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What AI execution speed measures
Inference is the stage when a trained model processes an input and produces a result. In a chat interface, that process may stream text a token at a time. A useful account of its speed separates four questions:
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- When does the first output appear? Time to first token (TTFT) measures the wait before generation becomes visible.
- How quickly does streamed output continue? Inter-token latency (ITL) measures the gaps between successive output tokens.
- When is the answer complete? Request latency measures the elapsed time until the final response arrives.
- How much work does the system serve? Throughput measures output volume or completed requests over time.
These measures describe different parts of the experience. A model can begin responding quickly but take a long time to finish, or generate a single user’s answer at a moderate pace while serving many users at once.
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| Metric | What it measures | Best question to answer |
|---|---|---|
| TTFT | Time from submitting a request until its first output token arrives. | How soon does the AI start answering? |
| ITL | Time between consecutive output tokens. | How quickly and smoothly does streamed text continue? |
| TPOT | Generation time normalized across output tokens; a commonly cited formula excludes the first token. | What is the average time per generated token? |
| Request latency | Time from sending a request until its final response arrives. | How long until the answer is complete? |
| Output tokens per second | Output tokens divided by elapsed benchmark time. | How much generated text does the server produce per second? |
| Requests per second | Successfully completed requests per second. | How many requests can the system serve? |
| Goodput | Completed requests per second that satisfy specified metric constraints, such as latency objectives. | How much work meets the responsiveness target? |
Definitions and formulas are documented by NVIDIA’s LLM benchmarking metrics guide, NVIDIA GenAI-Perf, and Google Cloud’s overview of model inference. Because tools can define intervals and benchmark boundaries differently, check the exact formula before comparing values. For example, do not assume a TPOT value uses the same treatment of the first token in every tool.
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How the metrics relate to what a user experiences
TTFT: the wait before the answer starts
TTFT begins at request submission and ends when the first output token arrives. Depending on where a benchmark starts and stops the clock, it can reflect queueing, prompt prefill, and network effects as well as model generation. A low TTFT makes an interface feel responsive at the outset, but it does not establish how quickly the complete answer will arrive.
ITL and TPOT: the pace of streamed generation
ITL describes the intervals between tokens; lower gaps mean tokens arrive more frequently. TPOT summarizes generation time per output token across a sequence. These are useful for comparing the pace of a streamed response, but a benchmark’s formula matters: conventions can differ, including whether the first-token interval is included.
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Request latency: time to the finished response
Request latency covers the full interval from sending a request to receiving its final response. It answers a different question from TTFT: a system may start quickly yet take longer overall because it generates more tokens or produces them more slowly.
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Output tokens per second describes generated output volume over time. Total tokens per second may include input and output tokens, so the label alone does not tell you which quantity was counted. Requests per second counts completed requests, but it can hide differences in work: a short prompt and answer are not equivalent to a long context and response.
Goodput: capacity that meets a service target
Goodput counts completed requests per second only when they meet specified metric constraints, such as latency objectives. NVIDIA defines the measure in its GenAI-Perf goodput documentation. It can be more informative than raw throughput when a system must meet a responsiveness target as well as serve work.
Why one speed score cannot rank every AI system
Speed changes with the workload and the conditions under which the system is measured. For example, raising the number of concurrent requests may increase aggregate throughput while worsening latency or token pace for each user. A result that favors total capacity may therefore describe a different priority from one that favors the waiting time of an individual user.
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What to check in an AI speed benchmark
Before treating a reported number as a comparison, look for the details that define what was measured:
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- Model and serving setup: Identify the model and relevant hardware or software configuration.
- Input and output lengths: Check the prompt or context size and the generated output length.
- Load pattern: Find the request rate or concurrency level. A result at one load does not automatically describe performance at another.
- Metric and formula: Confirm whether the result is TTFT, ITL, TPOT, request latency, output-token throughput, total-token throughput, requests per second, or goodput—and which intervals are included.
- Timing procedure: Check the measurement window and how warm-up, empty responses, and benchmark boundaries are handled.
- Latency aggregation: Look for the reported percentile or aggregation method. An average alone may not show how slow requests affect users at the tail.
Benchmark tools may differ in their definitions, timing windows, and handling of warm-up or empty responses. Consequently, figures with the same metric label are not necessarily directly comparable. The NVIDIA guides for LLM metrics and GenAI-Perf describe the measurement context behind their respective metrics.
How to compare two AI systems fairly
- Match the workload. Use the same model, input and output lengths, serving configuration, and load pattern.
- Compare user experience and capacity separately. Use TTFT and request latency, including a relevant tail percentile, for responsiveness; use output-token throughput or goodput at a stated concurrency for capacity.
- Add token-pacing metrics when streaming matters. Compare ITL or TPOT, checking that both tools use compatible formulas.
- Read the benchmark method, not just the headline value. Align timing boundaries, warm-up handling, measurement window, and latency aggregation before ranking results.
A larger tokens-per-second figure by itself does not prove that a system feels faster. It may reflect a different workload, higher concurrency, or a throughput definition that counts a different set of tokens.
Bottom line
AI execution speed is best understood as a set of workload-specific inference measurements, not one universal number. Choose the metric that matches the question—starting time, streaming pace, time to completion, or serving capacity—and compare results only when their workloads, conditions, and formulas align.
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