Year: 2025
Type: article
Abstract: Machine learning model optimization has become increasingly critical as model architectures grow exponentially in size and complexity.This article presents a comprehensive analysis of optimization techniques across multiple dimensions, including GPU utilization, hyperparameter tuning, and data preprocessing strategies.Through extensive empirical analysis, The article demonstrates that advanced optimization approaches can reduce training costs by up to 47% while improving model performance by 12%.The article findings show that combined memory optimization techniques achieve 82.4% memory reduction with only 0.42% accuracy loss, while automated hyperparameter optimization reduces training time by 51.8% compared to traditional methods.Furthermore, the article presents a case study of a 1.5B parameter language model optimization that achieved a 7.8x speedup through distributed training while reducing per-node memory usage from 134GB to 38GB.The article also explores future directions in quantum-enhanced optimization and energy-efficient training methods, projecting potential improvements in model efficiency up to 2028.
Author: Madhu Babu Kola (PMP)
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4407085030