Monitor an AI inference service in layers: collect GPU health and utilization, scrape the serving engine’s request and latency metrics, then correlate both with queues, throughput, and failures. GPU utilization alone cannot tell you whether users are waiting, requests are failing, or the GPU is healthy. For NVIDIA deployments, a practical setup pairs DCGM telemetry with the metrics exposed by Triton or vLLM and dashboards and alerts tied to service objectives.
What to monitor—and why GPU utilization is not enough
A GPU utilization percentage describes device activity, not the complete health of an inference service. A busy GPU can coexist with growing request queues or poor tail latency; a service can also slow down because of host, network, fabric, or scheduling problems. NVIDIA’s guidance recommends looking across the stack rather than treating one metric as a root-cause diagnosis (NVIDIA full-stack observability guidance).
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Put signals into four related groups:
- Device and platform: GPU utilization, memory, power, and available health or error telemetry; add host, fabric, and scheduler signals when relevant.
- Service outcomes: successful and failed requests, throughput, and latency distributions such as p50, p95, and p99.
- Capacity and waiting: pending or waiting requests, running requests, and, for LLM serving, KV-cache use and preemptions.
- Collection health: target availability, scrape errors, expected metric presence, and exporter or server health.
Read these together. For example, rising tail latency alongside a growing queue points to a different investigation than rising latency with a flat queue and a device or platform health event.
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GPU telemetry with DCGM
For NVIDIA data-center GPUs, DCGM provides GPU monitoring and health telemetry. NVIDIA describes DCGM-Exporter as its Kubernetes-oriented integration and lists Prometheus as an integration option (NVIDIA DCGM). Collect per-device utilization and memory, plus power and health or error signals available for the GPU, driver, and software versions in your deployment. XID errors and fabric error rates are examples of signals NVIDIA highlights; fabric metrics are relevant when the architecture uses that fabric.
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DCGM covers the device layer. It does not replace metrics from the inference server, which show whether the device is serving requests effectively.
Triton metrics
NVIDIA Triton exposes Prometheus-format metrics for collection; it does not push them to a remote server. The default endpoint is http://localhost:8002/metrics, and Triton’s metrics options can configure exposure. Check the deployed server’s configuration and version before relying on that default (Triton Inference Server Metrics).
Useful metric families include:
nv_gpu_utilization,nv_gpu_memory_used_bytes, andnv_gpu_memory_total_bytesfor device activity and memory.nv_inference_request_successandnv_inference_request_failurefor request outcomes. Failure reasons can includeREJECTED,CANCELED,BACKEND, andOTHER. Triton documents a limitation for ensemble failure-reason granularity, so some ensemble failures may be labeledOTHER.nv_inference_pending_request_countfor requests received but not yet executing in a backend model instance.nv_inference_request_duration_us,nv_inference_queue_duration_us,nv_inference_compute_input_duration_us,nv_inference_compute_infer_duration_us, andnv_inference_compute_output_duration_usfor request and phase timing.
Triton’s duration metrics are cumulative counters, not individual request-latency readings. Calculate rates or deltas, or use an appropriate distribution from the metrics your deployment exposes, rather than interpreting the accumulated total as a single request’s latency. Triton also distinguishes per-request metrics from metrics updated on an interval: changing the metrics polling interval affects the latter, not the former. Account for scrape interval and update cadence when diagnosing apparent lag. For models that support batching, Triton documents average batch size as inference count divided by execution count (Triton metric definitions).
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vLLM metrics for LLM serving
For vLLM, use the metrics endpoint and metric names for the version actually running. Its documented metrics include vllm:e2e_request_latency_seconds, vllm:request_queue_time_seconds, vllm:request_inference_time_seconds, vllm:request_prefill_time_seconds, vllm:request_decode_time_seconds, vllm:time_to_first_token_seconds, and vllm:inter_token_latency_seconds. Together, these can help distinguish time spent waiting from time spent in request processing and token generation (vLLM Production Metrics).
Also watch running and waiting requests, KV-cache usage, and preemptions where available. NVIDIA AIPerf identifies KV-cache usage approaching 1.0 as an indicator to investigate for OOM risk; it is not a universal threshold or proof of the cause. If you customize vLLM histogram boundaries, choose buckets that reflect the service objectives you need to observe. Each boundary adds series per metric and label combination, increasing storage, scrape size, and query costs; keep bucket lists short and change only the metric families you need (vLLM Production Metrics).
Build dashboards around service questions
Arrange panels so an operator can move from user-visible symptoms toward likely causes:
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- Service overview: successful and failed request rates, throughput, latency distributions, and current service-objective status.
