GPU infrastructure needs observability at several layers: device health, utilization, memory behavior, power and thermals, PCIe and NVLink traffic, node resources, scheduler state, job context, network performance, storage throughput, and application progress. A single “GPU utilization” chart cannot explain why a training job slowed down or why inference latency increased. Operators need telemetry that connects hardware state to workload identity and service outcomes.
Observability is closest to the operations side of the certification map. NVIDIA AI Operations aligns directly with NVIDIA’s current NCP-AIO emphasis on monitoring, troubleshooting, and optimization, while NVIDIA Certified Associate AI Infrastructure and Operations supplies the foundational hardware and infrastructure context required to interpret those telemetry signals.
Use DCGM as a GPU operations foundation
NVIDIA Data Center GPU Manager provides tools and libraries for observing, managing, validating, and diagnosing data-center accelerators. It exposes device metrics, health information, profiling counters, diagnostics, and topology-related data.
DCGM should be treated as one source in a broader observability system, not as the complete cluster-monitoring solution.
Export GPU metrics into the cluster monitoring stack
DCGM Exporter can expose selected DCGM fields in Prometheus format and attach labels for GPU, MIG instance, Kubernetes pod, namespace, container, host, and other contexts depending on configuration.
This lets teams build dashboards and alerts that connect physical GPU behavior with workload ownership.
Track utilization with context
SM activity, tensor-pipe activity, memory utilization, clocks, and memory occupancy can help distinguish compute-heavy from memory-heavy workloads. Utilization should be interpreted over meaningful intervals rather than one instantaneous sample.
A GPU at 30 percent may be healthy for a bursty online inference service but inefficient for a long batch training job expected to saturate the device.
Monitor thermals and power
GPU temperature, memory temperature, power usage, clock events, and thermal or power throttling can explain performance degradation that looks like application variability.
Correlate these metrics with rack temperature, facility cooling, and workload placement. Dense clusters can develop environmental hot spots even when average room conditions appear normal.
Use XID and health events for hardware diagnosis
NVIDIA XID errors and DCGM health watches can indicate driver, memory, PCIe, NVLink, thermal, power, or hardware issues. Alerts should include GPU identity, host, workload, and time.
Not every XID has the same operational meaning. Runbooks should map important error classes to triage steps such as workload restart, node drain, GPU reset, diagnostics, or hardware replacement.
Monitor communication paths
PCIe and NVLink telemetry can reveal whether a distributed workload is moving data as expected or is limited by an unhealthy link. Network metrics add the scale-out view beyond the node.
A training slowdown with healthy SM utilization on some ranks and poor communication on others may point toward fabric or topology problems rather than a model-code regression.
Connect telemetry to scheduler and job state
Cluster-level dashboards should show which job, pod, user, namespace, or queue owns each GPU. Without workload labels, an operator can see a hot device but cannot identify who is affected.
Scheduler metrics such as pending jobs, allocation fragmentation, node drains, and MIG profile availability help explain why capacity exists physically but is unavailable to users.
Diagnostics should be used before and after maintenance. DCGM diagnostics and vendor validation tools can test device health before returning a node to service. Run them after hardware maintenance, driver changes, unexplained XIDs, or repeated job failures.
Baselines are useful. A node that technically passes but performs materially below its peers may still deserve deeper investigation.
Build SLOs for the infrastructure service
Operators should measure more than component uptime. Useful service metrics include GPU allocation success, queue wait time, job failure rate, effective utilization, mean time to repair, and percentage of healthy allocatable GPUs.
The broader performance KPI principle applies: metrics are valuable when they tell teams whether the infrastructure is delivering the service users expect.
Keep observability architecture resilient
Monitoring should remain available during partial cluster failure. Central collectors, dashboards, and logs need enough redundancy and retention to diagnose incidents after nodes disappear or reboot.
The NVIDIA operations model is strongest when telemetry links device, node, scheduler, network, storage, and application behavior on one timeline. That correlation turns GPU metrics into operational evidence rather than isolated graphs.
Observability should begin with an inventory model that identifies every GPU, host, rack, MIG instance, driver version, firmware version, and scheduler-visible resource. Metrics without stable identity are difficult to correlate when hardware is replaced or reconfigured.
