Stanford Database Library of American Journal of Applied Science and Technology

Vol. 5 No. 08 (2025): Volume 05 Issue 08
Articles

Enhancing AI Model Training Through Cloud-Based GPU Infrastructure: Techniques and Performance Insights

  • Emeka Okoro
Emeka Okoro
Independent Researcher, Lagos, Nigeria

Published 2025-08-31

How to Cite

Emeka Okoro. (2025). Enhancing AI Model Training Through Cloud-Based GPU Infrastructure: Techniques and Performance Insights. Stanford Database Library of American Journal of Applied Science and Technology, 5(08), 82–89. Retrieved from https://oscarpubhouse.com/index.php/sdlajast/article/view/14

Copyright (c) 2025 Emeka Okoro

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

: The exponential growth of Deep Learning (DL) models, characterized by billions of parameters (Devlin et al., 2019; Radford et al., 2019), necessitates high-throughput, energy-efficient computational infrastructure. Cloud-based Graphic Processing Units (GPUs) have emerged as the dominant platform for modern Artificial Intelligence (AI) training, driven by their parallel processing capabilities and the flexibility of Infrastructure-as-a-Service (IaaS) platforms (Statista, 2023). This paper explores advanced techniques critical for optimizing AI model training within these cloud environments, specifically focusing on performance, power efficiency, and the implications of virtualization. We detail a methodology encompassing workload characterization using popular DL frameworks like TensorFlow (Abadi et al., 2016), PyTorch (Paszke et al., 2019), and JAX (Bradbury et al., 2018) across various model architectures (e.g., ResNet (He et al., 2016), BERT (Devlin et al., 2019)). Furthermore, we evaluate the impact of GPU virtualization technologies, such as SR-IOV (Dong et al., 2010) and rCUDA (Duato et al., 2010), which facilitate the partitioning and remote access of accelerators. A novel aspect of this study involves proxy-based modeling for thermal and acoustic evaluation of these cloud environments, providing insights into physical constraints often hidden from the end-user (Karan Lulla et al., 2025). The results demonstrate that while cloud virtualization introduces minor overheads, optimized data transfer protocols and batch-sizing techniques can mitigate these effects, achieving close to bare-metal performance. Crucially, power efficiency metrics (Abe et al., 2014; Ghosh et al., 2013) reveal that larger, state-of-the-art cloud GPU instances (Amazon Web Services, 2023) offer superior performance-per-watt ratios, aligning with sustainability goals (Patterson et al., 2021). The findings provide a robust framework for researchers and practitioners to select and configure cloud GPU infrastructure for maximum training throughput and efficiency, underscoring the ongoing trade-offs between performance isolation (Somani & Chaudhary, 2009) and resource utilization in the context of large-scale machine learning (LeCun et al., 2015).

 

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