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To measure cloud AI inference capacity, warm the endpoint, send it a representative workload at increasing concurrency or arrival rates, and record latency distributions, achieved throughput, errors, and resource use at each step. The result you need is not a single “fastest” number: it is the highest sustained throughput that still meets your chosen tail-latency service-level objective (SLO).
For a streaming language model, track time to first token (TTFT) separately from the time between generated tokens. Also report end-to-end request latency, since a fast model can still feel slow when requests wait in a queue or travel over a slow network.
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Measure the request from several angles. Each metric answers a different question: how long a complete request takes, when a user first sees output, how quickly the rest of the response arrives, and how much useful work the deployment sustains.
| Metric | What it measures | Why it matters |
|---|---|---|
| End-to-end request latency | Elapsed time from request admission or HTTP handling until the complete response. | Captures the full wait experienced within the measurement boundary, including any queue time inside that boundary. |
| Time to first token (TTFT) | Elapsed time from the request start to the first streamed output token. | For streaming LLMs, it is a key measure of perceived responsiveness. |
| Time per output token (TPOT) or inter-token latency (ITL) | The generation delay after the first token, measured between successive output tokens. | Indicates how quickly a streamed answer continues. Define the calculation used, because systems do not always use the labels identically. |
| Throughput | Requests per second and input, output, and total tokens per second. | Request counts alone can mislead when prompt or completion lengths differ. Output tokens per second is especially useful for comparing generated work. |
| Concurrency and queue time | In-flight requests and the time requests wait for a serving replica. | Shows whether rising load is being handled or simply accumulating in a queue. |
| Errors, timeouts, cancellations, and utilization | Failed or abandoned requests and the resource context in which the service ran. | Prevents a high throughput figure from concealing unreliable service or a result that depends on unreported resource conditions. |
Keep measurement boundaries explicit. Databricks distinguishes round-trip latency from model-computation latency and queue time; NVIDIA AIPerf exposes end-to-end latency histograms. A figure measured only around model execution cannot be presented as if it includes the entire client-to-service round trip.
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How are TTFT and TPOT different from total latency?
TTFT describes the wait before streaming begins. TPOT or ITL describes token-generation pace after that first token. End-to-end latency includes the request through completion, so it depends both on startup responsiveness and on how much output is generated. A long completion can therefore have an acceptable TTFT but a much longer total duration.
Choose one naming convention and document the exact formula and boundaries alongside results. For example, state whether TTFT starts when the client sends the request or when the server admits it, and how the token interval is summarized. This makes comparisons interpretable rather than treating similarly named measurements as interchangeable.
How do I design a representative cloud inference test?
A useful benchmark holds the deployment and workload definition steady while load changes. Without that control, a latency shift may come from different prompts, hardware, software, or routing rather than from concurrency.
Pin the deployment and quality conditions
Record the model identifier and version, serving framework, quantization, hardware type and count, cloud region, software image, sampling settings, prompt dataset, output-token cap, and quality target. Also note replica count, batching and scheduling configuration, network path, timeout policy, and the success criteria used to count a request as successful.
These details are part of the benchmark, not incidental metadata. A result for one accelerator topology or region does not establish performance for another, and changing quantization or the quality target can change what work is being compared.
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Warm the service and define the workload
Warm the endpoint before collecting measurements, then exclude cold-start observations unless the test is specifically about startup behavior. Use a prompt and output length distribution that resembles the intended production workload; a test made entirely of short prompts will not represent a service receiving long contexts and completions.
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Google Cloud published an example token-length distribution in 2026. It is a concrete starting point, not a universal workload:
| Token count | Input tokens | Output tokens |
|---|---|---|
| Minimum | 4 | 1 |
| Median | 108 | 132 |
| Mean | 226 | 195 |
| P90 | 635 | 488 |
| P99 | 887 | 778 |
| Maximum | 1,024 | 1,024 |
Use your own observed distribution when it is available. If you adopt a published example instead, identify it as the test workload rather than implying it describes all LLM traffic.
Sweep load in controlled steps
- Establish a baseline. Run the warmed service at low load and capture the same metrics planned for the full test.
- Increase concurrency or arrival rate. Add load in defined steps. At every step, record offered load as well as achieved load, since a generator can offer more work than the service successfully completes.
- Allow steady state and collect a useful sample. Run each step long enough for behavior to stabilize and for the sample to support the percentile estimates you plan to report. Record the duration and request count; do not present a percentile without its observation window.
- Capture service and workload context. Record token counts, in-flight requests, queue time, errors, timeouts, cancellations, and utilization together with latency and throughput.
- Mark the SLO boundary. Stop or flag a load point when it violates the selected tail-latency SLO. Google’s accelerator guidance recommends increasing batch or concurrency until the P99 SLA is breached, then recording sustained TPS per chip at the last compliant point.
- Repeat the run. Report variability across runs instead of selecting only the most favorable result. When possible, test prefill-heavy and decode-heavy workloads separately because they stress different bottlenecks.
Concurrency and arrival rate are alternative ways to describe offered work; say which one the test controls. The key comparison at each step is offered versus achieved load alongside the resulting queues, latency, and failures.
How should I measure p95 and p99 tail latency?
Report a latency distribution, not just an average. At minimum, publish p50 and either p95 or p99 for request latency; for streaming models, report TTFT and ITL/TPOT percentiles as well. Include the observation count, time window, units, and percentile method so a reader can judge what the numbers represent.
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Hugging Face defines p99 as the value below which 99% of requests fall. The distance between the median and p99 is useful: a large gap indicates that a substantial minority of requests experience much longer waits than the typical request. This is why the tail can determine usable capacity even when the mean or median looks healthy.
