Do these 3 things before closing this tab:
1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsFind the slow stage before changing hardware or scaling. Measure the service’s normal latency distribution and SLO, split streaming latency into time to first token (TTFT), inter-token latency, and total completion time, then trace each request through network, queue, 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.
After you have a hypothesis, reproduce it with the same model revision, prompt and output lengths, concurrency, backend, GPU type, software versions, and cache conditions as production. Change capacity or autoscaling only at a measured saturation point, and validate the change against p95 and p99 latency, throughput, errors, and cost.
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Start with a bounded latency question
“The API is slow” is not a diagnosis. Write down the affected endpoint, request class, region, model revision, tenant or deployment, and the exact time window. Compare the affected period with a known healthy baseline using a distribution, not one average. At minimum, record p50, p95, and p99 end-to-end latency. Averages can hide a tail in which a small but important fraction of requests wait in a queue or retry repeatedly.
- p50 shows the typical request.
- p95 is useful for an SLO that covers most user requests.
- p99 exposes severe tail behavior, such as overloaded workers, cold starts, or intermittent dependencies.
For a streaming language-model endpoint, capture three separate values: TTFT, the delay before the first token; inter-token latency, the delay between generated tokens; and total completion latency. A request can have a healthy token-generation rate after admission while still violating its SLO because it spent too long waiting for admission, retrieval, orchestration, or model startup.
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Compare slow requests with normal requests by input-token count, output-token count, concurrency, tenant or endpoint, status code, retry count, and fallback behavior. Prompt and completion length change the amount of computation required, so a latency comparison that mixes short and long requests can point to the wrong cause.
Trace every stage of one request
Instrument the full path and preserve a common request identifier. The trace should show when the request enters the client and network, reaches ingress, waits for an application or model queue, performs retrieval or external tool calls, passes through orchestration and filtering, is scheduled by the serving runtime, executes model computation, and transfers the response back to the client.
Distributed tracing is particularly useful for AI applications because retrieval, tool calls, and orchestration can add substantial time outside the model. Microsoft’s AI application guidance recommends tracing these stages instead of attributing the entire end-to-end value to inference.
Use stage timers that add up
For every request, make the stage durations additive or explicitly mark overlap. A practical decomposition is:
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- Client and network: DNS, connection setup, TLS, request upload, transit, and response download.
- Ingress and admission: gateway processing, authentication, rate limits, and time waiting for an available worker.
- Application work: prompt construction, retrieval, external tools, safety or content filters, and orchestration.
- Serving runtime: scheduler delay, batching, input transfer, model-instance selection, and output transfer.
- Model execution: prefill, decoding, and any model-specific post-processing.
- Retries and fallbacks: time spent on failed attempts or alternate endpoints, recorded separately from the successful attempt.
Do not infer a slow stage from the end-to-end number alone. A 2-second request might contain 1.4 seconds of queue wait and only 0.6 seconds of compute, or the reverse; the remediation is different.
Use serving-runtime measurements where available
NVIDIA Triton Model Analyzer, including the archived 26.00 documentation, separates server queue time, input-copy time, inference compute time, output-copy time, client response wait, and percentiles. Those categories distinguish waiting for a model instance from work performed after admission. Triton also warns that collecting CPU metrics can perturb latency and throughput, so keep metric collection consistent between baseline and comparison runs and account for its overhead.
Read inference signals as a group
Collect application, runtime, accelerator, node, scheduler, and network context with the same timestamps and labels. NVIDIA’s inference architecture guidance recommends retaining the association among an application or endpoint, serving runtime, GPU, node, scheduler, and network path. This lets you determine whether a slowdown follows a model, worker, tenant, GPU, node, placement, or route.
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AWS SageMaker AI’s OpenTelemetry reference lists commonly available vLLM and SGLang measurements: 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 utilization and memory use plus node-level measurements. Availability depends on the framework and its instrumentation; TTFT and inter-token latency are not guaranteed for every backend.
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Google Cloud’s GKE inference guidance notes that high KV-cache utilization can indicate an engine approaching capacity and may come before latency spikes. Treat that value as a clue to investigate alongside queue depth, throughput, concurrency, and latency, not as a universal threshold.