- Latency phases: total request latency beside queue time and the execution phases the deployed server exposes. For vLLM, include first-token and inter-token latency when useful.
- Capacity: running and waiting requests, pending requests, GPU utilization and memory, KV-cache use and preemptions for vLLM, and batching or execution behavior for Triton.
- Device and platform health: GPU power and health events, with host, fabric, or scheduler signals for deployments where those layers apply.
- Telemetry health: target availability, scrape errors, exporter or server process health, and whether expected time series are present.
Use failure reasons as investigation context, not as a complete explanation. A rising failure counter tells you to inspect corresponding backend and server logs; it does not by itself identify the underlying fault.
Set alerts that lead to an action
Keep the alert set small and connect each alert to a service-level indicator or objective and a response. NVIDIA’s observability guidance recommends prioritizing a top set of actionable metrics and mapping alerts to remediation. A high-level dashboard can support triage, with domain-specific interfaces used for deeper diagnosis (NVIDIA full-stack observability guidance).
Choose alert conditions using the latency objectives, traffic patterns, and failure budget of your own service; the cited documentation does not establish universal thresholds. Typical alert topics include sustained degradation in request latency or success rate, queue growth, device health events, and loss of telemetry. Treat a missing metric as a possible monitoring-path problem until collection is verified.
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Troubleshoot common symptoms
| Symptom | Compare | Next investigation |
|---|---|---|
| Tail latency rises while median latency remains acceptable | Waiting requests and latency distributions in vLLM; Triton queue duration and pending-request count. | A growing queue suggests investigating scheduling, concurrency, model instances, and serving capacity. NVIDIA AIPerf also describes vLLM waiting-request spikes as a queue-buildup signal (AIPerf Server Metrics Collection). |
| OOM or memory-related crashes | GPU memory, vLLM KV-cache usage, and preemptions. | Near-capacity cache use is a reason to investigate memory settings and workload length, not a standalone root-cause finding. AIPerf’s vLLM troubleshooting example suggests reducing max_model_len or increasing gpu_memory_utilization; validate the deployed version and workload before changing either setting (AIPerf troubleshooting guidance). |
| Throughput is low | Running versus waiting requests, successful request rate, GPU utilization, and relevant system or network signals. | AIPerf distinguishes a low-running, low-waiting pattern that may indicate a client bottleneck from high waiting that suggests a server bottleneck. Confirm the pattern against the rest of the stack rather than relying on one gauge (AIPerf Server Metrics Collection). |
| Failure counter rises | Triton failure-reason labels and backend or server logs. | Separate rejection, cancellation, backend execution errors, and other errors where labels provide that detail; use logs to investigate the cause. |
| Metrics disappear | Configured endpoint, scrape target, server state, network path, firewall, and response format. | Test the configured endpoint directly and verify that it returns Prometheus-format metrics. AIPerf documents endpoint and content-type checks for collection problems (AIPerf collection troubleshooting). |
| GPU utilization looks normal but service performance degrades | Request phases, queueing, GPU health, and node or fabric health. | Correlate layers: NVIDIA notes that degradation can originate outside the GPU, including in fabric or job scheduling (NVIDIA full-stack observability guidance). |
Choose components by the layer you need
These components have complementary roles rather than serving as interchangeable end-to-end monitoring products. AIPerf’s server-metric collection is described for benchmark-time collection and troubleshooting; it is not a substitute for deciding how to operate always-on monitoring.
| Component | Role | Check before adopting |
|---|---|---|
| DCGM / DCGM-Exporter | NVIDIA GPU telemetry and health monitoring; DCGM-Exporter is the Kubernetes-oriented integration. | GPU and driver support, available health signals, per-device visibility, deployment environment, and compatibility with the collector already in use. |
| Triton metrics | Request outcomes, queue and compute timing, and GPU or CPU metrics where enabled. | Serving version, per-model labels, batching behavior, metric update cadence, and failure-reason detail. |
| vLLM metrics | LLM queue, request phases, token timing, cache, and inference behavior. | Serving version, metric lifecycle, histogram resolution, and series-cardinality cost. |
| AIPerf server-metric collection | Benchmark-time metric collection and troubleshooting for compatible serving endpoints. | Whether the need is benchmark analysis or always-on operations, endpoint format, collection interval, and output requirements. |
| Prometheus and Grafana | Collection and dashboard layers in NVIDIA’s example monitoring stack. | Existing operational expertise, retention and cardinality requirements, alert integration, and ownership. |
The implementation details here are NVIDIA-centric. Metric names, defaults, availability, and deprecation behavior can vary by release, so verify them against the deployed GPU, exporter, and serving-engine versions.
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