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Find the slow stage before changing infrastructure. Measure p50, p95 and p99 against the service’s own baseline and SLO, then split streaming requests into time to first token (TTFT), inter-token latency and total completion time. Trace each request through network ingress, queueing, retrieval and tools, orchestration, serving runtime and model execution. Correlate those timings with queue depth, in-flight work, batch size, KV-cache utilization, token rates, GPU and node health, errors, and model or endpoint labels. Reproduce the suspected cause with the production workload shape, make one capacity or configuration change, and rerun the same test.
Start by defining what “high latency” means
A single average can hide the incident that users experience. Establish a known-good baseline for the affected endpoint and compare the current distribution with the service-level objective (SLO). Record at least p50 (median), p95 and p99 request latency for the same request class, region, model revision and time window. A rise in p99 with a stable p50 usually indicates a tail problem such as queueing, retries, a slow dependency or an unhealthy worker; a rise across all percentiles points to a broader capacity or execution change.
| Measure | What it tells you | How to use it |
|---|---|---|
| p50 | Typical request experience | Detects broad changes in normal execution. |
| p95 | Upper-tail experience for most users | Use when the SLO is stated as p95 end-to-end latency or when queueing affects a substantial minority. |
| p99 | Severe tail behavior | Useful for finding burst, retry, placement and noisy-neighbor effects that averages hide. |
| End-to-end latency | Time from client request to complete response | Use for the user-facing SLO, while decomposing it into stages to find the cause. |
For a streaming large-language-model endpoint, report three separate timings:
- TTFT: time to first token, including admission, queue wait, prompt processing and any startup work before the first token is emitted.
- Inter-token latency: the delay between generated tokens after streaming begins.
- Total completion time: time until the final token and response transfer complete.
These measures prevent a common misdiagnosis. Generation can be fast after the first token while users still wait because admission, retrieval, orchestration, queueing or model startup is slow. Tag every measurement with endpoint, model revision, tenant or request class, region, software and hardware labels, and whether the request was retried or served by a fallback.
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Instrument every stage of a request
End-to-end latency identifies the symptom, not the location. Add distributed traces or stage-level timers that preserve one request identifier from the client through the serving worker. The trace should let you separate the following stages:
| Stage | Useful timing or evidence | Typical interpretation |
|---|---|---|
| Client and network | DNS, connection, TLS, upload and response-transfer time; ingress-to-application delay | Slow before the request reaches the service, or while the response travels back. |
| Admission and queue | Time waiting for a worker or model instance; queue depth and in-flight requests | Demand exceeds immediately available serving capacity, or scheduling is uneven. |
| Retrieval and external tools | Individual dependency spans, timeout and retry counts | A search, database, tool call or dependency dominates TTFT or total time. |
| Application orchestration | Prompt assembly, filtering, policy checks and fan-out/fan-in spans | Control-plane work adds delay before or after model execution. |
| Serving runtime | Scheduler wait, batch formation, input copy, compute and output copy | The inference server is waiting, transferring data or executing a batch. |
| Model execution | Prompt processing, decode steps, token rate and model-specific timers | Prompt or output length, model size, backend or hardware limits compute speed. |
Microsoft’s AI application guidance recommends distributed tracing to attribute delay to retrieval, tool calls and orchestration rather than treating the entire application as one opaque span. Keep client-side and server-side clocks synchronized enough to make stage comparisons meaningful, and account for tracing overhead when comparing runs.
Use serving-runtime metrics when they are available
NVIDIA Triton Model Analyzer exposes server queue time, input-copy time, inference-compute time, output-copy time, client response wait and percentile measurements. These categories distinguish waiting for an available model instance from work performed after admission. For example, rising server queue time with stable compute time indicates contention for instances; rising compute time with little queueing points toward workload, model or hardware execution.
Metric collection can itself perturb a benchmark. Triton’s archived 26.00 documentation warns that CPU metric collection can affect latency and throughput measurements. Keep instrumentation settings identical for before-and-after tests, and run a control test with the extra collection disabled when you suspect measurement overhead.
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Correlate latency with inference-specific signals
Read related metrics over the same interval and filter them by endpoint, model and instance wherever possible. A dashboard showing only GPU utilization cannot explain a latency incident: a GPU can be highly utilized while serving efficiently, lightly utilized while requests wait on a dependency, or unevenly utilized because one worker is unhealthy.
