Published March 20, 2026
| Version v1
Journal article
Open
Optimize to Open: An Exploratory-Experimental Approach to the Computational Optimization of Open Large Language Models for Educational Access
Description
This study evaluated five open-source OLLMs for deployment in low-resource learning environments using unstructured pruning and Retrieval-Augmented Generation across three simulated educational infrastructures. Optimization reduced response time and memory use, improved educational precision, and increased throughput.