Understanding Time to First Token (TTFT) in LLM Applications
This technical article from the Redis Blog explains Time to First Token (TTFT), a critical metric for evaluating the responsiveness of Large Language Model (LLM) applications. TTFT measures the delay between sending a user request and receiving the first output token, directly influencing perceived performance and user experience. The analysis breaks down TTFT components, including network latency, request queue wait times, and the prefill phase where the model processes input prompts to build key-value caches. Unlike other latency metrics, TTFT scales with prompt length, making it sensitive to input size. The piece contextualizes TTFT alongside related metrics such as Time Per Output Token (TPOT), end-to-end latency, and throughput, highlighting the trade-offs engineers face when optimizing for speed versus efficiency. It notes that reasoning models may exhibit higher TTFT due to complex intermediate processing. The article serves as a guide for product teams and infrastructure engineers to balance these metrics based specific workload requirements, such as interactive chat versus batch processing, ensuring optimal application performance without sacrificing system throughput.
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Understanding Time to First Token (TTFT) in LLM Applications
This technical article from the Redis Blog explains Time to First Token (TTFT), a critical metric for evaluating the responsiveness of Large Language Model (LLM) applications. TTFT measures the delay between sending a user request and receiving the first output token, directly influencing perceived performance and user experience. The analysis breaks down TTFT components, including network latency, request queue wait times, and the prefill phase where the model processes input prompts to build key-value caches. Unlike other latency metrics, TTFT scales with prompt length, making it sensitive to input size. The piece contextualizes TTFT alongside related metrics such as Time Per Output Token (TPOT), end-to-end latency, and throughput, highlighting the trade-offs engineers face when optimizing for speed versus efficiency. It notes that reasoning models may exhibit higher TTFT due to complex intermediate processing. The article serves as a guide for product teams and infrastructure engineers to balance these metrics based specific workload requirements, such as interactive chat versus batch processing, ensuring optimal application performance without sacrificing system throughput.
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