Abstract
Artificial Intelligence has rapidly evolved into a transformative technological force, yet its exponential computational demand has raised serious concerns about energy consumption and carbon emissions. The process of training and deploying largescale AI models requires massive data centers, extensive GPU resources, and continuous cooling infrastructure, leading to an unsustainable environmental footprint. In recent years, the research focus has shifted toward AI model optimization as a critical strategy for achieving both energy efficiency and carbon reduction without compromising performance accuracy. Model optimization integrates algorithmic improvements, hardware acceleration, and data management strategies to reduce energy use across the AI lifecycle—from data preprocessing to model inference. Techniques such as model pruning, quantization, knowledge distillation, and neural architecture search have emerged as leading frameworks for minimizing computational complexity. Additionally, the rise of green data centers powered by renewable energy sources complements algorithmic efficiency, reinforcing the global movement toward sustainable artificial intelligence. This research paper examines the interplay between AI optimization techniques and sustainable computing practices, highlighting their potential to reshape the carbon trajectory of digital transformation. Through a synthesis of theoretical analysis and empirical findings, it explores how AI can evolve from an energy-intensive discipline into a model of ecological responsibility.
Keywords
AI optimization
energy efficiency
carbon reduction
sustainable computing
model pruning
quantization
neural architecture search
green AI
data center efficiency
low-power AI
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