TPOT
An AI inference performance metric referring to the average time required to generate each subsequent token during the streaming output phase. It determines the text scrolling speed visible to the user and, together with TTFT, constitutes a core component of total response latency.
Core Facts
Time to First Token (TTFT) refers to the time interval between a user sending a request and the model outputting the first token, and is primarily constrained by the duration of the prefill stage.
Large model inference is divided into two phases, Prefill and Decode; Prefill is a major component of Time to First Token (TTFT), while Decode corresponds to Time Per Output Token (TPOT).
TTFT（首个token生成时间）是衡量LLM用户体验的核心指标，决定用户从提交请求到看到第一个字符的等待时长
Timeline (last 90 days)
China Merchants Bank's tidal scheduling achieves cost benefits under the premise that inference services maintain performance metrics of TTFT below 5 seconds and TPOT below 50 milliseconds
China Merchants Bank's inference instances are configured with scaling metrics based on TPOT (time per output token), and when KEDA detects that the metric exceeds the threshold, it reclaims card resources occupied by training jobs and yields them to inference
LLM inference latency is typically broken down into Time To First Token (TTFT) and Time Per Output Token (TPOT).
TPOT (Time Per Output Token) refers to the average time taken to generate each subsequent token, and total response time is roughly equal to TTFT + TPOT × number of output tokens.
Large model inference is divided into two phases, Prefill and Decode; Prefill is a major component of Time to First Token (TTFT), while Decode corresponds to Time Per Output Token (TPOT).
Time per output token (TPOT) is primarily constrained by memory bandwidth and decoding efficiency.
Time per output token (TPOT) measures the speed at which a model generates tokens one by one during the autoregressive decoding phase.
Time to First Token (TTFT) refers to the time interval between a user sending a request and the model outputting the first token, and is primarily constrained by the duration of the prefill stage.
Time Per Output Token (TPOT) measures the speed at which a model generates tokens one by one during the autoregressive decoding phase.
Reducing TTFT relies more on parallelization of the prefill phase, while reducing TPOT more involves memory bandwidth optimization and batching strategies during the decoding phase.
All Facts (11)
Time to First Token (TTFT) refers to the time interval between a user sending a request and the model outputting the first token, and is primarily constrained by the duration of the prefill stage.
90%VerifiedLarge model inference is divided into two phases, Prefill and Decode; Prefill is a major component of Time to First Token (TTFT), while Decode corresponds to Time Per Output Token (TPOT).
65%VerifiedTTFT（首个token生成时间）是衡量LLM用户体验的核心指标，决定用户从提交请求到看到第一个字符的等待时长
65%UnverifiedChina Merchants Bank's tidal scheduling achieves cost benefits under the premise that inference services maintain performance metrics of TTFT below 5 seconds and TPOT below 50 milliseconds
50%UnverifiedChina Merchants Bank's inference instances are configured with scaling metrics based on TPOT (time per output token), and when KEDA detects that the metric exceeds the threshold, it reclaims card resources occupied by training jobs and yields them to inference
50%UnverifiedLLM inference latency is typically broken down into Time To First Token (TTFT) and Time Per Output Token (TPOT).
50%UnverifiedTPOT (Time Per Output Token) refers to the average time taken to generate each subsequent token, and total response time is roughly equal to TTFT + TPOT × number of output tokens.
50%UnverifiedTime per output token (TPOT) is primarily constrained by memory bandwidth and decoding efficiency.
50%UnverifiedTime per output token (TPOT) measures the speed at which a model generates tokens one by one during the autoregressive decoding phase.
50%UnverifiedReducing TTFT relies more on parallelization of the prefill phase, while reducing TPOT more involves memory bandwidth optimization and batching strategies during the decoding phase.
50%UnverifiedTime Per Output Token (TPOT) measures the speed at which a model generates tokens one by one during the autoregressive decoding phase.
50%