What is green AI?

Green AI is a software development approach focused on making AI systems environmentally sustainable and creating climate-friendly AI. It includes practices that reduce carbon footprint, increase energy efficiency, contribute to emissions reduction, and minimize other negative environmental impacts of AI technology. Compared to traditional AI, which is resource-intensive and relies on high-carbon approaches, sustainable AI balances performance with sustainability and considers the long-term consequences of AI development and adoption.

The Problem with Traditional AI Compared to Green AI

Training traditional AI models requires complex infrastructure and substantial computational resources, resulting in significant energy consumption and related carbon emissions. When processing extensive data volumes, data centers also use large volumes of water for cooling, which adds up to excessive resource use. To minimize the environmental impact of AI, green AI approaches rely on model optimization techniques, hardware efficiency, cloud optimization, and efficient programming. Here are the key differences between the two concepts.

Criteria	Traditional AI	Green AI
Values	High model accuracy and capabilities (often through scaling)	Balance between AI model performance and sustainable computing
Model design	Large architecture with resource-intensive training	Optimized models with pruning, compression, and knowledge distillation
Infrastructure	Energy-intensive datacenters	Sustainable data centers with improved cooling and renewable energy use
Environmental impact	High energy and water consumption with associated carbon emissions	Energy-efficient models and reuse of pre-trained models for lower carbon emissions
Cost	Lower setup costs with more expensive long-term maintenance	Higher upfront investments with lower operational costs
AI governance	Focus on performance and profitability; Reactive to emerging regulations	Sustainable software development and operation; Proactive compliance with environmental standards

Principles of Green Artificial Intelligence 

Implementing climate-friendly AI comes with a set of strategies that reduce the environmental impact of AI software solutions. Key principles of Green AI include:

• Efficiency & resource optimization. Efficient AI/ML algorithms, sustainable hardware, and model optimization through pruning, quantization, and knowledge distillation to reduce resource use.
• Sustainable infrastructure. The use of renewable energy, including solar, wind, and hydropower, for data centers, as well as cooperation with low-energy cloud providers.
• Transparency and accountability. Tracking energy consumption and analyzing model performance to find efficient ways to build an AI-powered system.
• Green software engineering. Implementing energy-efficient AI training approaches and sustainable coding practices across the software development cycle.

Why Green AI Matters Now

By 2030, energy demands from AI datacentres alone will exceed manufacturing steel, chemicals, and other energy-intensive products combined. Adopting green artificial intelligence is a long-term investment that makes an organization competitive and helps handle emerging challenges, including: 

• Limited resources → Energy-efficient AI approaches become a solution to considerably increasing energy consumption resulting from AI systems penetrating every field.
• Regulatory and ESG pressures on enterprises → Sustainability regulations are likely to become significantly stricter in the coming decades, and green AI is a way to prepare for this shift early.
• Remote deployment → Green AI systems use lightweight and energy-efficient models that can run even in power-constrained environments. Optimized models consume fewer resources and are easier to deploy in edge and remote systems.
• Rising costs → Green AI reduces computational cost in the long run, allowing companies to optimize resource use.

Putting Green AI into Practice

Green AI relies on the best practices that ensure AI software operates with minimized resource consumption and sufficient performance. Below are the main approaches software development and MLOps teams use.

• Smaller, task-specific models. AI engineers employ techniques like model pruning, quantization, and knowledge distillation approaches to simplify models. It involves eliminating redundant parameters, reducing the precision of calculations, and training smaller models to imitate the behavior of larger ones.
• Optimizing training pipelines for efficiency. Engineering teams optimize data loading, use parallel and distributed processing, and may reuse parts of previous training sessions to reduce compute times and resource consumption. 
• Green cloud computing. Organizations choose cloud providers with a zero-carbon portfolio and/or data centers powered by renewable energy that also optimize workloads for resource efficiency. 
• Energy-efficient hardware. Engineers deploy TPUs, ASICs, and energy-optimized GPUs that offer better performance per watt.
• Lifecycle optimization. Engineering teams ensure that green AI is applied throughout the software development lifecycle, including model compression for edge devices, minimizing updates, decommissioning models to avoid idle energy usage, and other approaches.

Green AI Use Cases

Although green artificial intelligence is gaining the most popularity in industries where environmental impact matters, such as GreenTech software development, it's a universal solution for any company that values sustainability. Here are some of the typical green AI use cases:

• Greentech equipment manufacturers build AI-powered production data systems to implement automated traceability in documentation.
• Chatbot providers deploy optimized NLP models for text processing to reduce energy costs without compromising response accuracy.
• Agriculture companies build TinyML models that run on microcontrollers to enable crop health analysis in remote locations.

Summing Up

The green AI definition demonstrates that it's a powerful alternative to traditional AI that ensures decent performance while optimizing resource utilization. It modifies AI model training, deployment, and maintenance to reduce carbon footprint and energy consumption, cutting the waste. More efficient algorithms, hardware efficiency, and low-energy cloud use enable green AI adopters to build more sustainable software systems and comply with emerging regulatory challenges.