Use histograms when results span replicas
Instrument request durations as histograms. Prometheus documents that histogram buckets can be aggregated across replicas and queried with histogram_quantile(); averaging precomputed summary quantiles from individual replicas is statistically unsound. OpenTelemetry defines Histogram as the instrument for request-duration distributions and percentile analysis.
Choose bucket boundaries that cover the actual latency range. Default millisecond buckets may not resolve multi-second generation requests well. If buckets are too coarse or end before the observed durations, the resulting distribution cannot describe the tail accurately. Preserve the bucket data needed for aggregation rather than retaining only a percentile calculated on each instance.
Show enough context to interpret the tail
Label whether each latency is client round-trip, server end-to-end, queue, or model-computation time. Report the number of requests and the measurement window with every percentile set. For streaming measurements, specify whether a percentile is taken across requests or across token intervals; those are different populations and should not be conflated.
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How do I find the highest throughput that still meets the SLO?
Plot achieved output-token throughput against concurrency, and add request throughput where it is meaningful. Overlay p95 or p99 latency and mark the SLO boundary. The useful operating point is the highest sustained throughput before tail latency violates the SLO—not the highest load the service can be made to accept.
As load rises, throughput may improve at first, then flatten as resources become constrained. Beyond that point, extra offered load can increase queueing and sharply worsen tail latency without adding much useful throughput. Google describes selecting an “ideal zone” on the latency-throughput curve for an SLO-constrained operating point.
MLPerf Endpoints treats serving as a Pareto frontier across concurrency rather than a single universal score. Its documentation says: “Endpoints measures that whole tradeoff as a Pareto frontier across concurrency levels.” Its required fields include system throughput, per-user output rate, first-token latency, and concurrency. This framing is useful because a deployment may trade individual responsiveness against aggregate work served, and the best choice depends on the SLO.
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For capacity planning, identify and retain the load steps on both sides of the SLO boundary. The compliant step shows the operating point to use; the first violating step shows how close additional offered load is to turning into unacceptable delay. Google’s guidance recommends recording sustained TPS per chip at the last P99-compliant point.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How can I compare two cloud inference deployments fairly?
Compare deployments only after matching the work and the measurement rules. MLPerf’s benchmark suite formalizes scenarios, latency constraints, throughput metrics, and quality targets to make comparisons reproducible. For an internal comparison, use the same controls even if you are not running a formal benchmark.
- Use the same model weights and tokenizer.
- Match prompt and completion token distributions, including maximum lengths.
- Keep batching, scheduling, quantization, and precision the same.
- Match hardware topology, replica count, cloud region, and network path—or clearly state every difference.
- Use the same warm-up approach, run duration, concurrency steps, timeout policy, and percentile window.
- Hold the quality target and success criteria constant.
Report the deployment identity with the measurements: p50, p95, and p99 request latency; TTFT and ITL/TPOT; achieved output tokens per second; concurrency at the SLO boundary; error and timeout rate; input and output token distributions; accelerator and replica count; region and network path; and quality target. Add cost per million tokens if cost is part of the decision.
A warm single replica and a cold, autoscaled multi-replica service are not equivalent test conditions. Likewise, request-per-second figures are not comparable if one run serves much shorter prompts or completions. Label those results as different scenarios rather than presenting one as proof that a deployment is universally faster.
How do I estimate inference cost per token?
First find the highest sustained throughput that meets the SLO. Then divide the accelerator cost—or amortized capital and energy cost—by sustained tokens per second to estimate unit cost. Google recommends normalizing to TPS per chip before translating the result into cost per thousand or million tokens.
Keep the cost scope explicit. Provider billing, autoscaling, storage, network egress, and idle capacity are separate terms; a GPU-only estimate is not the same as total cloud service cost. State the region, hardware configuration, billing assumptions, and whether the measured throughput is input, output, or total tokens so the estimate can be interpreted.
What mistakes make an inference benchmark misleading?
- Publishing only average latency. It hides slow requests that can violate the user-facing SLO.
- Combining queue and model time without labeling the boundary. This makes it impossible to see whether time is lost waiting or computing.
- Using only requests per second for variable-length text. A request rate does not reveal how many tokens the system processed.
- Comparing different scaling conditions. A warm single replica and a cold autoscaled fleet can have different startup, queue, and routing behavior.
- Averaging replica percentiles. Aggregate histograms rather than averaging already calculated quantiles.
- Leaving out reliability or quality. Throughput without errors, timeouts, cancellations, or a quality target can reward a deployment that is not delivering equivalent successful work.
- Calling a result “cloud performance” without deployment details. Region, software versions, accelerator topology, network path, and workload affect what the result actually establishes.
Frequently Asked Questions
Should I measure inference latency from the client or the server?
For user experience, collect client-side timing because it includes the network path and the complete interaction visible to the caller. Pair it with server-side measurements when you need to separate network and request-handling time from queueing and model computation.
How many requests do I need for a reliable p99?
There is no single request count that makes every p99 estimate reliable; it depends on how stable the service is and how much uncertainty you can tolerate. Report the observation count and repeat runs, and avoid treating a percentile from a small sample as a precise capacity guarantee.
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Does high GPU utilization mean my inference service is performing well?
Not by itself. Utilization is useful context, but it does not show whether requests meet their latency objective, whether output throughput is still increasing, or whether errors and queueing are acceptable.
Should cold starts be included in a cloud inference latency result?
Include them when the question is how a newly started or scaled service behaves. For steady-state serving capacity, separate startup observations from warmed measurements so the reported result answers that narrower question.
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