps correlation layer.
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1. Instrument the cluster and workloads
Collect control-plane and node metrics, pod and container resource usage, object status, events, scheduler outcomes, and kubelet health. Training jobs should emit duration, step time, retries, failures, queue wait, reservation, and scheduling data. Inference services should expose request rate, latency, errors, saturation, cache behavior, and health endpoints. Structured logs should carry the same workload identifiers used in metric labels.
2. Deploy DCGM Exporter on GPU nodes
NVIDIA recommends DCGM Exporter for GPU telemetry in Kubernetes. Run it as a DaemonSet so each GPU node exposes local measurements, or install it through NVIDIA GPU Operator. The exporter converts selected DCGM fields into Prometheus exposition format. Kubernetes pod-resources integration can add pod, namespace, container, and virtual-GPU labels, allowing a GPU sample to be attributed to the workload using it.
3. Scrape and retain metrics deliberately
Configure the scraper to discover exporter endpoints and attach stable cluster and environment labels. Choose scrape intervals and retention based on the questions you must answer: short intervals help diagnose bursty inference latency, while long retention supports capacity and workload-baseline analysis. Remote or federated storage is needed when several clusters must be viewed consistently.
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4. Add logs and distributed traces
Metrics identify abnormal behavior; logs provide error details; traces follow an inference request through frontends, schedulers, workers, and model servers. Preserve trace context across queues and asynchronous workers where possible. A failed request should be searchable by request ID, model version, pod, node, and GPU allocation.
5. Build one triage view
NVIDIA’s 2026 AI-factory guidance organizes telemetry into platform health, GPU health and performance, network fabric, cluster and jobs, and inference services. A practical triage dashboard follows that order and highlights only the top metrics tied to service-level objectives (SLOs). Link each panel or alert to the runbook that explains the next check.
Which GPU and cluster metrics should be collected?
Collect enough metrics to explain utilization, health, capacity, and user impact. The following groups cover the common diagnostic paths.
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| Area | Metrics and events | What the signal helps determine |
|---|---|---|
| Compute | GPU utilization, streaming-multiprocessor (SM) utilization, clock frequency, and throttling indicators | Whether kernels are actively using the device and whether clocks are being limited |
| Memory | Framebuffer memory used and free, memory utilization, memory clocks, allocation failures, and out-of-memory events | Whether the workload is memory-bound, leaking memory, or exhausting capacity |
| Power and thermals | Power draw, temperature, thermal excursions, and abnormal power behavior | Whether cooling, power limits, or hardware conditions are reducing performance |
| Fabric and I/O | NVLink and PCIe receive/transmit activity, InfiniBand or Ethernet throughput, congestion, and error rates | Whether multi-GPU or multi-node communication is limiting progress |
| Hardware health | XID errors, ECC errors, GPU health status, and node-level hardware events | Whether a device or host requires immediate investigation or replacement |
| Kubernetes | Node readiness, allocatable GPUs, pod and container status, evictions, scheduling failures, and object events | Whether the platform can place and keep the workload running |
| Training and batch | Job duration, step time, retries, failures, queue wait, reservation, scheduling delay, and idle allocated GPUs | Whether capacity, scheduling, or data delivery is wasting allocated accelerators |
| Inference | Request rate, latency percentiles, errors, queue depth, saturation, cache hit behavior, and health checks | Whether the service is meeting its SLO and where request time is spent |
Microsoft’s AKS GPU documentation specifically lists utilization, SM utilization, framebuffer memory, memory clocks, power, temperature, NVLink, and XID errors. It notes that low utilization can indicate CPU or I/O bottlenecks and that XID errors require immediate investigation.
How can you tell whether an AI job is GPU-bound, memory-bound, network-bound, or stalled?
Classify a bottleneck by comparing GPU, host, memory, fabric, queue, and application signals over the same time window. No single utilization percentage is sufficient.
