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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsWhen an LLM suddenly feels slow, first separate time to first token (TTFT), inter-token latency (ITL, also called time per output token or TPOT), and end-to-end request latency. They measure different parts of a request. A long TTFT usually means work before the first token—queueing, prompt processing (prefill), or network delay—while a normal TTFT followed by slow streaming points more toward decode contention, GPU saturation, or interference from other requests. Total latency can also rise simply because the model generated more tokens or the client delayed delivery.
No single dashboard value proves a root cause. Correlate latency percentiles with queue time, prompt and output token counts, prefill and decode duration, concurrency, GPU saturation, KV-cache use, preemptions, and network timing. The sequence below gives you a disciplined way to isolate the slow stage without assuming that a vLLM metric name or tuning flag applies unchanged to every serving stack.
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What “LLM latency” actually measures
A request passes through several stages, and each stage affects the user-visible result differently. NVIDIA’s benchmarking documentation describes TTFT as generally including request queuing, prefill, and network latency. The model then enters decode, generating output tokens one at a time. Queueing, batching, network transfer, and client handling can add time around those model stages.
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| Metric | What it measures | What a regression feels like | First places to investigate |
|---|---|---|---|
| Queue time | Time a request waits before inference work starts | The request sits idle before any model activity | Concurrency, admission limits, batching, scheduler backlog |
| Prefill time | Time to process the input prompt and prepare the request for generation | A long pause before the first token, especially with long prompts | Prompt-token count, competing prefill work, GPU compute and memory pressure |
| TTFT | Time from request arrival until the first generated token is available; it generally includes queueing, prefill, and network delay | High initial wait, even if subsequent tokens stream quickly | Queue, prompt size, prefill duration, network path, concurrency |
| Decode duration | Time spent generating the output tokens after prefill | Tokens arrive with large gaps after streaming begins | Decode load, active sequences, GPU saturation, interference from context processing |
| ITL / TPOT | Time between generated tokens (or average time per output token, depending on the metric definition) | Slow or uneven streaming cadence | Decode behavior, concurrency, batching, prefill/decode contention |
| End-to-end latency | Total elapsed time from request submission to completion | The complete answer takes longer, regardless of where the delay occurs | All model stages, output length, queueing, network, and client-side handling |
Metric definitions are not interchangeable. For example, vLLM documents both a per-output streaming latency sample and request-level time-per-output-token measurements. Choose the one that matches the experience you are diagnosing, and record its aggregation method and units.
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For implementation details, see NVIDIA’s NIM metrics documentation, vLLM’s usage metrics reference, and the vLLM metrics design notes.
Why did my LLM latency suddenly spike?
A spike is usually a change in workload, scheduling, resource availability, or the request path—not a mysterious property of the model. The same average latency can hide very different failures, so inspect the following causes separately.
Queueing and concurrency increased
As simultaneous requests rise, the scheduler may hold new work while existing sequences consume the available batch and KV-cache capacity. Queueing raises TTFT before it changes token cadence. A traffic burst can therefore make the service appear slow even when the model’s per-token compute has not changed.
Throughput does not increase indefinitely. NVIDIA describes throughput rising with offered load until available GPU compute saturates. Past that point, additional concurrency generally creates longer waits, more contention, or both. Compare request rate, active sequences, queue time, and latency percentiles rather than treating a high request count as proof that the GPU is being used efficiently.
Longer prompts made prefill more expensive
Prefill processes the input context before generation begins. More prompt tokens require more work and can extend TTFT. A deployment can therefore regress after a product change that adds conversation history, retrieved documents, tool traces, or larger system instructions—even when output lengths and model weights are unchanged.
Group requests by prompt-token count. If TTFT rises with prompt size while ITL remains close to baseline, the regression is concentrated before or around the first token. Do not compare a short synthetic prompt with a production request that includes thousands of history or retrieval tokens.
Prefill work interfered with decode
In aggregated serving, context processing and token generation share resources. TensorRT-LLM documents that context processing can delay token generation, and that optimizing one metric such as TTFT can trade off against TPOT. A system tuned to admit large prompt batches may start the first token promptly for some requests yet create larger gaps between tokens for already-streaming requests.
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This is why a single “latency” number is inadequate: a change can improve TTFT while degrading ITL, or the reverse. Check whether long prompts arrived at the same time that streaming cadence worsened.
