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When AI inference suddenly slows or times out, first determine whether requests are waiting for admission, spending longer in prefill or model execution, or failing somewhere in the network timeout chain. Instrument each phase, compare p50, p95, and p99 values, then change one control at a time: queue limits, active sequences, batching, prefill scheduling, cache headroom, or replica capacity.
GPU utilization by itself cannot explain the incident. A GPU can report near 100% utilization while queue depth, time to first token (TTFT), and end-to-end latency continue rising. The workflow below applies to Triton-based services, vLLM deployments, and Kubernetes or cloud autoscaling systems.
What does an inference latency spike actually measure?
End-to-end latency is not one operation. It is the sum of waiting for admission, preparing the input, executing the model, producing the output, and transferring the response. Streaming language-model requests add separate first-token and per-token behavior, so an average completion time can hide a serious user-visible delay.
| Phase | What it represents | Typical symptom when it grows |
|---|---|---|
| Queue or admission | Time before a request gets a processing slot | Waiting requests accumulate; latency rises before GPU work starts |
| Input processing or prefill | Tokenization, transfer, and processing of the prompt | Long prompts produce high TTFT while short prompts remain acceptable |
| Model compute or decode | Forward passes that generate output tokens | Inter-token gaps increase and completion time expands |
| Output processing | Detokenization, post-processing, serialization, and response delivery | Server compute looks normal but client-perceived latency remains high |
| Network and timeout path | Gateway, ingress, service-mesh, load-balancer, and client deadlines | Requests are cancelled or reported as timeouts despite eventual server completion |
For streaming LLMs, track TTFT, prefill time, decode time, inter-token latency, and total time separately. vLLM defines end-to-end latency as the interval between frontend arrival_time and the frontend receiving the final token. Triton exposes request, queue, compute-input, compute-infer, and compute-output durations, allowing the same decomposition for non-streaming or streaming models.
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How should I confirm the scope of the incident?
Before changing configuration, establish which traffic is affected and where the delay begins. A single average can conceal a tail-latency failure caused by a small number of long-context requests.
- Record request rate, status codes, timeout counts, cancellation counts, and retry volume.
- Capture the model name and version, serving framework version, replica or pod, region, accelerator type, and deployment revision.
- Separate prompt-token and generated-token distributions. Note whether the problem follows long prompts, long outputs, a particular tenant, or a specific endpoint.
- Compare p50, p95, and p99 latency. Use the same time window for queue depth, GPU memory, KV-cache usage, and request counts.
- Classify the user-visible symptom: delayed first token, large gaps between streamed tokens, slow final completion, HTTP 503 responses, client-side deadline errors, or gateway idle timeouts.
- Check whether all replicas show the same pattern. One unhealthy replica can create apparent randomness as the load balancer sends requests to it.
Do not infer a universal timeout budget from a framework default. The appropriate budget depends on measured phase latency, prompt and output limits, network hops, and the operational margin required by your clients.
Which metrics should I inspect in Triton and vLLM?
Build a dashboard that shows both the distribution of latency and the work causing it. The metric names below are the documented signals to collect; graph rates and percentiles rather than only instantaneous values.
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| Metric | Use it to answer |
|---|---|
nv_inference_request_duration_us |
How long the complete request took |
nv_inference_queue_duration_us |
How much of that time was spent waiting for a scheduler slot |
nv_inference_compute_input_duration_us |
Whether input preparation or transfer expanded |
nv_inference_compute_infer_duration_us |
Whether model execution itself became slower |
nv_inference_compute_output_duration_us |
Whether post-processing or response preparation is the bottleneck |
Enable first-response histograms when first-token behavior matters. A high request-duration percentile with a low compute-infer percentile points toward queueing, input or output work, or infrastructure outside the model rather than a slower kernel.
