Paper Title
COST-EFFICIENT TRAINING OF LARGE-SCALE GENERATIVE AI MODELS ON CLOUD PLATFORMS: STRATEGIES FOR BALANCING PERFORMANCE AND EXPENSE
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: Authors
Keywords
AI model training, generative models, cost efficiency, cloud computing, mixed precision training, federated learning, spot instances, AI hardware, cloud pricing models.
Abstract
Pre-training large-scale generative AI models over cloud platforms is computationally expensive and presents substantial overhead in cost control. With AI models becoming more extensive and more diverse, the costs of training such models have become a significant limitation for many organizations. In this article, the author presents possible directions that can be used to make training of generative AI models cost-efficient with a focus on their excellent results. Further, it goes through the primary cost, computational cost, cloud pricing model, storage cost, and data transfer cost. It also presents valuable solutions like the improvement of model architecture, the implementation of MP training, the utilization of spot instances, and FL. It also discusses actual-life scenarios to explain how organizations have effectively implemented the mentioned strategies. The article discusses future trends in AI hardware, cloud pricing models, and how AI will gradually take responsibility for managing resources. These advancements are additionally expected to extend the effectiveness of training AI models while minimizing expenses–recognizing AI models with abilities suited to the organization's goals will improve.
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How To Cite (APA)
Aditya Mehra (June-2023). COST-EFFICIENT TRAINING OF LARGE-SCALE GENERATIVE AI MODELS ON CLOUD PLATFORMS: STRATEGIES FOR BALANCING PERFORMANCE AND EXPENSE. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(6), g831-g841. https://ijnrd.org/papers/IJNRD2306655.pdf
Issue
Volume 8 Issue 6, June-2023
Pages : g831-g841
Other Publication Details
Paper Reg. ID: IJNRD_300291
Published Paper Id: IJNRD2306655
Downloads: 000122001
Research Area: Science and Technology
Author Type: Indian Author
Country: -, -, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2306655.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2306655
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