GPU-bound workload
High GPU and SM utilization, active clocks, sustained power draw, and little queue delay usually indicate compute saturation. Confirm that throughput or step-time SLOs are being met; a compute-bound job can still be unhealthy if it is throttling or repeatedly retrying.
Memory-bound workload
High framebuffer or memory utilization, elevated memory traffic, and lower-than-expected SM activity suggest memory pressure. Look for allocation failures, out-of-memory events, paging or transfer delays, and a working set that approaches device capacity. A job that repeatedly reaches the memory ceiling may need a smaller batch, activation checkpointing, or a different parallelism strategy; observability should identify the condition, not prescribe an unverified fix.
CPU or data-pipeline stall
Low GPU utilization combined with high CPU usage, I/O wait, data-loader latency, storage latency, or growing input queues points upstream of the GPU. Compare the time at which batches become available with GPU idle periods, and inspect worker pod throttling and node pressure.
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Network- or fabric-bound workload
In multi-GPU or multi-node training, low or oscillating GPU utilization with high NVLink, PCIe, InfiniBand, or Ethernet traffic can indicate communication overhead. Congestion, retransmissions, link errors, or an imbalanced topology strengthen that diagnosis. Correlate fabric metrics with collective-operation time and step duration rather than treating high bandwidth alone as an incident.
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Scheduler or capacity stall
Long queue wait, pending pods, failed placement, reservation conflicts, or insufficient allocatable GPUs indicate a scheduling or capacity problem. An allocated but idle GPU is a different failure: the scheduler placed the job, but the application is not consuming the reservation.
Hardware or thermal fault
XID or ECC errors, failed health checks, abnormal temperature, clock throttling, and unexpected power behavior require node- and device-level investigation. Preserve the GPU identity, node, timestamp, and preceding workload context so an operator can drain or quarantine the affected node when appropriate.
How do you connect GPU metrics to pods, jobs, queues, and requests?
Use a consistent identity model and keep labels bounded. The useful join path is cluster → node → GPU → namespace → pod → container → job → queue → model/request, with timestamps aligned across systems.
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- Cluster, region, environment, node, GPU index or UUID, namespace, pod, container, job, and queue are common operational dimensions.
- For inference, add service, model version, route, and a trace or request identifier in logs and traces rather than creating a metric time series for every request.
- Use virtual-GPU identity when a device is partitioned, but document how virtual devices map to physical hardware.
Control cardinality
Every unique label set creates another time series. High-cardinality labels such as unrestricted request IDs, user IDs, or dynamically generated pod names can increase memory, storage, query time, and cost. Keep metric labels to dimensions needed for aggregation and alerting; put detailed identities in exemplars, logs, or traces. Review series counts after each exporter or application change.
Normalize time and ownership
Use synchronized clocks and a shared naming convention for jobs and model versions. Add an owner label or metadata field that maps a workload to the team responsible for its code, data pipeline, or platform. Without ownership, correlation only produces a more sophisticated list of symptoms.
What alerts help an on-call engineer find the cause quickly?
Alert on sustained, actionable conditions and include enough context to start diagnosis. Workload-aware baselines are safer than one utilization threshold for every model.
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- Low utilization with upstream pressure: trigger when GPU utilization remains below that workload’s normal range while CPU, I/O wait, data-loader latency, or queue delay rises.
- Memory exhaustion: alert on high memory use, allocation failures, out-of-memory events, and persistent growth that differs from the job baseline.
- Thermal or clock throttling: alert on thermal excursions, reduced clocks, or power behavior outside the device and workload baseline.
- Hardware errors: page on XID or ECC errors, failed health checks, and repeated node-level faults; these often need immediate isolation and hardware investigation.
- Fabric degradation: alert on NVLink, PCIe, InfiniBand, or Ethernet congestion and error rates when the job depends on multi-node communication.
- Queue and allocation waste: alert on long queue wait, starvation, reservation conflicts, repeated retries, or GPUs allocated without corresponding process activity.
