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For generative AI, execution speed is not one universal number. It describes how quickly an AI system responds to a particular workload: how soon it starts producing output, how quickly that output continues, how long the complete request takes, and how much work the system serves over time. This guide focuses on model inference—the process of generating or returning an answer—not AI training or every other kind of AI workload.
What AI execution speed measures
When someone asks how fast an AI answers, they may mean several different things. A streamed answer can appear quickly but take a long time to finish; a system can also serve many requests in parallel while making each individual user wait longer. The right metric depends on whether you care about the start of an answer, its completion, its streaming pace, or the service’s capacity.
These distinctions are especially important for large language models (LLMs), where responses are generated as tokens. A token is a unit of text processing; it may represent a word, part of a word, punctuation, or another text element. Tokens per second therefore describe token generation, not a fixed number of words per second. See NVIDIA’s LLM benchmarking metric definitions and Google Cloud’s overview of model inference.
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| Metric | What it measures | Best for answering |
|---|---|---|
| Time to first token (TTFT) | Elapsed time from submitting a request until the first output token arrives | How soon does the AI start answering? |
| Inter-token latency (ITL) | The time between successive output tokens | How quickly and smoothly does streamed text continue? |
| Time per output token (TPOT) | Generation time normalized across output tokens; some definitions exclude the first token | How much time does each generated token take on average? |
| Request latency | Elapsed time from sending a request until the final response arrives | How long until the answer is complete? |
| Output tokens per second | Output tokens generated divided by elapsed benchmark time | How much generated text does the system produce over time? |
| Requests per second | Successfully completed requests divided by time | How many requests does the service complete over time? |
| Goodput | Completed requests per second that satisfy specified metric constraints, such as latency objectives | How much work does the service complete while meeting its responsiveness target? |
Metric names do not guarantee identical formulas across benchmarking tools. In particular, check whether a TPOT or ITL calculation includes the first token, and what interval the benchmark measures. NVIDIA’s GenAI-Perf documentation describes its inference measurements; the exact reported metric should be read alongside the tool’s methodology.
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How to interpret the metrics
TTFT: when the answer becomes visible
TTFT is the wait before the first output token appears. Depending on where the benchmark starts and ends its timer, this can reflect queuing, processing the prompt before generation (prompt prefill), and network effects. It is useful for judging how responsive an interface feels at the start, but it does not tell you how long the full answer will take.
ITL and TPOT: the pace after generation starts
ITL describes the gaps between consecutive tokens, while TPOT summarizes generation time per output token across a response. These measures help characterize a streamed answer after it begins. Since tools can calculate them differently, use the specific benchmark’s formula before comparing figures.
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Request latency: time to the finished response
Request latency covers the time from sending a request until its final response arrives. It is a direct measure of completion time, but comparisons are meaningful only when the requests contain comparable prompts and produce comparable amounts of output.
Throughput: work served over time
Output tokens per second measures generated output volume over time. Total tokens per second may count both input and output tokens, so check which token count a benchmark reports. Requests per second counts completed requests, but can hide differences in work: one request may contain a short prompt and answer, while another carries much longer context.
Goodput: capacity under a service target
Goodput counts completed requests per second only when they meet stated metric constraints, such as latency objectives. NVIDIA’s GenAI-Perf goodput documentation defines it in relation to requests that meet specified constraints, also called service-level objectives. This makes goodput useful when raw volume matters only if users receive responses within an intended service target.
Why a higher speed number may not mean a faster experience
Latency and capacity describe different outcomes. Raising concurrency—the number of requests handled at the same time—can increase a system’s aggregate throughput while worsening per-request latency or the token pace experienced by an individual user. A system that serves more total tokens may therefore feel slower to each person using it.
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For a fair comparison, evaluate at least one user-facing latency measure, such as TTFT or request latency, and one capacity measure, such as output-token throughput or goodput at a stated concurrency. Add ITL or TPOT if the pace of streamed output matters. A higher tokens-per-second figure alone does not establish that a system gives a better user experience.
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A speed result applies to the benchmark conditions that produced it. Before using a figure to choose or rank systems, check:
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- Model: Confirm that the same model is being measured.
- Input and output lengths: Match prompt and response sizes; requests per second alone does not show whether the work per request is comparable.
- Load: Note the request rate or concurrency. Results under light traffic may not describe performance under heavier use.
- Measurement boundaries and formula: Check what starts and stops the clock, which tokens or intervals a metric includes, and how the benchmark handles warm-up and empty responses.
- Measurement window: Identify the time period over which the benchmark collected results.
- Latency aggregation: Check whether the report gives an average or a tail percentile. A tail percentile helps show delays experienced by slower requests rather than only the typical result.
- Serving configuration: Record the relevant hardware and software setup; accelerator capability alone is not the same as measured end-to-end inference performance.
Different tools may define metrics and benchmark boundaries differently, so similarly named results are not automatically comparable. Google Cloud’s accelerator performance benchmarking guidance also underscores the need to hold the model and workload constant when comparing accelerator performance.
Is there one standard AI execution-speed number?
No single number in these metrics represents AI execution speed across systems and workloads. TTFT, token pace, full-request latency, throughput, and goodput answer different questions. A benchmark result is useful when its model, workload, load pattern, metric definition, and measurement conditions are clear; without that context, a speed figure can mislead rather than help.
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