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Pruning is a model compression technique that improves LLM inference efficiency by systematically removing less important parameters from the model. This process results in a smaller model size, leading to reduced memory requirements and faster inference.

Pruning for LLM Inference

Based on the scenario below, evaluate the company's decision to use the smaller, parameter-reduced model for their mobile app. Justify your conclusion by weighing the primary advantage against the main disadvantage in the context of this specific application.

Model Deployment Decision for a Mobile Application

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.

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?

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