Decode became compute- or memory-bound
After the first token, each additional token requires another decode step. More active sequences, longer generations, or higher sampling and model-compute demand can increase decode duration. GPU utilization that appears high at the dashboard level does not identify whether the device is busy with prefill, decode, memory movement, or unrelated work.
Use per-stage measurements and concurrency labels where available. A regression in ITL/TPOT with stable queue time and prompt sizes points toward decode behavior or contention, but it does not by itself distinguish compute saturation from memory pressure.
KV-cache pressure reduced scheduling headroom
The KV cache stores attention state for active contexts and generated tokens. As residency grows, the server has less room to admit or batch requests. Pressure can show up as queueing, preemptions, allocation failures, or abrupt tail-latency growth before an outright out-of-memory error.
vLLM exposes cache-usage, residency, and preemption-related metric families. NVIDIA’s AIPerf v0.12.0 reference describes KV-cache utilization above 0.9 as a near-full-capacity signal; that is a tool-specific indicator, not a universal alarm threshold. Interpret it with the metric version, model, context length, and scheduler configuration in your deployment.
Memory sizing or context limits changed
A model upgrade, longer maximum context, larger maximum sequence count, or altered GPU-memory reservation can reduce the number of requests that fit concurrently. NVIDIA’s vLLM troubleshooting guidance lists CUDA out-of-memory causes related to model and context sizing and suggests, for that documented environment, reducing --max-model-len, --max-num-seqs, or --gpu-memory-utilization as possible troubleshooting actions. Those flags and their effects are runtime- and version-specific; verify them against the exact engine and deployment before changing production settings.
Network or client handling added delay
TTFT includes network latency in NVIDIA’s definition, and end-to-end time includes network contributions as well. A changed load balancer, proxy, TLS path, region, streaming buffer, or client read loop can therefore look like an inference regression. If server-side TTFT and ITL are stable but the user’s elapsed time increases, compare timestamps at request arrival, first byte, each streamed chunk, and client completion.
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Output length or traffic mix changed
End-to-end time naturally grows when the model generates more tokens. A product change that requests longer answers can make total latency worse while TTFT and ITL remain healthy. Similarly, a new tenant, endpoint, model route, or prompt template can alter the distribution of token counts and concurrency without changing the serving binary.
How do I troubleshoot vLLM latency?
Use this sequence during an incident. Preserve a known-good comparison window and change one variable at a time.
1. Confirm the regression and define its scope
- Choose a known-good baseline with comparable traffic and the affected time window. Record median and tail percentiles, not only an average.
- Split the data by model, engine or deployment revision, request route, region, tenant, and any other labels your telemetry actually records.
- Compare request rate, active concurrency, prompt-token counts, output-token counts, and error rates between the two windows.
- Check whether every request regressed or only a subset—for example, long-context requests, one model route, or one availability zone.
- Do not infer a cause from one dashboard-wide GPU-utilization value. Keep stage timing, queueing, token counts, and cache signals in the same time range.
The vLLM metrics reference is a useful starting point for the metric families to expose: https://github.com/vllm-project/vllm/blob/main/docs/usage/metrics.md.
2. Classify the symptom as TTFT, token cadence, or total time
| Observed pattern | Measure next | What it narrows |
|---|---|---|
| TTFT increased; ITL is near baseline | Queue time, prompt tokens, prefill duration, concurrency, network timing | Work before or at the first token |
| TTFT is acceptable; gaps between tokens increased | Decode duration, active sequences, GPU saturation, long-context arrivals | Generation or interference after streaming begins |
| TTFT and ITL are stable; total time increased | Output-token counts, batching, network transfer, client completion timestamps | More generated content or overhead outside the model stages |
| All metrics worsen only at high load | Queue depth, concurrency, KV-cache residency, preemptions, saturation | Capacity or scheduling boundary |
These are diagnostic directions, not proofs. For example, a network delay can affect both TTFT and total time, and a cache event can produce both queueing and decode interference.
3. Align tokens, traffic, stages, and cache on one timeline
Build a time-aligned view containing:
- Prompt tokens per request and generated tokens per request.
- Request rate, active concurrency, queue depth, and queue time.
- Prefill duration, decode duration, TTFT, ITL/TPOT, and end-to-end latency.
- KV-cache usage or residency, preemption counts, allocation failures, and out-of-memory events.
- GPU compute and memory indicators with the same labels and sampling interval.
