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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesReliable AI serving observability requires three layers at once: inference-server metrics for request and token behavior, GPU telemetry for hardware activity, and traces for following an individual request. Scrape the server’s Prometheus-compatible /metrics endpoint, collect GPU data through DCGM Exporter, graph both in a shared dashboard, and add OpenTelemetry tracing when aggregate metrics cannot explain a delay.
The three signals you must monitor together
An inference service can appear healthy when viewed through only one metric. GPU utilization may be high while users wait in a queue. Average latency may look acceptable while a long-tail of requests times out. Generated tokens per second can rise because more requests are running even as each user receives a slower response. Monitoring works when request behavior, workload pressure, and hardware activity are viewed on the same time axis.
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| Layer | Answers | Typical signal types | What it cannot explain alone |
|---|---|---|---|
| Inference server | How requests and tokens behave | Latency histograms, token counters and rates, running and waiting requests, cache gauges | Whether a delay came from a specific downstream call or a particular request path |
| GPU telemetry | How the accelerator is being used | Utilization and other device measurements collected by DCGM Exporter | Whether users are waiting, decoding slowly, or receiving fewer tokens |
| Tracing | What happened during one request | Spans across the gateway, scheduler, model server, and other services | Long-term population trends unless traces are sampled and summarized |
The practical outcome is a dashboard that correlates latency distributions and token rates with queue depth and GPU utilization, plus a trace view for individual failures.
Define latency before you alert on it
“Latency” is not one measurement for a streaming language-model response. Use separate distributions for the complete request and for the phases a user experiences.
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End-to-end request latency
End-to-end latency runs from request acceptance until the response is complete. For a non-streaming response, this is usually the time until the full body is returned. For streaming, decide whether the measurement ends when the final token is emitted, when the connection closes, or at another documented application boundary. Keep that definition stable so a percentile remains comparable across releases.
Time to first token (TTFT)
TTFT measures the delay before the first generated token reaches the client. It includes work such as request admission, queueing, model prefill, and transport overhead. A high TTFT with normal token spacing usually indicates an admission, queue, or prefill problem rather than a decode-rate problem.
Inter-token latency
Inter-token latency measures the gaps between streamed tokens after generation begins. It describes the cadence a user sees while reading. A service can have excellent TTFT but poor inter-token latency if the decode phase is overloaded or repeatedly interrupted. Do not collapse TTFT and inter-token latency into a single “streaming latency” number.
Component timing
When the serving stack exposes them, inspect queue, inference, prefill, and decode timing separately. These components turn a symptom into a location:
- Queue time: time spent waiting for an available scheduler or execution slot.
- Prefill time: processing the prompt and constructing the initial state. Long prompts can increase it substantially.
- Decode time: incremental generation after the first token.
- Inference or execution time: model execution as defined by the server’s instrumentation.
Metric families and labels differ by server version. Treat the names exposed by the deployed build as authoritative rather than copying a dashboard designed for another release.
Define throughput with an explicit unit
Throughput is meaningful only when its numerator and time window are stated. Keep these measures separate:
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| Measure | What it counts | Useful for | Common interpretation error |
|---|---|---|---|
| Prompt-token throughput | Input tokens processed per unit time | Capacity planning for prompt-heavy workloads and prefill pressure | Calling it “generation speed” even though it excludes output tokens |
| Generated-token throughput | Output tokens produced per unit time | Comparing decode capacity and aggregate output production | Assuming a high aggregate rate means each request receives a high rate |
| Completed-request rate | Requests completed per unit time | Service-level capacity and arrival-versus-completion comparisons | Ignoring differences in prompt and output lengths |
| In-flight request count | Requests currently running | Concurrency and saturation context | Treating concurrency as throughput |
Use counters where possible and calculate rates over a defined interval in your monitoring system. Record whether a rate covers prompt tokens, generated tokens, or completed requests in the panel title and alert description. NVIDIA AIPerf documentation identifies generation throughput and queue depth as useful observations when collecting server metrics.
Expose and collect inference-server metrics
1. Verify the deployed endpoint
Most modern inference servers publish Prometheus-compatible metrics at an HTTP /metrics endpoint. Confirm that the endpoint is enabled, reachable from the collector, and protected from public internet exposure. Fetch it manually from an allowed network location and inspect the text output before building dashboards.
Record the server name, version, model identifier, and metric exposition format at deployment time. A metric visible in one release may be renamed, removed, or given different labels in another. The actual endpoint on the running build is the source of truth.
2. Configure a Prometheus scrape
Add the endpoint as a Prometheus target using the hostname and port selected for your deployment. Set a scrape interval that captures queue and latency changes without creating unnecessary collection load. Benchmark tooling may scrape frequently to characterize a short test; that cadence is not automatically a production recommendation.