| Signal | What a change can indicate | Required corroboration |
|---|---|---|
| Queue depth and queue wait | Requests are arriving faster than the runtime can admit them, or capacity is unavailable. | Compare with concurrency, batch size, throughput and p95/p99 latency. |
| In-flight requests or batch size | Concurrency and batching have reached a different operating point. | Check prompt/output lengths, queue wait, token throughput and errors. |
| KV-cache utilization | The engine may be approaching its ability to retain active sequence state; high utilization can precede latency spikes. | Check queue depth, active sequences, token rates and endpoint-specific latency. Do not use a universal threshold. |
| TTFT and inter-token latency | TTFT isolates admission and prompt-side work; inter-token latency reflects decode progress after streaming starts. | Filter by framework support and compare with prompt and output token distributions. |
| Token throughput | Shows useful work delivered, not merely device activity. | Relate to concurrency, model, batch, hardware and software version. |
| GPU utilization and memory | Shows device pressure, allocation and possible memory constraints. | Pair with queue, compute time, KV-cache, node health and errors. |
| Node and scheduler health | Placement, throttling, thermal or hardware problems can affect one worker or zone. | Compare workers, nodes, GPUs, placement and network path. |
| Errors, retries and fallbacks | Failed or repeated work inflates latency and can create a feedback loop. | Separate first-attempt time from retry time and identify the affected dependency. |
The AWS SageMaker AI OpenTelemetry reference lists framework metrics for vLLM and SGLang such as TTFT, inter-token latency, queue depth, batch or in-flight requests, KV-cache utilization, token throughput and end-to-end request latency. It also lists GPU and node measurements. Availability depends on the framework instrumentation in use, so an absent metric is not evidence that the underlying behavior is absent.
Google Cloud’s GKE inference guidance treats high KV-cache utilization as a clue that an engine is approaching capacity. Use it with queue, throughput and latency data, not as a standalone alarm. NVIDIA’s inference architecture likewise recommends retaining application and endpoint, serving-runtime, GPU, node, scheduler and network context so that a slowdown can be associated with a model, worker, tenant, GPU, node, placement or path.
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A repeatable troubleshooting workflow
1. Confirm the affected population
- Record the endpoint, region, model revision, deployment version and incident time window.
- Choose a normal comparison window with the same traffic class and calculate p50, p95 and p99 end-to-end latency.
- For streaming responses, calculate TTFT, inter-token latency and total completion time separately.
- Break slow and normal requests down by prompt or input tokens, generated or output tokens, concurrency, tenant, endpoint, error, retry and fallback behavior.
- Check whether the SLO breach affects all requests or only a model, region, worker, tenant or request size.
Prompt and output length matter because they change the compute required for the same model. Concurrency also changes batching, queueing and memory pressure. Do not compare a short, lightly concurrent test with a long, bursty production workload and call the result a capacity improvement.
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2. Locate the slow stage
- Open traces for a representative slow request and a normal request from the same class.
- Compare network, orchestration, retrieval, external-tool, filtering, queue, runtime scheduling, model-compute and response-transfer spans.
- Use serving-runtime queue and compute components to determine whether an admitted inference is slow or is waiting for an instance.
- Check dependency timeout and retry spans. A fast model span cannot compensate for a slow retrieval or tool call before it.
- Confirm that the measured span boundaries and clocks are consistent across clients and workers.
If the trace has only one application span, add child spans before changing GPU count or autoscaling. Capacity changes cannot fix delay that occurs in a database, tool, policy service or response path.
3. Correlate the explanation with system behavior
- Plot the latency percentiles and stage timings against queue depth, in-flight requests, batch size and token throughput.
- Overlay KV-cache utilization, GPU memory and utilization, node health, errors and scheduler placement for the same endpoints and instances.
- Compare healthy and unhealthy workers. A single outlier often indicates node, GPU, placement or network-path trouble rather than a global model limit.
- Check deployment events, model loading, worker restarts and cache state around the start of the incident.
- Write a falsifiable explanation, such as “queue wait rises only when active sequences and KV-cache utilization are high,” then test that relationship under controlled load.
4. Reproduce with a representative workload
Build a repeatable load test that preserves the factors that affect inference behavior:
- Production-like prompt and output-length distributions, not one fixed short prompt.
- Target concurrency, burst pattern and tenant or endpoint mix.
- The same model artifact or revision, backend, GPU type, software versions and serving configuration.
- Deployment and cache conditions, including whether workers are warm or starting.
- Instrumentation settings and sampling that are comparable between runs.
Record TTFT, inter-token latency, end-to-end p95 and p99, output throughput, concurrency, errors, queue depth, runtime queue and compute components, model size, token distributions, backend, GPU type and software versions. NVIDIA’s official guidance states: “Serving tests should record time to first token, inter-token latency, request latency, output throughput, concurrency, error rate, model size, prompt distribution, output distribution, backend, GPU type, and software versions.”
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5. Change one cause or capacity variable, then retest
- Choose the smallest change that tests the explanation: remove an unnecessary dependency, correct a placement issue, adjust batching, add serving capacity or change an autoscaling signal.
- Run the same workload and compare the same percentiles and stage components.
