Energy-Efficiency and carbon footprint in vector RAG and knowledge graph-based retrieval model : a case study of the AIT website's carbon footprint and performance optimization | |
| Author | Bestha, Sai Haneesha |
| Call Number | AIT RSPR no.DSAI-25-04 |
| Subject(s) | Energy conservation--Case studies Generative artificial intelligence Information retrieval--Automation |
| Note | A research study submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Data Science and Artificial Intelligence |
| Publisher | Asian Institute of Technology |
| Abstract | The rapid advancement of Retrieval-Augmented Generation (RAG) models has transformed information retrieval and generation by integrating external knowledge with powerful generative capabilities. Despite these benefits, the environmental impact of deploying such systems particularly in terms of energy consumption and carbon emis sions remains an important concern. This study investigates the development and assessment of energy-efficient RAG archi tectures, focusing on two major approaches: Vector RAG and Knowledge Graph-based Retrieval System. Using academic content extracted from the Asian Institute of Technology website, both models were implemented to support question answering and contex tual information retrieval. A central component of the research involved measuring and analyzing the carbon footprint of each RAG architecture through detailed monitoring of CPUand GPUenergy usage.The comparative evaluation reveals differences in retrieval performance, accuracy, and energy demands, and identifies the most energy-intensive stages within each pipeline. Additionally, the study introduces optimization strategies aimed at improving computational efficiency while maintaining retrieval quality and response consistency. The findings contribute meaningful insights toward the design of sustainable AI systems that reduce environmental impact without sacrificing performance, supporting broader goals for environmentally responsible AI deployment. |
| Year | 2025 |
| Type | Research Study Project Report (RSPR) |
| School | School of Engineering and Technology |
| Department | Department of Information and Communications Technologies (DICT) |
| Academic Program/FoS | Data Science and Artificial Intelligence (DSAI) |
| Chairperson(s) | Chutiporn Anutariya; |
| Examination Committee(s) | Virdis, Salvatore G.P.;Pattama Krataithong |
| Scholarship Donor(s) | AIT Scholarship; |
| Degree | Research Studies Project Report (M. Eng.) - Asian Institute of Technology, 2025 |