International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Investigating the Optimal Cloud Computing Infrastructure for Training Large-Scale Generative Models

Author(s) Abdul Sajid Mohammed, Shalmali Patil
Country USA
Abstract The training of large-scale generative models, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), presents unique challenges due to their computational intensity and memory requirements. These models often require significant hardware resources, distributed frameworks, and scalable environments to manage vast datasets and extensive neural architectures. Cloud computing has emerged as a vital infrastructure for addressing these demands, offering scalable and flexible platforms that support high-performance computing, on-demand resource allocation, and specialized services. This survey explores the interplay between cloud computing and generative model training, highlighting key requirements, state-of-the-art solutions, optimization strategies, and cost-energy efficiency considerations. Furthermore, it identifies the prevailing challenges in cloud-based training environments and outlines potential future directions. The findings provide a comprehensive foundation for researchers and practitioners aiming to enhance the efficiency and scalability of generative model training through optimal cloud infrastructure.
Keywords Generative AI, Cloud Computing, Scalable, Distributed Computing, GPU Acceleration, TPU Pods, Federated Learning, Energy-Efficient Computing, Cost Optimization, AI Infrastructure, Data Security, Sustainability AI, Machine Learning
Field Computer > Data / Information
Published In Volume 4, Issue 6, November-December 2022
Published On 2022-11-29
DOI https://doi.org/10.36948/ijfmr.2022.v04i06.30908
Short DOI https://doi.org/g8tzzg
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E-ISSN 2582-2160
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