vLLM metrics
| Metric or series | Diagnostic purpose |
|---|---|
vllm:e2e_request_latency_seconds |
End-to-end request distribution through final token reception |
vllm:time_to_first_token_seconds |
Admission plus prompt processing delay before streaming begins |
vllm:inter_token_latency_seconds |
Gaps between generated tokens during decode |
vllm:request_time_per_output_token_seconds |
Time spent per output token, useful for generation-rate regressions |
vllm:request_queue_time_seconds |
Time waiting before execution |
vllm:request_prefill_time_seconds |
Prompt-processing cost |
vllm:request_decode_time_seconds |
Generation-stage compute cost |
| Running, waiting, and swapped request counts | Whether active work is displacing queued work or being moved because of memory pressure |
vllm:kv_cache_usage_perc |
Whether the KV cache is approaching exhaustion |
Use a common time axis for these series. For example, a TTFT spike that coincides with waiting-request growth but not with decode-time growth is an admission problem. A stable queue with rising inter-token latency is more consistent with decode contention, memory pressure, or a compute regression.
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Is the request stuck in a queue or is the model slow?
Queue time is the first branch in the diagnosis. Google Cloud documentation states that “Queue size directly correlates to request latency.” A growing queue with a high queue-time percentile means demand is arriving faster than the configured admission and execution capacity can absorb it.
Signs of queueing or admission pressure
- Waiting-request count and queue-time p95 or p99 rise before compute durations do.
- TTFT increases while decode time for requests that have started remains comparatively stable.
- More requests are swapped or preempted as the scheduler protects memory.
- HTTP 503 responses appear when an in-flight or queued-request limit is reached.
- One replica has a disproportionate queue because of uneven load balancing or a failed worker.
Signs of compute or memory pressure
- Queue depth is modest, but compute-infer, prefill, decode, or inter-token latency rises.
- GPU memory and KV-cache usage approach their limits, with repeated preemption or swapping.
- Long prompts or unusually large generations correlate with the slow requests.
- All replicas show similar slow execution, suggesting a workload or model change rather than one bad instance.
During a load test, plot queue depth beside p95 end-to-end latency. A queue spike that precedes the latency increase is a useful overload indicator; a latency increase without queue growth directs attention to execution, memory, or downstream response handling.
How do KV-cache pressure and long prompts create timeouts?
Autoregressive serving must retain key-value state for active sequences. Long prompts consume cache during prefill, and long generations keep those sequences resident during decode. When cache headroom disappears, new requests can wait, existing requests can be swapped or preempted, and repeated scheduling work can amplify latency.
Checks to run
- Graph
vllm:kv_cache_usage_percagainst waiting, running, and swapped request counts. - Measure prompt-token and generation-token percentiles, not only their averages.
- Review maximum active-sequence and maximum queued-request settings.
- Look for repeated preemption, swap activity, or admission failures in server logs.
- Separate long-context or large-generation traffic when it causes head-of-line blocking for short requests.
Do not solve cache exhaustion by simply allowing an unlimited queue. An unbounded backlog increases memory used by request metadata and makes every client wait longer. Protect the service with explicit active-sequence and queue limits, then return a controlled overload response that clients can retry or route to a fallback.
How should I tune batching and scheduling?
Continuous batching raises utilization by interleaving requests, but a larger batch is not automatically faster for every user. Prefill work for a long prompt can compete with decode work that is supposed to deliver regular streamed tokens.
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Review these vLLM controls
max_num_seqsandmax_num_active_seqscap concurrent sequences. Lower values can protect TTFT and inter-token latency; higher values can improve throughput when memory allows.max_num_queued_reqsbounds waiting work. When the limit is reached, the service can return HTTP 503, so clients need retry, exponential backoff, jitter, or a fallback path.- The KV-cache watermark controls how much memory headroom remains before admission or scheduling becomes unsafe.
- The prefill scheduling interval determines how often the scheduler gives prefill work an opportunity to run. vLLM documents a default interval of 1; validate any change under your own prompt mix.
- Asynchronous scheduling and stream interval affect how promptly generated tokens are scheduled and delivered. Test them with realistic streaming clients rather than synthetic single-request calls.