- Verify that throughput, error rate, cost and resource pressure remain acceptable; a faster p95 achieved by dropping requests is not a successful fix.
- Keep the result as the new baseline only after the service has stabilized across representative traffic.
Diagnose common latency patterns
High TTFT, normal inter-token latency
Look first at admission, queue wait, prompt processing, retrieval, tool calls, orchestration and model startup. Compare queue depth and runtime scheduling with dependency spans. If the first token is delayed only on cold workers, include model loading and cache conditions in the test. Once streaming begins, normal inter-token latency means decode capacity is not the primary user-visible delay for that request class.
Normal TTFT, high inter-token latency
Inspect decode compute, active sequences, batch composition, KV-cache utilization, GPU memory and token throughput. Longer output lengths and higher concurrency can increase work even when prompt admission is quick. Compare workers and model revisions, then test with the same output distribution and backend.
High queue wait with stable compute time
This pattern indicates contention for model instances or insufficient admission capacity rather than slower execution per request. Check in-flight requests, batch size, autoscaler reaction time, worker health and placement. If only one endpoint or tenant queues, use endpoint- and tenant-level labels before scaling the entire service.
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Compare prompt and output token counts, model artifact, backend, GPU type, software version and cache state. A model or runtime change can increase work per token; a different GPU or unhealthy node can reduce throughput. Validate with a controlled test rather than inferring cause from GPU utilization alone.
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High p99 with a stable p50
Investigate bursts, retries, dependency timeouts, noisy neighbors, uneven worker placement and isolated node or network problems. Tail traces are essential because averages and p50 can remain normal while a small but important population breaches the SLO.
Latency rises with KV-cache utilization
Treat the correlation as an approaching-capacity signal. Confirm that queue depth, active sequences, memory use, token throughput and p95 or p99 rise together. Test lower concurrency, more workers or a configuration that reduces per-request cache pressure, then measure whether the full latency distribution improves without unacceptable throughput or cost regression.
Capacity planning and autoscaling
Scale from the observed saturation point and the service objective, not from a generic utilization rule. AWS EKS guidance demonstrates using load-test observations to set queue-depth and p95 end-to-end-latency triggers, but those values belong to that demonstration workload and are not universal production thresholds. AWS Prescriptive Guidance similarly frames compute sizing and dynamic scaling around workload and objectives such as end-to-end latency or TTFT; prompt length, response length, concurrency, queue delay and utilization all matter.
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For each candidate configuration, hold the model and request mix fixed and compare:
| Comparison axis | What to record |
|---|---|
| User latency | TTFT, inter-token latency, end-to-end p95 and p99. |
| Capacity | Output throughput at target concurrency, queue depth and in-flight work. |
| Memory pressure | KV-cache utilization, GPU memory and eviction or admission behavior where exposed. |
| Reliability | Error, timeout, retry and fallback rates. |
| Reproducibility | Model artifact, backend, software version, GPU type, deployment state and cache conditions. |
After a material model, backend, hardware or workload change, establish a new measured baseline. A threshold copied from another region, framework or model can trigger too late, scale excessively or optimize the wrong metric.
Quick Recap
Common mistakes that prolong incidents
- Using only an average: percentiles expose tail failures and SLO breaches.
- Calling GPU utilization a diagnosis: pair it with queue, compute, memory, token and node signals.
- Measuring only total response time: TTFT and inter-token latency reveal whether the delay is before or during generation.
- Testing with unrealistic prompts: token-length distributions and concurrency determine the work being measured.
- Changing several variables at once: you cannot know which change helped, and a faster result may be caused by a different workload.
- Ignoring retries and fallbacks: repeated work can create the queue growth you are trying to explain.
- Comparing unlike environments: different model artifacts, GPU types, backends, software versions, cache states or instrumentation invalidate a before-and-after claim.
- Adopting example autoscaling numbers as policy: vendor demonstrations are workload-specific; load-test the target service first.
- Ignoring observability cost: metric collection and tracing can alter throughput or latency, so keep measurement configuration consistent.
Operational checklist
- Endpoint, model revision, region, tenant and incident window are recorded.
- Baseline and incident p50, p95 and p99 are calculated for the same request class.
- Streaming requests have TTFT, inter-token latency and total completion time.
- Traces cover network, queue, retrieval, tools, orchestration, runtime, model execution and response transfer.
- Queue depth, in-flight requests, batch size, KV-cache, token throughput, GPU, memory, node, scheduler and error metrics are correlated by time and endpoint.
- Slow requests are compared with normal requests by input tokens, output tokens, concurrency, retries and fallbacks.
- The load test records model, backend, GPU, software, deployment and cache conditions.
- A proposed fix is tested with the same workload and evaluated for latency, throughput, errors and cost.
- Autoscaling thresholds are derived from measured saturation and the service SLO, then revalidated after changes.
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