What analysis approach would most effectively identify the hardware factors that impact an AI model's inference time?

The most effective analysis approach to identify the hardware factors impacting an AI model's inference time is to perform a multiple regression analysis with inference time as the dependent variable. This method allows for a detailed examination of how various independent hardware factors—such as GPU type, CPU speed, memory size, and other relevant specifications—quantitatively influence inference time.

Multiple regression analysis is particularly advantageous because it can handle multiple predictor variables simultaneously, offering insight into which factors have significant effects and to what extent. This provides a comprehensive understanding of the relationships and can help ascertain the importance of each factor in the context of inference performance. Additionally, regression analysis can quantify these relationships, enabling predictions about inference time based on specific hardware configurations.

In contrast, creating a bar chart comparing average inference times across hardware configurations would provide a visual representation but would not yield a detailed understanding of how each hardware factor contributes individually to inference time. Clustering could reveal groups of configurations with similar inference times, but it would not directly identify or analyze the individual factors influencing those times. Conducting a t-test between two different GPU types limits the analysis to a binary comparison and does not account for the multitude of other hardware factors that could also contribute to variance in inference time. Therefore, the multiple regression analysis is