Use stable labels such as environment, cluster, service, model, and revision. Avoid putting user IDs, request IDs, full prompts, or other unbounded values into metric labels. Those values create a new time series for every request and can overwhelm storage and query performance.
3. Inspect metric types and units
Before writing queries, identify whether each measurement is a counter, gauge, histogram, or summary. Counters need a rate calculation; gauges show the current state; histograms support percentile estimates when bucket boundaries are retained. Confirm whether durations are seconds, milliseconds, or another unit and whether token counters include rejected, cancelled, or only completed requests.
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Build a dashboard that connects demand, latency, and hardware
Organize panels so an operator can move from user impact to workload pressure and then to hardware evidence. A useful first page contains the following views:
- Request latency distributions: show median and high percentiles for end-to-end latency and TTFT. Include a tail percentile appropriate to your traffic volume; a percentile without its time window is not actionable.
- Inter-token latency: graph the distribution for streamed responses separately from TTFT.
- Latency components: show queue, prefill, decode, and inference timing when the server exports them.
- Token rates: display prompt-token and generated-token rates in separate panels, with completed-request rate beside them.
- Running and waiting requests: place both counts on one graph. A rising waiting count is a direct pressure signal; a high running count without a queue can indicate healthy concurrency or simply a large workload.
- GPU utilization: graph the DCGM Exporter signal for each GPU, alongside the server panels.
- Errors and cancellations: include request failures, timeouts, and cancellations if the server exposes them, because a falling completion rate can otherwise look like a throughput decline.
Use consistent time ranges and aligned legends. If a queue spike begins at 12:05, the operator should be able to see whether GPU utilization changed at the same time and whether TTFT or decode time moved first.
Collect GPU utilization as a separate telemetry layer
NVIDIA’s AIPerf tutorial documents a collection path using DCGM Exporter. The exporter publishes device telemetry that Prometheus can scrape, including the DCGM_FI_DEV_GPU_UTIL metric. Add this target independently of the inference-server endpoint, then join the data in dashboards by host, GPU, cluster, or another stable deployment label.
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GPU utilization is a device-activity measurement, not a user-experience score. Low utilization can accompany a software bottleneck, CPU-bound preprocessing, a small request batch, an idle workload, or a model that does not keep the device busy. High utilization can accompany excellent throughput or severe queueing. Always inspect utilization beside request latency, queue depth, and token rates.
Use per-device views
A cluster-wide average can conceal a single overloaded or unhealthy accelerator. Graph each GPU when practical, and include host and device identity in the legend. If the serving layer assigns work unevenly, compare per-GPU utilization with the number of active requests or model replicas on that device.
Add traces for request-level diagnosis
Metrics aggregate many requests into distributions and rates. They tell you that TTFT increased, but not necessarily which service or operation caused the increase for one request. vLLM documents OpenTelemetry tracing support; use tracing when you need to follow an individual request across a gateway, authentication layer, scheduler, model server, and downstream services.
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Useful span boundaries
- Request arrival at the API or gateway.
- Queue admission and scheduler wait.
- Prompt processing or prefill.
- Decode iterations or generation phase.
- First-token emission and final-token completion.
- Serialization, network transfer, and downstream tool calls when applicable.
Attach low-cardinality attributes such as model revision, route, region, and outcome. Do not place full prompts or generated text in spans by default; they may contain sensitive data and can dramatically increase telemetry volume. Sample enough traces to investigate tail latency while retaining aggregate metrics for all traffic.
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Compare monitoring approaches before choosing a stack
| Approach | Coverage | Signal | Overhead and limits | Best use |
|---|---|---|---|---|
| Inference-server metrics | Framework- and version-dependent | Request distributions, token/request rates, queue and cache gauges | Scrape volume and label cardinality must be controlled; no request path by itself | Continuous production monitoring and alerting |
| Prometheus plus Grafana | Any endpoint Prometheus can scrape | Stored time series, queries, dashboards, and alerts | Retention and query cost grow with series count and scrape frequency | Correlating server and GPU data over time |
| DCGM Exporter | NVIDIA GPU device telemetry | Hardware measurements such as DCGM_FI_DEV_GPU_UTIL |
Does not describe request semantics or user-visible latency | Connecting accelerator activity to serving behavior |
| OpenTelemetry tracing | Instrumented services and supported server integrations | Individual request spans and timings | Collection and storage overhead; sampling is usually required at scale | Explaining one slow or failed request |
| Benchmark collectors | Test workloads and supported server families | Controlled workload metrics and queue observations | Test scrape cadence and traffic shape may not represent production | Capacity experiments and release comparisons |
Diagnose common patterns
High TTFT, normal inter-token latency
Check waiting requests and queue time first. If the queue is growing, demand exceeds available admission capacity or concurrency limits. If the queue is flat but prefill time rises, compare prompt-token throughput and input lengths. A network or gateway delay before the first token is another possibility; a trace can distinguish it from server-side work.