Choose the objective before changing batch size
| Objective | Likely direction | Risk to watch |
|---|---|---|
| Maximum throughput | Permit more concurrent sequences and larger effective batches | TTFT and inter-token tail latency can rise |
| Tight first-token target | Reserve capacity for prompt processing and limit competing work | Lower utilization or lower total throughput |
| Stable streaming gaps | Prevent long prefill jobs from monopolizing decode scheduling | Long prompts may complete later |
| Predictable overload behavior | Use bounded active and queued work | More explicit 503 responses during bursts |
Change one setting, replay a representative workload, and compare p50, p95, p99 TTFT, inter-token latency, queue time, throughput, and rejection rate. A setting that improves average throughput but worsens the tail may be unsuitable for interactive traffic.
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Which autoscaling signals work better than GPU utilization?
AWS documentation notes that GPU utilization (DCGM_FI_DEV_GPU_UTIL) shows whether the GPU is busy, not how busy it is. A device can remain near 100 percent while demand varies widely, so utilization alone cannot indicate whether users are waiting or whether another replica is needed.
Combine at least one backlog signal with one user-facing latency signal:
- Queue depth or queue-time percentile: reacts to admission pressure and directly reflects waiting work.
- TTFT: useful for streaming LLMs where the first response token is the primary user complaint.
- End-to-end p95 or p99: captures completion experience, including long-tail requests.
- Batch size or active-sequence count: indicates scheduler saturation when queue metrics are not available.
- GPU utilization and memory: provide context, but should not be the sole trigger.
Google Cloud guidance distinguishes queue-size scaling for throughput and cost when the latency target is achievable from batch-size or latency-based scaling when response targets require faster action. As a starting experiment, queue thresholds around 3–5 are documented examples, not universal limits; tune them with load tests, cooldown periods, and the measured time required to start a new replica.
Prevent autoscaling oscillation
- Measure the delay from a scale-out signal to a ready model replica, including image startup, weight loading, and warm-up.
- Set the scale-out signal early enough that the new capacity arrives before the queue reaches its client deadline.
- Use a scale-in stabilization window so a short lull does not remove capacity while a backlog is still draining.
- Segment latency-sensitive and batch traffic when one workload would otherwise trigger scaling for the other.
How do I diagnose a hang or timeout in vLLM?
First determine whether the server is still progressing and whether the timeout was generated by the client or an intermediary. Use targeted diagnostics before enabling expensive tracing.
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- Set
VLLM_LOGGING_LEVEL=DEBUGandVLLM_LOG_STATS_INTERVAL=1.temporarily so request and scheduler progress appears frequently in logs. - For suspected CUDA kernel stalls, run an isolated reproduction with
CUDA_LAUNCH_BLOCKING=1. This makes kernel errors surface at the operation that launched them, but it changes timing and should not remain enabled in normal production serving. - For distributed-communication problems, enable
NCCL_DEBUG=TRACEduring a focused investigation and collect logs from every participating worker. - Use
VLLM_TRACE_FUNCTION=1only for a narrowly scoped case. The vLLM documentation warns that function tracing can make token generation more than 100 times slower. - Correlate request IDs across the client, gateway, ingress, service mesh, load balancer, and model server. Confirm whether cancellation reached the server or whether the server finished after the client had already abandoned the request.
- After collecting evidence, restore normal logging and disable diagnostic environment variables that distort performance or produce excessive logs.
A hang with no queue movement, no token progress, and no corresponding network error requires different investigation from a healthy server whose response was discarded by an expired gateway deadline.
How should I set and trace timeout budgets?
Trace the complete path rather than setting one large number at the client. Record the deadline at each hop: client library, API gateway or ingress, service mesh, load balancer idle timeout, server request timeout, and model-level cancellation.
- Measure p95 and p99 for queue, prefill, decode, output processing, and network delivery under the intended workload.
- Add an explicit operational margin for burstiness, replica startup, and transient retries.
- Ensure an upstream deadline is longer than the downstream operation it is waiting for; otherwise the request can be cancelled while the model is still working.
- Configure streaming idle timeouts based on the maximum acceptable inter-token gap, not only total completion time.
- Make cancellation observable and idempotent so abandoned work releases scheduler slots and KV-cache blocks.
- Return a clear timeout or overload status and include a request identifier for correlation.
