Energy Consumption and Computational Demand of Modern AI Systems: A Practical Survey

The rapid growth of Artificial Intelligence (AI) has led to unprecedented demand for computational resources and energy consumption. Large-scale deep learning models, particularly Convolutional Neural Networks (CNNs) and transformer based architectures, require substantial computing power during both training and inference. As AI systems are increasingly deployed at scale—from cloud data centers to edge devices—energy efficiency and sustainability have become critical concerns. This paper presents a practical survey of the computational and energy demands of modern AI systems. We analyze energy consumption across training and inference stages, compare cloud-based and edge-based deployments, and discuss the environmental impact of data centers. Furthermore, we examine emerging directions in energy-efficient AI, including model compression, quantization, knowledge distillation, and hardware-aware optimization. The goal is to provide engineers and researchers with a concise, deployment-oriented reference for understanding AI energy challenges and selecting practical approaches toward sustainable AI systems.