A Comparative Survey of Generative AI Models and Implementations
Year: 2025
Type: article
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.
Author: Weidong Liao
Institution: Shepherd University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4409263779