Optimization of Deep Learning Model Training through Hardware Configuration Analysis and Fine-Tuning Techniques

This paper presents an integrated approach to analyzing and optimizing the training speed of intelligent systems based on deep learning algorithms. The study focuses on two key aspects: evaluating the impact of GPU hardware characteristics—such as memory size, core count, and architecture—on training performance, and applying fine-tuning techniques to reduce training time without significantly sacrificing model accuracy. A simulation-based model was developed to compare different hardware configurations and to estimate training time, cost, and efficiency under various constraints. The results highlight optimal trade-offs between performance and budget, providing practical recommendations for hardware selection and training strategies in deep learning projects. This work also introduces an efficiency metric that integrates performance, cost, and time, and demonstrates its use in real-world scenarios such as AI development, cloud training cost optimization, and educational environments.