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High inference latency is usually a measurement and localization problem before it is a hardware problem. Start by isolating the affected model, endpoint, tenant or project, service tier, and time window. Then split latency into time to first token (TTFT), inter-token latency (ITL), queue time, and total request duration. Correlate those curves with prompt and output lengths, concurrency, cache state, memory pressure, upstream timeouts, and network evidence. Only after you identify the changed phase should you test a capacity, configuration, or serving change against representative production traffic and your application’s SLO.
1. Define exactly what became slow
“Latency” can mean several different user experiences. A chat user may notice a delayed first token, while an application that waits for a complete JSON response may care about end-to-end duration. A streaming client can have acceptable TTFT but still feel slow if tokens arrive irregularly. Begin with the user-visible symptom and write down the comparison point: a previous release, an earlier time window, or an unaffected deployment.
Filter the baseline before calculating anything
Compare like with like. Filter dashboards and logs to the same model, endpoint, service tier, project or tenant, region, and traffic period. Keep request IDs and client-side send and receive timestamps when available. Pooling different models or tiers can conceal a low-volume regression, or make a localized problem appear global.
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- Record the deployed model artifact, serving backend, software version, hardware allocation, precision, and autoscaling configuration.
- Separate streaming and non-streaming requests if both are present.
- Mark releases, routing changes, prompt-template changes, policy or safety-layer changes, and provider incidents on the same timeline.
OpenAI Help Center guidance recommends retaining request evidence and filtering by the dimensions that distinguish one workload from another. Its troubleshooting documentation states: “Always review P50 / P75 / P95 percentiles. Averages can hide real-user impact.”
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2. Use percentiles and error proportions
Plot p50 and at least p95 latency. Add p90 and p99 when your service-level objective or traffic volume makes those tails meaningful. The median describes a typical request; the tail describes users who encounter contention, unusually large prompts, long generations, retries, or a slow network path.
Track errors as a proportion of requests, not only as a raw count. Ten errors during 100 requests and 10 errors during 100,000 requests are operationally different. Keep separate series for timeouts, transport failures, provider HTTP statuses, application-level errors, cancellations, and retries.
| Measure | What it tells you | Common mistake |
|---|---|---|
| p50 | Typical request behavior | Declaring the service healthy while the slow tail deteriorates |
| p95 | Experience of a substantial slow-tail segment | Comparing different traffic mixes or windows |
| p99 | Extreme tail behavior when volume supports a stable estimate | Overreacting to a tiny sample |
| Error rate | Failure proportion over the same request population | Using a raw error count without the denominator |
Use the same percentile window, population filters, and timestamp definition in every comparison. A provider’s dashboard may measure server handling time, while your application measures queueing, retries, serialization, and network transfer as well.
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Time to first token (TTFT)
TTFT is the elapsed time from request submission until the first generated token is received. It includes waiting and input processing, and in a hosted API it can also reflect provider-side reasoning or other internal work. Prompt size is a major influence: a longer input generally requires more prefill work.
Inter-token latency and token velocity
Inter-token latency (ITL) is the interval between generated tokens. Some systems expose token velocity instead, usually expressed as tokens per second. A falling velocity or rising ITL means generation is slower after the first token, even when TTFT is unchanged.
End-to-end duration
End-to-end duration runs from the client’s request start to completion of the response. Output length, decoding speed, reasoning, downstream processing, and network transfer all contribute. A request can have a normal TTFT but a long total duration simply because it generates more tokens.
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Queue, prefill, and decode
For vLLM, NVIDIA’s AIPerf reference models end-to-end time as queue plus prefill plus decode. Queue is time waiting for GPU resources; prefill processes input tokens; decode generates output tokens. These names and boundaries are backend- and version-specific, so verify definitions for your deployed stack before copying metric names into a runbook. The AIPerf Server Metrics Reference identifies version 0.13.0 as the latest documentation version shown in the source material.
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| Pattern | Likely phase to inspect first | Evidence to collect |
|---|---|---|
| TTFT and queue time rise together | Queueing and capacity | Waiting requests, offered load, admission, concurrency, autoscaling delay |
| TTFT rises while queue is stable | Prefill or input processing | Prompt-token distribution, cache state, model or reasoning changes |
| ITL worsens | Decode | Output lengths, generation rate, GPU health, memory, competing work |
| Total duration rises while TTFT is stable | Decode or response path | Output-token count, downstream handling, transfer time, retries |
4. Follow a repeatable diagnostic workflow
Step 1: Capture a request-level record
For a representative sample, store a request ID, model and endpoint, tenant or project, service tier, region, client send time, first-byte or first-token time, final-byte time, status, retry count, input-token count, output-token count, and cancellation or timeout reason. Synchronize clocks and record the timezone. If privacy rules prevent retaining prompts, retain token counts, hashes, or approved metadata instead.
Step 2: Establish an unaffected comparison
Choose an earlier period with the same release and traffic profile, or an unaffected model, endpoint, tier, or tenant. Do not compare a quiet overnight window with a busy peak window. If no clean baseline exists, begin a controlled measurement with a fixed workload and record its limitations.
