Reducing energy consumption in AI computing: Key strategies for sustainability

May 18, 2025

As artificial intelligence becomes a driving force across numerous sectors—from healthcare and finance to entertainment and logistics—reducing energy consumption in AI computing sustainability emerges as a pressing concern for industry leaders and global organizations alike. The rapid development and deployment of sophisticated AI models such as ChatGPT, Grok, and DeepX have placed unprecedented pressure on both energy grids and natural resources. Striking a balance between innovation and environmental stewardship is vital to ensure technology advances while supporting a sustainable future. This article delves into how on-device AI, energy demand management, and international advocacy—supported by insights from the World Economic Forum—are converging to shape AI for a greener tomorrow.

Key takeaways: Advancing toward sustainable AI computing

Efforts to reduce energy consumption are at the heart of sustainability in AI computing, with on-device AI providing a compelling approach.
Rising energy demand driven by powerful AI systems remains a formidable challenge for environmental goals.
The World Economic Forum is spearheading global dialogue and practical strategies for energy-efficient AI innovation.
Effective solutions must harmonize computing needs, privacy, energy use, and responsible data management, including technologies like cookies.
Concrete changes in model design, deployment, and resource management are key to achieving long-term sustainability in the AI sector.

What drives energy demand in AI computing?

The rapid progress in artificial intelligence owes much to advanced computational processes. AI computing encompasses a variety of tasks stretching from large-scale model training to real-time data inference. Each stage of this process demands significant computational power and energy, especially when training expansive neural networks such as those powering ChatGPT, Grok, and DeepX. These activities occur predominantly in energy-intensive data centers, amplifying both direct and indirect energy usage.

Model training: The initial stage of developing an AI model involves processing massive datasets over extended periods. This stage can require millions of kilowatt-hours (kWh) in electricity and results in substantial carbon emissions.
Model inference: After training, deploying models to analyze, predict, or interact requires ongoing computational resources, generating a steady baseline for energy consumption.
Data transfer and storage: Supporting AI computations necessitates moving and storing enormous quantities of data, with additional energy used for maintaining network infrastructure.

The interplay of these components results in significant operational costs and environmental impacts, underscoring the urgency for innovative solutions in reducing energy demand within the AI domain.

AI Computing Component	Typical Energy Use	Environmental Impact
Large model training (e.g., LLMs)	Extremely high (up to millions of kWh per model)	Considerable greenhouse gas emissions, resource intensive
Cloud-based inference	Consistent, high especially at scale	Persistent carbon footprint, infrastructure-dependent
On-device inference	Low to moderate per device	Distributed, lower total energy and emissions

How does energy demand impact sustainability in AI computing?

The consistently high energy demand from AI systems challenges the overarching goal of sustainability. Not only does this exacerbate the consumption of electricity—often sourced from fossil fuels—it also heightens the environmental burden through resource depletion and emissions. As energy usage grows, the negative effects ripple across operational costs, infrastructure requirements, and society's ability to meet wider climate targets. Lowering the energy demands of AI is crucial in aligning technological progress with environmental responsibility.

Consumption of non-renewable resources increases as more data centers and edge devices operate around the clock.
Major sources of greenhouse gases come from the electrical load required for training and running AI.
Rising costs for electricity and cooling decrease the affordability and accessibility of AI-powered solutions, potentially hampering wider adoption.

The more energy AI computing consumes, the further away we move from the vision of a technology-driven yet sustainable future—making solutions for efficiency essential.

What is on-device AI and how does it contribute to energy-efficient AI computing?

On-device AI represents a transformative approach, where algorithms execute directly on a user’s device (such as smartphones, tablets, or laptops), as opposed to centralized server farms. By shifting processing loads closer to the data’s origin, this method cuts down on the need for continuous data transmission and reduces reliance on energy-intensive remote data centers. Additionally, on-device AI not only serves sustainability objectives but also strengthens user privacy and application speed. Here’s how it supports a sustainable AI ecosystem:

Data is processed locally. There’s less need to send information across networks or maintain a persistent cloud session, leading to considerable reductions in energy usage.
Devices operate independently. This autonomy helps distribute work more evenly and improves resilience, so that many small sources replace a few large, power-hungry centers.
Energy efficiency is built-in. AI models optimized for devices frequently require less computational power by design, translating directly into savings on energy and operational costs.

On-device AI is therefore positioned as a flagship technology in advancing AI computing sustainability, promoting both environmental gains and end-user trust.

What are the roles and challenges of large AI models like ChatGPT, Grok, and DeepX?

