Need for speed: making quick LLM calls

The author of the text discusses the performance of large language models (LLMs) in generating responses. They analyze their dataset of over two million LLM calls and find that the average query takes around 12.7 seconds, with a significant portion of queries taking more than 43 seconds. The authors aim to provide actionable advice on how to speed up LLM processes, focusing on optimizing prompt length and token output. They build a simple linear model to predict response time based on input parameters such as prompt tokens and completion tokens. The analysis reveals that reducing output tokens can significantly improve response speed, with each additional output token costing around 54 milliseconds. The authors also explore the impact of model choice, finding that GPT-3.5-turbo is a faster model than its predecessors, especially for single-token responses. They observe a spike in query volume during working hours and evenings, which can be mitigated by optimizing prompt length and using more efficient models. The study concludes with recommendations for businesses looking to leverage LLMs, including the importance of securing access to these powerful tools.

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