A Comparative Survey of Generative AI Models and Implementations

Abstract

JOSIAH P. Pryor, Dept of Computer Science, Mathematics and Engineering, Shepherd University, Shepherdstown, WV, 25443, and WEIDONG LIAO (Faculty Advisor), OSMAN GUZIDE (Faculty Advisor), Dept of Computer Science, Mathematics and Engineering, Shepherd University, Shepherdstown, WV, 25443. Navigating the Generative AI Landscape: A Comparative Survey of Generative AI Models and Implementations

As the field of Generative AI rapidly evolves, individuals and organizations face challenges in selecting the most suitable models and implementation strategies for their needs. This poster presents a survey of Generative AI models, including Large Language Models (LLMs), Vision Models, and Large Multimodal Models (LMMs). It compares open-source and closed-source models, as well as locally hosted versus cloud-based implementations. The objective is to provide a clear framework for understanding which AI tools and deployment methods align best with specific business domains and use cases.