Step 3: Plot the latency components
- Plot p50, p95, and any SLO-relevant tail for TTFT, ITL or token velocity, and end-to-end duration.
- Plot queue time, prefill time, and decode time when the backend exposes them.
- Overlay request rate, concurrency, waiting requests, running requests, and autoscaler events.
- Overlay prompt and output-token percentiles, cache hit or reuse indicators, GPU memory, KV-cache use, preemptions, and hardware-health alerts.
- Overlay upstream HTTP statuses, application errors, client timeouts, load-balancer events, and network latency.
Step 4: Identify the first curve that changed
The first changed component is a useful lead, not proof. Queue growth can be caused by increased demand, reduced capacity, memory pressure, or an admission-policy change. Rising prefill can reflect longer prompts, a cache miss, a model change, or contention. Decode slowdown can reflect longer outputs, lower generation throughput, hardware faults, or another workload sharing the accelerator.
5. Diagnose queueing and capacity pressure
When TTFT and queue time rise together, inspect offered load and available serving capacity before changing application code. Check requests per second, concurrent requests, pending and waiting requests, running requests, scheduler or admission limits, replica count, autoscaling lag, and rate limits. A queue that grows faster than it drains indicates that arrivals temporarily exceed service capacity.
Signals that strengthen the queueing hypothesis
- Waiting-request count grows during the same interval as TTFT p95.
- Queue-time p95 rises while prompt and output distributions remain comparable.
- Autoscaling adds capacity late, or new replicas require a long model-load and warm-up period.
- Admission or concurrency limits reject or defer work despite apparently healthy GPU utilization.
High GPU utilization alone is not a diagnosis. A GPU can be busy with long prefill operations, decode work, memory management, or another tenant while requests still queue. Conversely, low utilization can coexist with poor latency when a memory limit, synchronization point, CPU bottleneck, or network wait is the actual constraint.
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Memory and KV-cache evidence
Review GPU memory, KV-cache utilization, allocation failures, and preemptions together. NVIDIA’s AIPerf reference interprets growing vLLM waiting requests as queue build-up, KV-cache use above 0.9 as near full, and preemptions as evidence of memory pressure. Those interpretations are documented backend guidance, not universal thresholds for every serving system. Confirm the metric semantics and limits for your version.
Memory pressure can increase latency before it produces an obvious out-of-memory error. Requests may be preempted, recomputed, admitted more slowly, or forced into smaller effective batches. Compare cache state and prompt-length distribution before concluding that adding replicas is the only answer.
6. Diagnose prompt, cache, and prefill changes
If TTFT rises while queue time is stable, examine input work. Plot prompt-token p50 and tail, system-prompt size, retrieved context size, tool or function descriptions, image or multimodal payloads where applicable, and cache-hit or prefix-reuse rates. A small average increase can hide a new population of very large prompts that drives p95.
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- A prompt template adds instructions, examples, schemas, or conversation history.
- Retrieval returns more documents or less aggressively trims context.
- Prefix caching stops matching because a supposedly static prefix changes.
- A model, tokenizer, precision, or serving-backend change alters input processing cost.
- Reasoning settings increase internal work before the first visible token.
Hosted APIs may not expose queue, prefill, or cache internals. In that case, use request-level TTFT, input-token counts, release timestamps, and provider support evidence. Do not infer a specific hidden phase from TTFT alone.
7. Diagnose decode and output-length problems
If TTFT remains stable but ITL worsens or total duration increases, inspect output-token distributions and generation throughput. Compare requests with similar model, prompt, and sampling settings. A product change that asks for longer explanations, larger JSON arrays, or more tool-call steps can increase end-to-end time without any infrastructure regression.
Check decode time, tokens per second, GPU health and memory, batch or concurrency behavior, and competing workloads. Keep maximum-output-token limits, stop sequences, reasoning settings, and streaming behavior consistent in the comparison. A longer output is a workload change, not automatically a serving failure.
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8. Check the network and application path
When application timeouts rise but provider-side errors do not, determine whether requests reached the provider. Compare client send timestamps and request IDs with provider request records. Inspect DNS, TLS setup, connection pooling, proxy and load-balancer queues, idle and request timeouts, retry policies, and response-body handling.
Distinguish timeout locations
- Before provider arrival: the client, proxy, load balancer, firewall, DNS, or network path may be failing.
- During provider processing: compare provider request duration and status with the client deadline.
- During response transfer: inspect streaming connections, buffering, idle timeouts, and downstream consumers.
- After a retry: separate the original attempt from the retry; retries can multiply load and make queueing worse.
Provider HTTP status and application-side status may differ. OpenAI guidance notes that HTTP 499 responses can appear as 5xx errors in a customer’s systems, so map statuses at each hop instead of treating one dashboard as authoritative.