The available framework documentation does not establish one universal timeout value. A defensible budget is therefore derived from measured phase percentiles and a stated margin, then revisited when model, prompt, traffic, or infrastructure changes.
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| Observed condition | First mitigation to test | Trade-off |
|---|---|---|
| Queue time and waiting requests rise | Add replica or GPU capacity; lower admission limits until capacity catches up | Higher cost or more 503 responses during bursts |
| Long prompts dominate TTFT | Isolate long-context traffic, enforce prompt limits, or reserve prefill capacity | Long-context requests may receive a separate service target |
| Decode gaps rise with active sequences | Reduce concurrency or protect decode scheduling from prefill work | Lower aggregate throughput |
| KV cache is near exhaustion | Reduce active sequences or context/output limits and restore cache headroom | Some requests must be rejected or shortened |
| 503 responses occur at the queue limit | Implement retry with backoff and jitter, fallback routing, and client-side load shedding | Retries add traffic if not bounded |
| Compute time rises on every replica | Check model or runtime changes, kernel behavior, accelerator health, and input shapes | May require rollback or a new capacity test |
| Server completes after clients time out | Align gateway, mesh, load-balancer, server, and client deadlines | Longer deadlines can hold resources longer |
What should an operational dashboard and alert policy contain?
Use separate panels for request volume, errors, queueing, execution, memory, and tail latency. Alert on sustained conditions rather than a single sample.
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- Request rate, success rate, HTTP 503 rate, cancellation rate, and retry rate.
- p50, p95, and p99 end-to-end latency, TTFT, inter-token latency, queue time, prefill time, and decode time.
- Waiting, running, and swapped requests; active sequences; and queue-limit saturation.
- KV-cache usage percentage, GPU memory, GPU utilization, and accelerator errors.
- Prompt and generation token percentiles, split by endpoint, tenant, model version, and replica.
- Replica count, pending scale-up time, readiness time, and load-balancer distribution.
Useful alert combinations include queue depth rising with TTFT p95, KV-cache usage remaining high with swapped requests, or p99 latency increasing while GPU utilization stays flat. A GPU-only alert misses the situation where the device is busy but users are waiting in an admission queue.
What is a practical incident runbook?
- Contain: stop an unsafe rollout, cap incoming concurrency, and preserve a small amount of cache and compute headroom.
- Classify: identify affected model versions, replicas, regions, prompt lengths, output lengths, and timeout layers.
- Measure: compare queue, prefill, decode, output, TTFT, inter-token, and end-to-end percentiles.
- Choose: decide whether to add capacity, isolate traffic, tighten admission, adjust scheduling, or correct timeout alignment.
- Validate: replay a representative load and confirm both latency tails and error rates improve without causing cache exhaustion.
- Document: record the triggering workload, metric evidence, configuration change, rollback condition, and the new alert threshold.
This sequence keeps a temporary overload response separate from a permanent capacity decision. It also prevents a misleading “GPU is busy” diagnosis from hiding queue growth or a gateway deadline that is shorter than normal model execution.
Frequently Asked Questions
Why does latency rise even when GPU utilization is low?
The bottleneck may be CPU tokenization, network transfer, output serialization, a single unhealthy replica, or a scheduler admission limit. Inspect queue, input, output, and per-replica metrics before assuming the accelerator is underused.
Should I retry an inference request after an HTTP 503?
Retry only when the response represents bounded overload and the operation is safe to repeat. Use exponential backoff with jitter, cap attempts, honor a remaining client deadline, and route to a fallback or lower-priority queue when available.
How can I tell whether long prompts are causing head-of-line blocking?
Break latency and queue metrics out by prompt-token bands, then compare short requests arriving during long-context bursts. If short requests show higher TTFT while decode metrics remain stable, separate routing or prefill controls can reduce the interference.
What should I preserve when collecting logs from a timeout incident?
Keep request IDs, model and runtime versions, replica identity, timestamps from every network hop, token counts, cancellation status, and the relevant scheduler and cache counters. This lets you distinguish a server hang from a response that finished after an upstream deadline.
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