- Inference SLO violations: alert on latency percentiles, error rate, request saturation, queue depth, and cache behavior against the service objective.
Each notification should state the cluster, node, GPU, workload, time window, severity, owner, observed value and baseline, and a direct remediation link. Group alerts from the same node or job so one hardware fault does not create an unmanageable storm.
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Start with causal relationships and runbooks, then add statistical detection. A useful incident record connects the first symptom to dependent signals instead of ranking unrelated anomalies equally.
Build dependency-aware correlation
Model relationships among GPU devices, nodes, pods, jobs, queues, network links, storage paths, and inference services. If several pods on one node report XID errors and thermal excursions, group them as a node incident. If many nodes show normal GPU health but one queue has rising wait time, route the incident to scheduling or capacity ownership.
Use workload baselines
Compare a training job with previous runs of the same model, batch size, and topology. For inference, compare by model version, traffic pattern, and time of day. Detect changes in step time, request latency, memory footprint, cache behavior, and retry rate rather than relying on a universal GPU percentage.
Attach remediation evidence
A recommendation should identify the likely owner and the next diagnostic check: inspect a node’s DCGM health and kernel events for an XID alert; inspect data-loader and storage latency for low utilization with high I/O wait; inspect queue reservations and pending pods for scheduling delay; inspect fabric counters and topology for communication stalls. Keep automated actions conservative, such as opening a ticket or marking a node for human review, unless the organization has tested safe drain or rollback procedures.
Should you use managed GPU monitoring or run the stack yourself?
Managed services reduce platform maintenance, while self-managed components provide finer control. Choose by operational ownership, required metric customization, and multi-cluster design rather than by dashboard appearance.
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| Decision axis | Managed integration | Self-managed DCGM, Prometheus, and dashboards |
|---|---|---|
| Deployment effort | Provider supplies supported collection paths and integrations; prerequisites and version limits still apply. | Team installs and maintains exporters, scrapers, storage, dashboards, and alert rules. |
| Metric control | Convenient defaults, with customization bounded by provider behavior. | Full control over DCGM fields, scrape settings, labels, retention, and routing. |
| Upgrades | Provider manages much of the service; teams must track supported Kubernetes and driver versions. | Team owns GPU drivers, exporter upgrades, scrape health, compatibility, and rollback. |
| Multi-cluster view | Often integrated with the provider’s regional or global monitoring service. | Requires federation, remote write, or distributed time-series storage and consistent labels. |
| Cardinality and cost | Usage and retention are governed by provider pricing and collection behavior. | Team controls series volume and retention but pays in infrastructure and operations. |
| Best fit | Teams prioritizing faster adoption and fewer monitoring components to operate. | Teams needing custom fields, on-premises coverage, specialized routing, or uniform cross-cloud behavior. |
GKE managed Prometheus path
Google Cloud documentation describes installing DCGM Exporter on GKE and sending its metrics to Google Cloud Managed Service for Prometheus. Verify the supported GKE version, system-metrics collection, managed Prometheus collection, and managed GPU-driver prerequisites before rollout. Use self-managed DCGM when the required metric set or labeling model is outside the managed integration’s supported configuration.
AKS managed GPU path
Microsoft documents managed GPU metrics in preview using DCGM Exporter and Azure Managed Prometheus. Check the documented prerequisites and preview status for your region and cluster version, then validate the meaning and availability of utilization, memory, power, temperature, NVLink, and XID metrics before writing production alerts.
What is a practical implementation sequence?
Roll out in stages so every new signal has an operational purpose and a known owner.
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- Standardize identity: decide cluster, environment, node, GPU, namespace, pod, job, queue, model, and service labels before collecting at scale.
- Enable baseline Kubernetes telemetry: collect node, pod, container, object, scheduler, and event data, plus application logs and traces.
- Deploy DCGM Exporter: use a DaemonSet or GPU Operator, verify one endpoint per GPU node, and enable only the DCGM fields required for diagnosis.