Large language models and generative AI systems, typified by ChatGPT, Grok, and DeepX, epitomize the challenges and opportunities at the intersection of advanced AI computing and energy sustainability. These models are celebrated for their versatility—offering services from automated translation to text generation and conversational interfaces—but their vast computational requirements strain both the technology landscape and the environment.

Challenges arise during every lifecycle stage of these models:

Training involves substantial compute power. Building accurate models often means weeks of processing on clusters of specialized hardware, collectively consuming vast amounts of power and emitting significant CO₂.
Serving large volumes of users demands scalable infrastructure. Real-time inference loads can strain data center resources and further amplify ongoing energy expenditures.

In response, leaders are employing strategies such as knowledge distillation (compressing large models into streamlined versions), pruning redundant computations, deploying energy-optimized hardware like AI-specific chips, blending more renewable energy sources into the computing mix, and pioneering on-device versions of their tools to keep computational loads distributed and efficient.

What is the influence of internet technologies like cookies on AI sustainability?

Although typically considered a matter of web privacy or personalization, cookies and similar data labels play a crucial but sometimes overlooked role in the AI sustainability conversation. Cookies facilitate website functionality, remember user preferences, and aid in personalizing digital content—all with minimal energy impact on their own.

Their wider influence emerges through efficient data management:

Cookies support local storage of user configurations, allowing some decisions and customizations to be handled directly on devices instead of incurring remote server loads.
Efficient cookie management reduces unnecessary network activity by transmitting only relevant data, which conserves bandwidth and lowers backend energy consumption.
Judicious use maximizes user privacy while helping minimize redundant processing—two goals aligned with the principles of responsible, sustainable AI.

Responsible implementation of such technologies enables smarter allocation of computational tasks, playing a supporting role in reducing overall energy consumption across an AI-enhanced digital ecosystem.

How does the World Economic Forum shape sustainable AI computing?

The World Economic Forum (WEF), a leading international non-governmental organization, takes a proactive role in advancing sustainability in AI computing. By convening experts, corporations, and governments, the WEF promotes collective action around developing, deploying, and regulating responsible AI and digital infrastructures. Their efforts span advocacy, strategic guidance, and dissemination of actionable frameworks, especially championing the transition to on-device AI.

Promoting research and best practices. The WEF releases influential whitepapers and policy reports advocating for energy-conscious AI design and deployment.
Convening global forums and working groups. These events foster dialogue between public and private sector leaders on balancing innovation, efficiency, and ethics.
Highlighting scalable solutions. The organization draws attention to real-world exemplars—such as SK Telecom’s or Microsoft’s AI energy efficiency initiatives—that demonstrate tangible results and can be adopted across industries.

Through these multisectoral activities, the WEF shapes both the agenda and the practical roadmap for advancing sustainability in the AI era, guiding stakeholders toward technologies that minimize environmental harm without curbing progress.

How can organizations and developers reduce energy consumption in AI computing?

Achieving sustainability in AI computing requires a multifaceted approach that integrates technology, policy, and operational improvements. Industry best practices have emerged to address both systemic and practical dimensions of the problem. Here is a cohesive step-by-step approach:

Optimizing algorithms and model architectures. Developers should prioritize creating lightweight models, implement knowledge distillation, and leverage quantization to decrease the energy required for both training and inference.
Shifting processing to edge devices where appropriate. By running inference on-device rather than in the cloud, energy used for data transfer and server-based computation is significantly reduced.
Selecting energy-efficient hardware solutions. Advanced hardware accelerators, such as AI-specific chips and energy-optimized GPUs, allow for faster, less power-hungry computations.
Opting for renewable energy sources. Powering data centers and edge nodes with green electricity lessens the environmental toll of their operation.
Using responsible data practices, including smart cookie management. Collecting and processing only necessary information curtails data overload and avoids superfluous computation.

By following these structured initiatives, organizations can cut their operational energy usage and lead the transition to more responsible, energy-aware AI solutions.

Charting the future: A path toward truly sustainable AI computing

The imperative to reduce energy consumption in AI computing for true sustainability is a defining challenge—and opportunity—of the technological era. The convergence of on-device AI, refined model design, responsible data and internet technology utilization, and proactive global leadership spearheaded by forums like the World Economic Forum offers a multifaceted roadmap for aligning digital progress with planetary responsibility.

Embracing these strategies does not mean slowing advancement, but rather ensuring that each leap forward in artificial intelligence leaves behind a smaller ecological footprint. Only by orchestrating technical ingenuity with concerted sustainability efforts can AI fulfill its potential as an engine for both economic and environmental benefit.