Correlating these series prevents a common mistake: blaming a cache threshold because latency rose at the same time, when the actual change was a prompt-length or traffic burst. Conversely, a cache metric that stays near capacity while queueing and preemptions rise is meaningful evidence of a capacity boundary.
4. Check memory pressure, preemptions, and errors
- Search server logs for KV-cache allocation failures, preemptions, CUDA out-of-memory messages, and context-length rejections.
- Compare the affected requests’ context lengths with the configured maximum and with the known-good window.
- Check whether the number of concurrent sequences or memory reservation changed during a rollout.
- If the deployment matches NVIDIA’s documented vLLM environment, evaluate the documented mitigation candidates—lowering
--max-model-len,--max-num-seqs, or--gpu-memory-utilization—in a controlled test. Do not assume these settings have identical semantics in another runtime or version.
See NVIDIA’s vLLM troubleshooting guidance for the environment-specific OOM discussion.
5. Separate server timing from network and client timing
- Timestamp request submission in the client.
- Timestamp arrival at the serving endpoint, first response byte, each streamed chunk, and final response completion.
- Compare those timestamps with server-recorded queue start, prefill start and end, first-token emission, decode completion, and response flush where available.
- Look for proxy buffering, retries, connection reuse changes, TLS termination moves, or a client that reads the stream less often than before.
If server first-token and per-token measurements are unchanged but the client’s first byte is late, investigate the path between them rather than retuning the model.
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6. Reproduce with a representative workload
Use production-like prompt and output-length distributions, concurrency, request arrival patterns, and streaming behavior. A single short prompt at concurrency one cannot reveal queueing, cache pressure, or prefill/decode interference.
vLLM’s latency benchmarking documentation distinguishes an interactivity-oriented mode that favors low per-request latency from a throughput-oriented mode that favors aggregate tokens per second at high concurrency. Select the mode that matches the decision you are making, and report both user-facing latency and throughput when evaluating a change.
7. Change one variable and compare the same outcomes
- Keep model revision, hardware, prompt/output mix, concurrency, and request path fixed.
- Change one scheduler, batching, memory, or routing variable.
- Run enough representative requests to observe tail behavior, not just a few fast samples.
- Compare TTFT, ITL/TPOT, end-to-end latency, throughput, errors, preemptions, and cache use.
- Retain the change only if it improves the user-facing objective without creating an unacceptable regression elsewhere.
How to read common latency patterns
The following patterns are useful triage shortcuts after you have verified the metric definitions and aligned the time windows.
| Pattern | Likely interpretation | Next diagnostic step |
|---|---|---|
| TTFT rises with prompt length; ITL is stable | Prefill work is dominating the initial wait | Bucket by prompt tokens and inspect prefill duration and concurrency |
| TTFT rises with queue time; prompt lengths are unchanged | Scheduler or capacity pressure is delaying admission | Inspect active sequences, queue depth, batching, and cache residency |
| TTFT is stable; ITL worsens as concurrency rises | Decode is contending for compute or memory | Compare decode duration, GPU saturation, and prefill arrivals |
| Long prompts and streaming gaps appear together | Prefill may be interfering with decode in aggregated serving | Test prompt-length buckets and a workload with fewer concurrent long contexts |
| Total time rises; generated-token count also rises | The model is doing more output work | Normalize by output tokens and compare ITL/TPOT |
| Only client-observed timing worsens | Network, proxy, buffering, or client handling changed | Compare server and client timestamps at first byte and each chunk |
| Tail latency jumps with cache use, preemptions, or allocation failures | Memory capacity or scheduling headroom is being exhausted | Check context limits, sequence limits, cache behavior, and OOM logs |
Which configuration or architecture should you compare?
When you are choosing between serving configurations, compare them on the workload that matters rather than on one synthetic number.
| Comparison axis | Questions to answer |
|---|---|
| TTFT versus ITL/TPOT | Does the option improve the initial wait at the cost of slower streaming, or vice versa? |
| Throughput versus tail latency | At the target concurrency, how many tokens per second are delivered, and what happens to p95 or p99 latency? |
| Prompt and output lengths | Does performance hold for short prompts, long contexts, short answers, and long generations? |
| KV-cache capacity | How much headroom remains before queueing, preemptions, or allocation failures appear? |
| Shared versus separated prefill and decode | Does separating stages reduce interference for this traffic mix, and what KV-cache transfer overhead does it add? |
| Observability cost | Can you attribute regressions by stage without creating excessive metric-cardinality cost? |
TensorRT-LLM’s disaggregated-serving documentation makes the central trade-off explicit: optimizing TTFT can come at the expense of TPOT. It also describes KV-cache transfer overhead in separated arrangements. Treat disaggregation as a workload-dependent design choice, not a free latency improvement.
