You are part of a team investigating the performance variability of an AI model across different hardware configurations. The model is deployed on various servers with differing GPU types, memory sizes, and CPU clock speeds. Your task is to identify which hardware factors most significantly impact the model's inference time. Which analysis approach would be most effective in identifying the hardware factors that significantly impact the model's inference time?
Select an answer to reveal the explanation.
Short Explanation and Infographic
Imagine your boss walks in and says, "Our model's response time is all over the map on these different servers. Is it the GPU model, the RAM, or the CPU clock speed that's slowing us down?" If you just run a basic t-test or draw a bar chart, you're only looking at one thing at a time and missing how they interact. You need to analyze all these hardware factors together to see how they impact inference time. That is exactly what multiple regression analysis is for. The inference time is your dependent variable, and the hardware specs are your independent variables. This gives you mathematical coefficients showing exactly which factor is the real bottleneck. That's how you get real, actionable data!
Full explanation below image
Full Explanation
When analyzing how multiple independent hardware parameters (such as GPU architecture, VRAM size, and host CPU frequency) simultaneously influence a single performance metric like model inference latency (the dependent variable), multiple linear regression is the most appropriate statistical method. Multiple regression allows researchers to model the relationship between several independent variables and a single continuous dependent variable. By fitting a regression model, you can determine the overall statistical significance of the predictors, evaluate the strength of the relationship (using the R-squared value), and look at individual coefficients to isolate the effect of each hardware parameter while holding the others constant.
Let's analyze the incorrect options: Option A (clustering) is an unsupervised learning technique used to group data points based on feature similarity. While it might group similar configurations together, it does not output statistical significance or isolate which specific hardware factors drive the performance variance. Option C (t-test) is limited to comparing the means of exactly two groups (e.g., comparing the speed of GPU A vs. GPU B). It cannot handle multiple independent variables simultaneously, nor can it incorporate continuous variables like memory size and CPU clock speed into a single analysis. Option D (bar chart) is a descriptive visualization tool. While helpful for initial data exploration, it cannot provide statistical proof, quantify individual variable coefficients, or account for confounding variables in complex hardware setups.