Time to First Token & Tokens per Second — Learning Bundle
This bundle compares two AI speed measures. TTFT is time to the first token. TPS is tokens made each second afterward. It is not one standard metric.
Another example
A voice assistant lowers TTFT for natural turns. A document tool needs steady TPS and tracks both by model and region.
Main parts
- 01User intent and context
- 02Model or tool decision
- 03Grounded result and fallback
Use it when
Use both when streaming speed affects users or system capacity. Report end-to-end percentiles and name the measured task.
Do not use it when
Do not compare unequal prompts or output lengths. Averages hide tails, and high TPS does not prove quality.
Name used in code
request start → first token; generated tokens / decode seconds Before you ship
Check that every member concept has its own name and link. Show all members in the same situation. Give a visible rule for when each one applies and which groups are valid. Never present the bundle as one canonical pattern or as parts that can be swapped freely.
Outcome: Use TTFT & TPS — Learning Bundle to compare its member concepts. Keep their boundaries clear, then decide which members apply and which can work together. User context: A voice assistant lowers TTFT for natural turns. A document tool needs steady TPS and tracks both by model and region. Learning bundle to unpack: TTFT & TPS — Learning Bundle. Canonical members: Time to First Token, Output Tokens per Second Why this comparison helps: Use both when streaming speed affects users or system capacity. Report end-to-end percentiles and name the measured task. Do not treat the bundle as one pattern when: Do not compare unequal prompts or output lengths. Averages hide tails, and high TPS does not prove quality. Comparison requirements: Name every member concept on its own. Explain its job and limits. Show every member in the same real situation. Give a visible sign for when to use, combine, or leave out each member. Decision quality: Keep platform and evidence limits clear. Show which members can work together. Point out false matches, unsafe swaps, and cases that need another concept. Acceptance criteria: A reader can explain each member in one sentence and tell them apart in the example. They can defend the chosen member or valid group and open every canonical guide. Evidence and limits (evidence boundary): This is not a normative standard. Its name, limits, and expected behavior can change from one source to another. Unknowns to confirm: Target platform, decision setting, limits, and evidence needed to use, combine, or leave out members.
Check this request
Review the current use of TTFT & TPS — Learning Bundle. Definition: This bundle compares two AI speed measures. TTFT is time to the first token. TPS is tokens made each second afterward. It is not one standard metric. Release checks: Check that every member concept has its own name and link. Show all members in the same situation. Give a visible rule for when each one applies and which groups are valid. Never present the bundle as one canonical pattern or as parts that can be swapped freely. Before changing code, report the evidence you found, gaps, severity, and the smallest safe fix.