Practical remediation choices and their trade-offs
Reduce admission pressure
If queue time and cache residency rise together, reduce the number of simultaneously admitted sequences or shape traffic before it reaches the engine. This can improve TTFT and tail latency but lower peak throughput. Measure the resulting tokens per second and user-facing percentiles at the same arrival rate.
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Control prompt growth
Set explicit limits or policies for conversation history, retrieved documents, and tool traces when long prompts are the trigger. The correct limit depends on product quality and model context requirements; do not choose a universal token cutoff from latency alone. Validate answer quality and truncation behavior alongside TTFT.
Protect decode from long prefill work
If long-context arrivals coincide with streaming gaps, test scheduling or architectural changes that reduce prefill/decode interference. A separated design may improve one stage while adding cache-transfer overhead, so evaluate TTFT, ITL/TPOT, and total latency together.
Address memory headroom before chasing micro-optimizations
When preemptions, near-full cache utilization, or allocation failures appear, first restore safe capacity. In the documented NVIDIA vLLM environment, context length, maximum sequences, and GPU-memory-utilization settings are candidate controls. Apply them in a staging or canary test and verify behavior on your exact software version.
Use routing and admission policies deliberately
Separating traffic by model, context class, or latency objective can prevent a small number of long requests from dominating a shared queue. Keep the routing change measurable: compare each pool’s queue time, cache use, throughput, and tail latency, including the overhead of moving requests or transferring cache state.
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Detailed histograms are valuable for TTFT, ITL/TPOT, queue, prefill, and decode analysis, but every bucket boundary can create another time series for each metric and label combination. vLLM warns that custom histogram buckets increase monitoring-system cost. Keep bucket sets short and focused on the metric families you actively use for decisions.
Choose labels that answer operational questions—model, deployment, route, region, and workload class—without putting high-cardinality identifiers such as request IDs or arbitrary prompts into metric dimensions. Put those identifiers in traces or structured logs instead.
Record the metric definition with the dashboard: whether the value is per token or per request, whether it includes queue and network time, and whether the percentile is calculated at the server, client, or monitoring backend. Ambiguous labels can lead an otherwise correct team to fix the wrong stage.
Common troubleshooting mistakes
- Diagnosing from average latency alone: averages hide queue bursts and long-tail failures. Include percentiles and request cohorts.
- Calling every first-token delay “model speed”: TTFT can contain queueing, prefill, and network time.
- Using TTFT to judge streaming quality: ITL/TPOT describes the gaps after generation starts.
- Comparing unlike workloads: prompt length, output length, concurrency, and arrival pattern must be matched.
- Treating a runtime metric as universal: vLLM, NVIDIA benchmarking tools, and other engines may define similarly named measurements differently.
- Optimizing throughput while ignoring users: a higher aggregate token rate can coexist with worse TTFT or tail latency.
- Changing several flags at once: you lose the ability to attribute an improvement or regression.
- Assuming a cache threshold is a diagnosis: a near-full indicator is evidence to correlate with queueing, preemptions, request sizes, and errors—not a standalone explanation.
A compact incident checklist
- Compare the affected window with a known-good baseline using the same percentiles.
- Split by model, route, deployment, region, prompt length, output length, and concurrency where available.
- Classify the regression as TTFT, ITL/TPOT, end-to-end, or a combination.
- Correlate queue, prefill, decode, token counts, cache residency, preemptions, and network timestamps.
- Check for context-limit changes, memory failures, OOM events, and rollout differences.
- Reproduce with representative traffic and the same latency objective.
- Change one variable, then remeasure user-facing latency, throughput, and failure behavior.
- Document the metric definitions and workload so the next comparison remains valid.
Bottom line
LLM latency spikes become tractable when you stop treating latency as one number. Identify whether users are waiting for the first token, waiting between tokens, or waiting for completion; then correlate that symptom with queueing, prompt and output tokens, prefill and decode work, concurrency, KV-cache pressure, GPU saturation, and network timing. The evidence will narrow the next test, but only a controlled comparison on representative traffic can show whether a configuration change actually improved the service.
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