- Validate attribution: run a known GPU workload and confirm that metrics map to the expected node, GPU, pod, namespace, and job.
- Add workload and service metrics: instrument queue wait, retries, job duration, request rate, latency, errors, cache behavior, and health endpoints.
- Build the triage dashboard: place platform, GPU, fabric, job, and inference panels together with links to logs, traces, and runbooks.
- Create workload-aware alerts: begin with hardware errors, failed health checks, memory exhaustion, queue delay, and SLO violations; tune thresholds against real baselines.
- Test failure paths: rehearse an XID event, a data-loader slowdown, a pending pod, a congested link, and an inference latency spike. Confirm that alerts group correctly and reach the right owner.
- Review cost and cardinality: measure series counts, scrape volume, query performance, retention, and cross-cluster storage before expanding labels or resolution.
What commonly goes wrong, and how do you fix it?
Most monitoring failures are design failures rather than missing dashboards. Check these conditions when telemetry does not lead to an answer.
- Exporter is present but no data appears: verify that every GPU node runs the DaemonSet, the endpoint is discoverable, DCGM can access the driver, and the scraper target is healthy.
- Metrics exist but cannot be tied to a job: enable pod-resources integration where supported, inspect label joins, and correct namespace, pod, container, and GPU identity mismatches.
- Queries are slow or storage grows unexpectedly: remove high-cardinality labels, reduce scrape frequency for low-value fields, and shorten retention for exploratory series.
- Low utilization pages constantly: replace a fixed threshold with a workload baseline and require corroborating CPU, I/O, queue, or data-loader evidence.
- Alerts are noisy during normal scaling: add pending and startup grace periods, group alerts by job or node, and distinguish planned capacity changes from failures.
- Hardware errors are ignored: route XID and ECC alerts separately from performance warnings, preserve device and node identity, and document the drain, quarantine, and escalation procedure.
- Inference dashboards miss user impact: add request rate, latency percentiles, error rate, queue depth, cache behavior, and trace links; GPU utilization alone cannot represent an SLO.
- Cross-cluster comparisons are misleading: standardize units, labels, driver and exporter versions, and topology metadata before aggregating.
What should a production readiness checklist include?
Use this checklist before declaring GPU observability operational:
- Every GPU node exposes exporter metrics and has a monitored scrape target.
- GPU, node, pod, job, queue, and inference identities join consistently.
- DCGM fields cover utilization, memory, clocks, power, temperature, fabric activity, XID, and ECC health.
- Kubernetes status, scheduler outcomes, storage and data-loader signals, logs, and traces are available in the same incident workflow.
- Training and inference SLOs are defined and represented by alerts.
- Alerts include time window, severity, owner, workload, and a remediation link.
- Cardinality, retention, scrape volume, and query cost are reviewed after changes.
- On-call exercises have validated runbooks for hardware, pipeline, fabric, scheduler, and service failures.
Frequently Asked Questions
Can DCGM Exporter monitor non-NVIDIA accelerators?
DCGM Exporter is designed for NVIDIA data-center GPUs and exposes DCGM fields. Other accelerator vendors require their own exporter or device-plugin telemetry, which must be normalized into the same workload and SLO model if you operate a mixed cluster.
How often should GPU metrics be scraped?
There is no universal interval. Select a resolution that captures the shortest incident you need to diagnose, then measure the resulting series volume and storage cost; use coarser collection for slow-changing health fields and finer collection for bursty inference behavior.
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Should request IDs be Prometheus labels?
Usually no. Per-request labels create a separate time series for every request and can overwhelm the metrics system. Keep request IDs in structured logs and traces, and use exemplars or links to connect a sampled request back to aggregate metrics.
How do I monitor GPU sharing or partitioning?
Collect the virtual-GPU or partition identity alongside the physical GPU identity, and document capacity and memory boundaries for each partition. Alerts should distinguish a partition’s limit from exhaustion of the underlying physical device.
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