DCGM metrics can expose clocks, temperatures, power, utilization, PCIe traffic, profiling counters, XID errors, NVLink health, and other device data depending on hardware and configuration. Build dashboards around questions rather than attempting to graph every available field.
A fleet overview can show allocatable GPUs, unhealthy GPUs, nodes under maintenance, temperature outliers, XID counts, and power constraints. Workload views can focus on utilization, memory, throughput, and job-level context.
GPU utilization is an interval measurement, not a guarantee of useful model work. A kernel repeatedly doing unproductive busy work can report high utilization, while a latency-sensitive service may look underutilized despite meeting its objective.
Tensor-pipe and SM activity help distinguish workload types, but these profiling metrics should be interpreted with application context. Use them for relative comparison and phase analysis rather than as a universal efficiency score.
Memory telemetry should include used memory, bandwidth activity where available, ECC errors, and OOM events. A workload can fail because memory capacity is exhausted even when compute utilization remains low.
Power metrics can reveal capped or throttled devices. Compare board power, configured limits, clock events, and thermal conditions. A GPU repeatedly hitting power-brake conditions may deliver less throughput than an otherwise identical peer.
Temperature should be evaluated against device and facility context. One GPU running warmer than its neighbors can indicate airflow blockage, fan issues, rack hot spots, or workload imbalance.
DCGM health status can watch categories such as PCIe, NVLink, memory, thermal, power, driver, and internal components. Alert routing should reflect severity: some events justify monitoring, others node isolation.
XID errors should be retained as events as well as counters. Operators need to know exactly when an error occurred, which job was running, and whether the same device has a history of similar failures.
MIG introduces another telemetry dimension. Metrics need labels for GPU instance profile and ID so teams can attribute utilization and faults to the correct tenant slice without losing the physical GPU relationship.
Time-shared GPU environments also need per-process or per-container context where available. Aggregate device utilization can look healthy even if one workload dominates the device and another experiences poor latency.
Kubernetes integration should attach namespace, pod, and container labels carefully. High-cardinality user labels can explode Prometheus series counts; collect only metadata needed for operations and chargeback.
Scheduler metrics should expose pending jobs, allocation failures, node labels, taints, MIG profile shortage, and preemption. These signals explain whether poor user experience originates from hardware, scheduling policy, or simple capacity shortage.
Network telemetry should be correlated with distributed jobs. Collect NIC throughput, errors, congestion indicators, and switch health alongside GPU traffic. A job stalled on collective communication can appear as falling GPU utilization across every rank.
Storage telemetry matters for input pipelines and checkpointing. Track read/write throughput, metadata latency, queue depth, cache hit rate, and mount health. GPU wait caused by storage should be visible without requiring application teams to prove it first.
Application metrics complete the picture. Training steps per second, loss progression, tokens per second, inference latency, queue depth, and request errors tell operators whether hardware health translates into useful service.
Dashboards should support drilldown from service to job to node to device. An SLO breach in inference latency should let an operator identify whether one replica, one GPU, one node, or the whole cluster is affected.
Alert thresholds need baseline and duration. A one-second temperature spike may not matter, while sustained clock throttling or repeated XIDs do. Avoid paging on every metric threshold without considering persistence and business impact.
Change annotations improve diagnosis. Mark driver upgrades, firmware changes, MIG reconfiguration, scheduler releases, network maintenance, and model deployments on the same timeline as performance metrics.
Logs and metrics should share identifiers. Node names, GPU UUIDs, pod IDs, job IDs, and rack labels let operators move from a metric anomaly to relevant system and scheduler logs quickly.
Retention should cover the typical time between failure and investigation. Some hardware problems are noticed only after a multi-day job finishes poorly, so one hour of high-resolution telemetry is insufficient.
Observability should be tested during failure. Disconnect a node, trigger a controlled workload error, or drain a GPU and verify that dashboards, alerts, labels, and runbooks point to the right component.
Good GPU observability reduces mean time to explanation. The platform team can distinguish hardware fault, power/thermal constraint, scheduler fragmentation, network stall, storage starvation, and application inefficiency with evidence instead of escalating blindly between teams.
