What Is Time to First Token (TTFT), and Why Does It Matter for LLM Apps?

Time to first token (TTFT) is the time from starting an LLM request until the first output token—or, in a streaming app, the first content-bearing response chunk—reaches the user. It measures how long the application takes to begin answering, not how quickly it finishes. TTFT is most useful alongside token-to-token timing and total response time.

What TTFT measures

For a streaming request, TTFT is commonly measured from the client’s request start to the first non-empty content chunk it receives. The August 2026 IETF Internet-Draft “Benchmarking Terminology for Large Language Model Serving” defines it as “the elapsed time between request initiation and receipt of the first output token.” The document is an Internet-Draft, not a final RFC.

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Measurement conventions vary: a tool may count any token, the first non-empty content, or the first non-reasoning output. A reported TTFT is meaningful only when its convention and timing boundary are clear. For a non-streaming request, users receive the response all at once, so the first-token milestone is not separately visible; the draft notes that TTFT and end-to-end latency coincide in that case.

Why TTFT matters in an LLM application

In a streaming chat interface, users can see the answer begin before generation is complete. A shorter TTFT therefore reduces the initial visible wait and can make the application feel more responsive. It does not tell you whether the answer will continue arriving quickly.

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TTFT is one part of the experience, not a stand-alone measure of speed. A system may start quickly but deliver later tokens slowly, or begin slowly and then stream at a brisk pace. Measure the start, the stream, and completion separately:

• TTFT: delay until the first output token or content-bearing chunk arrives.
• Inter-token latency (ITL) or time between tokens (TBT): spacing between successive tokens as observed by the client. The precise definition depends on the measurement tool.
• End-to-end latency: time until the complete response arrives. Some tools report time to last byte (TTLT) for this endpoint.

What can make the first token slow

TTFT can include several stages between the user and the model. Which stages are included depends on where the clock starts and stops, and on the application stack:

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• Request and network transit: the request must reach the service, and the response must travel back to the client.
• Authentication and admission: the service may need to validate the request and decide whether to accept it.
• Queueing: a request may wait for available serving capacity. Under heavy load, this delay can outweigh model processing.
• Prompt prefill: the model processes the input prompt and prepares the initial key-value cache before generating output. Longer prompts generally require more prefill work. The August 2026 IETF draft says uncached prefill latency scales approximately linearly with input-token count. Prefix caching may reduce the work for requests that share a prefix, but whether it is suitable depends on the application and workload.
• First-token generation and delivery: after prefill, the model generates its first output token; the service then serializes and returns the response chunk through the API and client layers. Client-side buffering can delay when content becomes visible.

A client-side timer captures network and delivery effects the server may not include. A server-side timing result can therefore look faster than the wait experienced by the user; the two are not directly comparable unless their timing boundaries match.

How to measure TTFT consistently

For user-visible streaming responsiveness, start the clock when the client initiates the request and stop it when the first non-empty content chunk reaches the client. Record the convention explicitly. NVIDIA AIPerf documents TTFT using this request-start-to-first-non-empty-chunk boundary, including network latency, queuing, prompt processing, and first-token generation: Metrics Reference | NVIDIA AIPerf Documentation.

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Keep these details with each result so comparisons are interpretable:

• Whether the request streams or returns as one complete response.
• What counts as the first token: any token, first non-empty content, or first non-reasoning output.
• Whether timing is measured at the client or server.
• Prompt size in tokens, concurrency or load, and model and deployment identity.
• First-response latency, token-to-token cadence, and complete-response latency as separate values.
• Whether reported figures are means or percentiles, with the same statistic used across comparisons.

Vendor dashboards use vendor-specific metric names. Microsoft Foundry lists AzureOpenAITimeToResponse for streaming first-response latency, AzureOpenAINormalizedTBTInMS for average token spacing, and AzureOpenAITTLTInMS for time to last byte. It also recommends considering prompt and generated-token counts when interpreting latency: Azure OpenAI in Microsoft Foundry Models performance & latency.

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How to troubleshoot a high TTFT

1. Check the measurement first. Confirm the request is streamed, that the timer stops at the intended first-content event, and that the client and server timing boundaries have not been mixed.
2. Compare results by prompt size. Look at prompt-token counts alongside first-response latency. If latency rises with prompt length, prefill is one plausible contributor; the timing alone does not prove it is the cause.
3. Inspect load and queueing. Compare measurements across concurrency or load conditions and review available queue or capacity signals. A high-load delay may occur before the model starts processing.
4. Check the delivery path. If service-side timing is acceptable but client-observed TTFT is high, investigate network transit, API handling, serialization, and client buffering.
5. Check the rest of the response separately. If TTFT is acceptable but the answer takes a long time to finish, examine token cadence and total response time rather than treating it as a first-token problem.

Microsoft advises interpreting latency with token counts; total response time by itself can rise simply because an answer is longer. Likewise, a TTFT figure cannot diagnose slow delivery of later tokens.

How to compare models or deployments

Use the same streaming mode, first-token convention, measurement location, prompt workload, and load conditions. Compare client-observed first-content latency with token cadence and complete-response time, and record prompt and output token counts. State whether the results are means or percentiles. Without aligned timing boundaries and workloads, a lower reported TTFT may reflect a different measurement setup rather than a faster user experience.

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