Normal TTFT, slow inter-token latency
Focus on decode timing, generated-token throughput, running-request count, and per-GPU utilization. Decode contention can slow token cadence after admission. If utilization is unexpectedly low, investigate scheduling, batching, CPU overhead, or uneven device assignment rather than assuming the GPU is saturated.
High GPU utilization, acceptable latency
This may represent efficient use of available capacity. Check the latency tail and waiting queue before increasing hardware. A high device percentage is not itself an incident when user-facing objectives and queue behavior remain within limits.
High GPU utilization, rising queue depth
This is a stronger saturation signal. Confirm that generated-token and completed-request rates have not stalled, then examine decode and end-to-end latency. Capacity changes, workload shaping, concurrency limits, or model-level optimization may be required, but choose the remedy only after identifying whether prefill or decode dominates.
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Look for CPU preprocessing, request serialization, network waits, scheduler gaps, memory pressure, or an underfilled batch. Compare queue, prefill, and decode components and use a trace to locate idle time. Low utilization does not prove that adding GPUs will help.
Throughput falls while request count rises
Separate prompt and generated-token rates. A workload with longer prompts may reduce output production even as completed requests increase, while a workload with longer outputs can reduce request completion rate despite stable generated-token throughput. Keep the workload mix visible through labels or a companion log-derived view without creating unbounded metric labels.
Choose alert conditions that reflect user impact
Alert on a combination of symptoms rather than a single universal threshold. Thresholds depend on model, hardware, prompt and output lengths, batching policy, and service objectives.
- TTFT tail: alert when a high percentile exceeds the service objective for a sustained window.
- Inter-token tail: alert when streaming cadence degrades independently of TTFT.
- Queue growth: alert on a sustained increase in waiting requests, especially when paired with rising TTFT.
- Completion or error rate: alert when completed requests fall or failures and cancellations rise.
- Token-rate anomaly: compare prompt-token, generated-token, and request rates against the workload’s normal range.
- GPU imbalance: alert on a device that diverges sharply from peers when scheduling should be even.
Document the evaluation window, percentile, unit, model scope, and required operator action in each alert. A statement such as “generated-token rate below normal” is incomplete unless “normal” is defined for a specific model and traffic class.
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Control metric cost and cardinality
Latency component histograms can be valuable but expensive. vLLM documentation notes that large histogram bucket sets carry a cost. Retain buckets needed for the percentiles and objectives you actually use, and avoid duplicating the same histogram across high-cardinality labels.
Set retention according to the question you need to answer. Short retention may be sufficient for fast incident response, while capacity planning needs longer historical trends. Downsample or aggregate older data where your monitoring system supports it. Keep raw traces for a shorter period than low-cost aggregate metrics when storage or privacy constraints require that trade-off.
Version, security, and deployment checks
- Version alignment: check the exact serving-stack documentation and the live endpoint after every upgrade. Names, labels, histogram buckets, and availability can change.
- Network controls: expose
/metricsonly to authorized collectors. Metrics can reveal model names, workload volume, and infrastructure details. - Authentication and TLS: apply the same access controls and transport protections used for other internal observability endpoints.
- Clock synchronization: keep hosts and telemetry systems synchronized so traces and time-series events align.
- Failure behavior: monitor the collectors themselves. A missing scrape can look like zero traffic if dashboards do not distinguish “no data” from a measured zero.
- Label discipline: use bounded dimensions such as model revision and environment; never use request IDs or prompt content as ordinary metric labels.
A repeatable operating workflow
- Start with the server: inspect its live
/metricsendpoint and inventory latency, token, queue, and error measurements. - Write definitions: document what counts as TTFT, final completion, prompt tokens, generated tokens, and a completed request.
- Scrape and store: add the endpoint to Prometheus with controlled intervals, retention, and bounded labels.
- Add GPU telemetry: deploy DCGM Exporter for NVIDIA devices and collect
DCGM_FI_DEV_GPU_UTILalongside server data. - Build correlated panels: align TTFT, inter-token latency, component timing, token rates, queue depth, and per-device utilization.
- Exercise failure paths: generate or observe queueing, long prompts, long outputs, cancellations, and a device or collector failure so dashboards prove useful before an incident.
- Add tracing: instrument request boundaries with OpenTelemetry and sample enough slow or failed requests to explain outliers.
- Review after upgrades: compare the live metric inventory and dashboard queries with the new server version before declaring monitoring complete.
The result is an observability system that answers three different questions without confusing them: how users experience the request, how much work the service is completing, and how actively the GPUs are operating. Correlation across those views is what makes latency, throughput, and utilization data useful for capacity decisions and incident response.
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