Metric cardinality should be managed deliberately. Labeling every GPU metric with dozens of user-defined Kubernetes labels can overwhelm the monitoring backend. Preserve the dimensions needed for ownership and troubleshooting while keeping the series count sustainable.
Sampling frequency should vary by metric. Temperature and utilization may justify frequent collection, while firmware inventory or static topology changes slowly. Collecting every field every second increases monitoring cost without improving diagnosis.
Retention tiers can help: keep recent high-resolution metrics for incident analysis and downsample older data for trend and capacity planning. Long-term history is useful for identifying hardware degradation and seasonal demand even when per-second samples are unnecessary after several weeks.
Dashboards should compare peer nodes. A GPU running at 70 percent utilization is hard to interpret in isolation, but if seven peers in the same job run at 95 percent, the outlier becomes actionable. Peer comparison is one of the strongest ways to detect stragglers.
Observability should include maintenance state so drained or intentionally capped nodes are not mistaken for failures. Integrate scheduler maintenance labels and change windows with alerts to reduce false paging.
Runbooks should start from symptoms. “Training throughput dropped 30 percent” should map to checks for GPU clocks, utilization, XIDs, network, storage, scheduler placement, and recent changes. Operators should not need to memorize every DCGM field to begin a useful investigation.
Finally, validate the monitoring system itself. Missing metrics from one rack can be a telemetry outage rather than a perfectly healthy rack. Alert on exporter health, scrape freshness, and collector coverage so silence is not mistaken for normal operation.
Capacity planning dashboards should aggregate by model, team, queue, GPU generation, and rack. This helps operators see whether demand growth is concentrated in one workload class or whether the entire cluster is nearing saturation.
Utilization should be paired with useful-work metrics. A training cluster with 90 percent average GPU utilization but falling steps per second may be running inefficiently after a software or data-pipeline change.
Alert ownership should be explicit. Hardware alerts may route to platform operations, scheduler fragmentation to cluster administrators, and application regressions to model teams. One shared alert queue slows resolution because every issue starts with triage from scratch.
Runbooks should include evidence-gathering commands, expected healthy ranges, and escalation criteria. This makes response consistent across shifts and reduces dependence on the one engineer who remembers a rare GPU error code.
Observability is mature when a user complaint can be translated into a small number of evidence-backed hypotheses within minutes rather than hours.
Capacity trend reports should include queue demand as well as utilization. A cluster at 85 percent utilization with rapidly growing pending demand may require expansion, while the same utilization with little queueing may simply reflect healthy use of existing assets.
Telemetry should also support cost allocation. Mapping GPU-hours, power, storage, and scheduler ownership to teams can help identify workloads that are both expensive and inefficient without turning observability into punitive monitoring.
The strongest observability platform serves users and operators simultaneously: application teams can see why their job is slow, while infrastructure teams can see whether the root cause is device health, facility limits, scheduler policy, network, storage, or software change.
Observability should include software compatibility state. Driver, CUDA, container toolkit, GPU Operator, DCGM, and firmware versions can explain why two otherwise identical nodes behave differently. Version labels on dashboards make regressions after rollout much easier to isolate.
Change rollouts should use canary nodes where possible. Upgrade a small subset, compare health and workload telemetry with the existing fleet, then expand only when performance and error rates remain within expected bounds.
For inference platforms, service telemetry such as request queue depth, time to first token, tokens per second, and error rate should sit beside GPU metrics. This prevents operators from optimizing device utilization while user latency is getting worse.
For training platforms, pair hardware signals with steps per second, checkpoint duration, dataloader wait, and rank stragglers. The objective is to explain useful model progress, not merely device activity.
Observability ownership should be reviewed whenever a new metric source is added so alerts still route to teams capable of fixing the underlying component rather than accumulating in one generic operations queue.
Keep a small set of golden workloads for observability validation. Running them after driver, firmware, scheduler, or monitoring changes confirms that expected metrics still appear and gives operators a stable performance baseline for comparison.
That baseline keeps telemetry trustworthy after change.
Operators should also verify that dashboards preserve the labels needed to trace one anomalous GPU back to its host, rack, workload, owner, and current maintenance state.