AI-Optimized Storage Solutions for Generative...

AI-Optimized Storage Solutions for Generative AI Models in Cloud: Addressing Storage and Retrieval Challenges for Large-Scale Models

Author Name : Thejaswi Adimulam

ABSTRACT Due to the recent advancement in generative AI, especially large language and diffusion-based image-generating models, there has been immense pressure on large and efficient storage systems. These models, which consist of tens or hundreds of billions of parameters, present significant problems related to data handling, especially from a storage perspective, especially in cloud scenarios where data storage and the ability to scale are paramount. This research article aims to identify new storage architectures and approaches that improve generative AI models' storage and retrieval methods. Emphasis is placed on achieving the maximum efficiency of these systems in terms of certain requirements and constraints within cloud environments. The approaches should enhance scalability, increase speed, and decrease expenses, enabling storage, management, and access to these complex models. These solutions in this study help tackle storage obstacles for generative AI, making such technologies easier to incorporate into a range of applications and services and contributing to the extensive adoption and usage of such impactful models.