| Signal | What it can indicate | What to check with it |
|---|---|---|
| Queue depth or queue wait | Requests are waiting for admission or a model instance. | Concurrency, batch size, available instances, p95 TTFT, and arrival rate. |
| In-flight requests or batch size | The scheduler is carrying more simultaneous work or forming larger batches. | Prompt and output lengths, compute time, queue growth, and error rate. |
| KV-cache utilization | Memory reserved for active sequences is under pressure. | Sequence lengths, evictions or preemption, GPU memory, queue depth, and token throughput. |
| TTFT | Admission, queueing, prefill, retrieval, or orchestration is delaying the first token. | Queue wait, input-token count, retrieval/tool spans, and model startup events. |
| Inter-token latency | Decoding or scheduling is slow after generation starts. | Output length, batch composition, GPU utilization, memory pressure, and runtime compute time. |
| Token throughput | The amount of generated work completed per unit time. | Concurrency, model and backend versions, GPU type, and output-token distribution. |
| GPU utilization and memory | Accelerator activity or memory pressure. | Queue and compute components, per-GPU distribution, throttling, and node health; utilization alone is not a diagnosis. |
| Errors, retries, and fallbacks | Extra attempts or degraded paths are inflating user-visible latency. | Status codes, timeout budgets, retry policy, dependency spans, and endpoint labels. |
Localize the bottleneck by symptom
High end-to-end latency, normal model timings
If serving-runtime compute and queue times are normal but the request is slow, inspect client, network, ingress, retrieval, tools, orchestration, filtering, and response transfer. Compare upload and download durations, connection reuse, DNS and TLS timing, dependency spans, and retry counts. A model change will not fix a gateway or retrieval delay.
TTFT rises while inter-token latency stays stable
This pattern points toward admission and prefill rather than decoding. Check queue depth, queue wait, in-flight requests, input-token count, batch formation, retrieval and tool spans, and model or worker startup. If the first token is delayed before any generation occurs, increasing decoder throughput alone is unlikely to improve the SLO.
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Inter-token latency rises after TTFT remains steady
Investigate decode scheduling and compute. Compare output-token lengths, batch composition, GPU memory and utilization, KV-cache pressure, runtime compute time, and per-worker behavior. A single unhealthy GPU or node can raise the tail while aggregate utilization appears acceptable.
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Queue depth and p95 grow with concurrency
You are approaching a capacity boundary, but the boundary depends on this model, request mix, backend, and hardware. Measure the concurrency at which queue wait, p95, or p99 begins to accelerate. Check whether larger prompts or longer completions move that point, and whether one endpoint or tenant is consuming the available capacity.
Latency spikes with high KV-cache utilization
Correlate the spikes with active sequence count, sequence lengths, preemption or eviction events, GPU memory, queue depth, and token rate. Reduce simultaneous long sequences, add capacity, or change batching only after a representative test confirms which variable is limiting. A cache-utilization percentage without those correlations cannot establish causality.
Only one model, worker, GPU, or node is slow
Filter every metric by model revision, endpoint, worker, GPU, node, zone, and network placement. Compare health, temperature or throttling indicators where exposed, memory errors, restarts, software versions, and request mix. Drain or replace an outlier only after confirming that its traffic and workload are comparable to healthy peers.
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Separate the first attempt from the overall user request. A dependency timeout, rate limit, or transient model error can create a long request even when the successful attempt is fast. Check retry count, backoff, fallback endpoint, timeout hierarchy, and whether the retry policy amplifies load during an incident.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Reproduce production conditions before changing anything
Build a repeatable load test that resembles the affected service. Record the test configuration alongside every result:
- Model artifact and revision, serving backend, runtime and driver versions.
- GPU type, number of replicas, placement, and node configuration.
- Prompt or input-token distribution and output-token distribution, including long-tail cases.
- Concurrency, arrival pattern, batching settings, and cache or warm-up state.
- TTFT, inter-token latency, p50/p95/p99 end-to-end latency, output throughput, queue depth, compute time, and errors.
- Deployment conditions such as cold versus warm workers, autoscaling state, and retrieval or tool dependencies.
NVIDIA’s inference guidance explicitly calls for recording TTFT, inter-token latency, request latency, output throughput, concurrency, error rate, model size, prompt and output distributions, backend, GPU type, and software versions. Include cache and deployment conditions when they affect startup or request behavior.
Run enough traffic to observe both the healthy region and the suspected saturation region. Keep instrumentation comparable: changing tracing, CPU metric collection, sampling, or client timing can change the result. Compare queue and compute components, not only the final latency percentile. A result from different hardware, software, model artifact, workload, or measurement configuration is not a clean before-and-after comparison.
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Choose the remedy that matches the measured stage
Client, network, or ingress
- Compare persistent and new connections, DNS and TLS time, request upload time, and response download time.
- Check regional routing, load-balancer queues, proxy limits, connection pools, and payload compression.
- Ensure client timeouts cover the intended server budget without triggering premature retries.
- Use the trace to verify that the slow interval is outside application and model spans before changing inference capacity.
Retrieval, tools, or orchestration
- Measure each retriever, database, tool, filter, and prompt-building span independently.
- Compare requests that call a dependency with requests that do not, while keeping token lengths similar.
- Investigate dependency queueing, cache misses, serial tool calls, and retry or timeout behavior.