9. Use a symptom-to-investigation map
| Observation | First checks | Interpretation limits |
|---|---|---|
| TTFT and queue time rise; waiting requests grow | Offered load, admission and concurrency, backend capacity, autoscaling lag, KV-cache pressure | Queueing indicates contention, but multiple resources may be involved. |
| TTFT rises while queue is stable | Prompt-token distribution, prefill stage, cache state, model, backend, and reasoning changes | Hosted APIs may not expose all phase internals. |
| ITL or token velocity worsens | Decode time, output lengths, generation throughput, GPU health and memory, contention | Compare equivalent traffic and serving-stack versions. |
| End-to-end time rises but first token is stable | Output-token count, decode time, downstream response handling, network transfer | Longer output can explain the increase without a TTFT regression. |
| Application timeouts rise but provider health has no matching errors | Client or proxy timeout, load balancer, network path, request-arrival evidence | Requests may fail before reaching the provider. |
| One model, tier, tenant, project, or endpoint is affected | Filter to that dimension and compare unaffected traffic | Aggregated dashboards can mask a localized issue. |
10. Account for service tiers and provider visibility
Compare service tiers separately. Standard service tiers can show wider latency variation and are not described in the cited OpenAI guidance as having guaranteed latency. Use the applicable service terms rather than extrapolating behavior from one tier to another.
OpenAI’s Service Health dashboard is described as available to Enterprise API customers. Do not assume every account can access it. Other users can still use application timestamps, request IDs, usage data, status information available to their account, and provider support channels. A “normal” provider dashboard does not disprove a client-side timeout or a tenant-specific regression.
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After forming a hypothesis, change one material variable at a time where practical. Establish a representative workload containing the real distribution of prompt lengths, output lengths, concurrency, cache states, model versions, and request priorities. Synthetic requests with one fixed prompt can miss the tail that caused the incident.
Keep the comparison controlled
- Use the same model artifact, backend and software versions, hardware class, precision, prompt and output profile, and cache conditions.
- Run long enough to include warm-up, steady state, autoscaling behavior, and representative peak concurrency.
- Compare p50, p95, p99 where meaningful, TTFT, ITL, total duration, throughput, concurrency, queue time, and error rate.
- Record cost, replica count, GPU allocation, and any change in batching or admission policy.
- Canary the change and define rollback thresholds before exposure.
Scaling, batching, GPU allocation, and serving-architecture changes can improve throughput while worsening latency, or reduce latency while increasing cost. Select the configuration that meets the application SLO, not a generic target copied from another workload.
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Illustrative sizing figures
Amazon Web Services presents an example workload target of TTFT below 1 second, end-to-end latency below 5 seconds, and queueing delay below 500 milliseconds. These are illustrative inputs in its right-sizing guidance, not universal SLOs or measured cross-platform results. Define your own objectives from the product experience, workload, and contractual requirements.
AWS also states: “Two deployments serving the same model can require significantly different infrastructure depending on factors such as prompt length, response length, concurrency, and latency objectives.” Treat that as a sizing principle: model name alone is not enough to select capacity.
12. GPU sharing, architecture, and observability pitfalls
NVIDIA’s inference reference architecture recommends caution with time-sliced GPU sharing for latency-sensitive large-language-model serving unless validation demonstrates that it meets the workload’s objectives. Time slicing can allow another workload to interrupt or contend with decode and prefill activity. This is architecture guidance, not proof that shared GPUs caused a particular incident and not an instruction to replace hardware without measurement.
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Correlate application, serving, GPU, and network telemetry on one timeline. A serving metric without request context cannot explain which users were affected; a client timer without queue or GPU evidence cannot identify the constrained resource. Ensure dashboards identify backend version and metric definitions, because AIPerf names and meanings are version- and backend-specific.
13. Build a durable latency runbook
Turn the investigation into a repeatable procedure so the next incident does not begin with dashboard archaeology.
Quick Recap
- Declare the symptom: affected SLO, percentile, model, endpoint, tier, tenant or project, region, and start time.
- Capture evidence: request IDs, synchronized client timestamps, statuses, retries, token counts, and release markers.
- Segment: compare affected and unaffected dimensions without pooling unlike workloads.
- Decompose: chart TTFT, ITL or velocity, total duration, and backend queue, prefill, and decode where available.
- Correlate: inspect request rate, concurrency, waiting and running requests, cache state, KV memory, preemptions, GPU health, network, and upstream errors.
- Form one hypothesis: state which changed phase or resource should improve if the hypothesis is correct.
- Run a controlled test: use representative traffic, fixed versions and cache conditions, and compare the same percentiles and error definitions.
- Roll out safely: canary, monitor the SLO and tail latency, and retain a rollback path.
- Document the boundary: note which internals were unavailable, which interpretations were backend-specific, and which evidence would distinguish competing causes next time.
14. What not to conclude from a single metric
- Do not equate high GPU utilization with the root cause; it may be expected work, and low utilization does not guarantee low latency.
- Do not treat average latency as representative of the slow tail.
- Do not call a longer total response a TTFT regression without checking output length and decode time.
- Do not infer provider health from an application dashboard that may include local queues, retries, or proxy timeouts.
- Do not copy a queue, KV-cache, or utilization threshold from one backend to another without verifying metric semantics.
- Do not promise that an illustrative sizing figure is achievable for every model, region, tier, or workload.
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