Model Deployment Decision for a Mobile Application

A development team is optimizing a large language model for deployment on devices with limited memory. They apply a technique that identifies and permanently removes 40% of the model's parameters deemed least important to its performance. Which of the following outcomes represents the most probable trade-off the team will encounter?

When applying pruning to a large language model, the less important parameters are temporarily ignored during the inference process to speed up computation, but they remain part of the model's file.