- Set budgets for optional work so a slow dependency cannot consume the entire inference SLO.
Queueing and admission
- Relate queue wait and depth to arrival rate, concurrency, batch size, and available model instances.
- Check whether one tenant, endpoint, or request class is starving others.
- Test admission limits, fair scheduling, batching, and request prioritization with the same workload used to establish the baseline.
- Do not lower a queue limit merely to make the queue look smaller; rejected or retried requests can worsen user-visible latency.
Runtime scheduling and batching
- Separate scheduler wait, input-copy, compute, and output-copy times.
- Compare batch size and sequence-length mix at the same concurrency.
- Check whether batching improves throughput but harms TTFT or p99 beyond the SLO.
- Keep backend and runtime versions fixed while evaluating one scheduling change at a time.
Model execution and accelerator capacity
- Compare prefill and decode behavior using TTFT and inter-token latency.
- Check GPU memory, KV-cache utilization, per-GPU throughput, node health, and runtime compute time together.
- Evaluate model quantization, parallelism, or a different hardware class only with a controlled test that preserves request distributions and quality requirements.
- Account for model startup and warm-up when the service scales from zero or replaces workers.
Set scaling thresholds from observed saturation
A scaling policy should express the service objective, not a generic utilization number. AWS EKS guidance demonstrates choosing queue-depth and p95 end-to-end-latency triggers from a load test; its numerical values belong to that demonstration workload and are not universal production thresholds. AWS Prescriptive Guidance likewise 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 affect the result.
To derive a threshold, increase offered load in controlled steps and note where p95 or p99 leaves the SLO, queue depth begins persistent growth, KV-cache pressure rises, or error rate becomes unacceptable. Select a trigger that starts capacity changes before that point, allowing for provisioning and warm-up time. Test scale-in separately; removing a worker can create a queue surge or cold-start penalty.
| Configuration comparison | Keep fixed | Measure |
|---|---|---|
| Backend or runtime version | Model revision, prompt and output distributions, concurrency, GPU type, and cache state. | TTFT, inter-token latency, p95/p99, queue and compute time, throughput, and errors. |
| GPU or replica count | Backend, software versions, request mix, placement, and deployment warm-up. | Capacity at target concurrency, KV-cache pressure, per-GPU balance, cost, and tail latency. |
| Batching or scheduler setting | Model, hardware, arrival pattern, and instrumentation. | Batch size, queue wait, TTFT, inter-token latency, throughput, and SLO compliance. |
| Autoscaling policy | Load pattern, dependency behavior, warm-up assumptions, and timeout or retry policy. | Scale-out delay, queue growth, p95/p99, error rate, replica count, and cost. |
Validate and establish a new baseline
After a change, repeat the same load test and compare the full distribution and its components. Confirm that the intended metric improved without unacceptable regressions in throughput, error rate, quality, or cost. Check both normal and tail workloads, including long prompts, long completions, high concurrency, dependency calls, and cold-start paths when they are part of production.
Keep the old and new test records together, including model, backend, hardware, software, cache, and instrumentation details. A material change to any of those can invalidate the old baseline. Establish a new baseline after the change and update the SLO dashboard, alert thresholds, runbook, and capacity assumptions.
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Operational checklist
- Identify endpoint, region, model revision, request class, tenant, and incident window.
- Compare p50, p95, p99, TTFT, inter-token latency, and total completion time with the healthy baseline.
- Break traces into network, ingress, queue, retrieval or tools, orchestration, runtime, compute, and response transfer.
- Correlate queue depth, in-flight work, batch size, KV-cache utilization, token rate, GPU and node metrics, errors, retries, and labels.
- Classify the pattern: network or dependency, admission, prefill, decode, cache pressure, outlier worker, or retry amplification.
- Reproduce with production-like tokens, concurrency, model and software versions, backend, GPU, cache, and deployment state.
- Change one capacity, scheduling, dependency, or scaling variable at a time.
- Repeat the test, verify p95/p99 and SLO compliance, and document the new baseline.
Common troubleshooting mistakes
- Using only GPU utilization: a busy GPU can be efficient, while a low average can hide one overloaded device, queue wait, memory pressure, or a slow dependency.
- Using only average latency: averages conceal tail failures that users experience as timeouts.
- Combining TTFT with generation time: this hides whether admission or decoding is responsible.
- Testing with short synthetic prompts: the resulting capacity point may not apply to production’s token distribution.
- Changing hardware and software together: you lose the ability to attribute the improvement.
- Copying a vendor threshold: AWS and other platform examples are tied to their demonstrated workload; load-test your own service.
- Ignoring measurement overhead: tracing and CPU metric collection can perturb latency, so keep collection settings consistent.
- Scaling before confirming saturation: extra replicas will not fix a slow retrieval service, network path, or serial orchestration step.
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