Energy-Efficient Computing: How To Cut Costs and Scale Sustainably in 2026

With AI the centerpiece of technology and innovation today, energy efficient computing is quietly becoming one of the most urgent challenges.

In this article, we will discuss what makes energy efficient computing relevant for your organization, especially when modern resource-intensive AI workloads play an important role in driving your business operations and services.

What is energy efficient computing?

The term energy-efficient computing refers to an approach towards sustainable technology. Included in Gartner’s Top 10 Strategic Technology Trend for 2025, energy-efficient computing is “the design and development of efficient computing architecture, code, and algorithms that run on hardware optimized for reduced energy use.” Running certain systems on renewable energy sources can be a goal, for example.

That definition might sound academic, but its implications are anything but. As AI workloads explode and data centers scale to keep up, energy-efficient computing is fast becoming a strategic business imperative — one that affects everything from cost control to carbon emissions to brand reputation.

What energy-efficient computing really means

Energy-efficient computing isn’t a single technology. Instead, it’s a mindset that requires optimization across hardware, software, data, and operations.

At its core, the goal is to maximize compute performance per watt, squeezing more useful work out of every unit of energy. That involves improvements like:

• Hardware efficiency: Specialized chips (like GPUs, TPUs, and NPUs) that are designed to handle AI workloads more efficiently than general-purpose CPUs.
• Software optimization: Algorithms and frameworks that minimize redundant processing, compress data intelligently, and reduce the number of required computations.
• System-level design: Smarter workload distribution, dynamic cooling systems, and power-aware scheduling that align computing demand with renewable supply.

In practice, the most effective organizations are taking a holistic approach, treating energy efficiency as both a technical and strategic variable, embedded into architecture decisions, procurement, and even developer training.

The energy cost of intelligence and AI workloads

AI’s power comes at a price. Every query, model training run, and real-time inference consumes energy — sometimes staggering amounts. Let’s review the distribution of energy consumption in the enterprise IT industry.

According to the International Energy Agency (IEA) Global Energy Review 2025, there are two core issues: increased energy demand as well as increased electricity consumption. The globe needs more energy than ever…and we’re using more energy than ever before, too.

On the “using more energy” side, the globe consumed more than 1,100 terawatt-hours (TWh) in 2024. Expansion of the data center sector played a significant role in this increase: global data center capacity increased 20% in the U.S. alone.  

IEA estimates that electricity demands for data centers globally could double by 2030, reaching 945 TWh annually. This is higher than the entire electricity consumption of Japan. And that’s not just regular electric use by humans. AI is a significant driver.

Impact of AI workloads on data center energy

Energy consumption from AI-specific data center workloads could increase four- to six-fold by 2026 compared to 2023. For a real sense of scale, IEA estimates the U.S. will consume more energy for data and AI workloads than all of the energy-intensive manufacturing sector put together. That includes steel, cement, and chemicals. And the gap is widening.

To set some user expectations, a single ChatGPT query consumes around 0.3 Wh of energy per message. That sounds small until you multiply it by billions of queries. By 2027, AI workloads in the U.S. could account for more than 300 TWh of electricity annually — equivalent to over 20% of today’s total household electricity consumption.

This surge is in line with the expectations of AI vendors and investment firms that have planned for $500 billion investment to construct new data centers in the U.S.

The impact of energy demands for business: Predictions and trends

For organizations, that energy translates directly into operational cost. For the planet, it translates into carbon. For leadership, it raises questions of efficiency, responsibility, and risk:

• How much of your IT budget goes toward energy consumption?
• Can your infrastructure scale sustainably as AI workloads grow?
• What happens if regulators start mandating energy disclosures for AI operations?

The answers to these questions are already shaping the next era of computing strategy.

A return to CapEx? Increasing operational costs

Perhaps the most immediate and direct implication relates to cost. Rising demand for specialized hardware increases capital expenditures (CapEx), but investing in energy efficiency can significantly cut long-term operating costs (OpEx). The result: organizations can scale AI without runaway utility bills.

Reporting carbon emissions

Many companies are also expected to outline carbon emissions in ESG reporting specific to AI workloads. Sustainability disclosures may require organizations to disclose the environmental impact of their AI operations accurately down to the individual inference query. This will require:

• Automated monitoring
• Energy analytics
• Demand-side management of energy consumption

Efficiency energy frameworks

Meeting mandates regulations or achieving certain compliance may become one path. Frameworks will become foundational for certain activities, such as:

• ISO/IEC 30134 for defining Energy Performance Indicators (EnPIs), measurable metrics to track energy efficiency of systems, processes, and facilities over time.
• ISO 50001 for analyzing and managing AI-specific energy workloads.

Use case: energy efficiency isn’t easy

One model emerged as perhaps the most promising and impactful AI models developed with the energy-efficient computing paradigm, when Deepseek shook the AI industry recently. The team behind DeepSeek reported training a 600-billion-parameter model for only $5.6 million, a fraction of the $100 million cost estimated for OpenAI’s GPT-4.

There’s a caveat, of course: while that price differential was true at release, it may not portray the entire picture of how AI models consume energy. As MIT Technology Review points out, this lower cost is primarily due to differences in training data size, compute resources, and methodology — not just better energy efficiency.

Experts caution that such comparisons can be misleading, as true efficiency gains depend on a complex mix of factors, including hardware, datasets, and energy sources.

Energy-efficient data center design and strategies

AI is a new class of computation with fundamentally different energy dynamics. Training large language models, for instance, can consume millions of kilowatt-hours over weeks or months. Even inference — the day-to-day running of those models — adds a constant background load.

Techniques such as model distillation, quantization, and pruning are proven methods for reducing the computational and energy requirements of large AI models during both training and inference. You can find the list of the popular energy-efficient AI models here.

That said, there’s more to energy-efficient computing than regulating the model size, training, and inference methodology for LLMs. Practices such as intelligent HVAC and focusing data center construction in cold climates will be the primary source of energy efficient datacenter operations.

Related reading: data center optimization >

How AI can help solve energy inefficiencies

Now, here’s a twist: AI can help solve the very problem it creates.

Machine learning models can optimize cooling, predict peak demand, and dynamically route workloads to the most efficient hardware or region. Google and Meta have both reported 15–30% energy savings in data center cooling after applying AI-based optimization.

The broader implication is clear: AI energy efficiency becomes self-reinforcing. The more we invest in efficient AI, the better equipped it becomes to improve its own energy profile — a virtuous cycle that benefits both performance and sustainability goals.

Practical steps toward energy-efficient computing

Energy efficiency doesn’t happen by accident. It’s the result of intentional design choices across every layer of your stack. Some practical steps include:

1. Measure and monitor energy use. Track power usage effectiveness (PUE), carbon usage effectiveness (CUE), and energy per inference for AI workloads. Visibility is the first step toward optimization.
2. Consolidate and virtualize workloads. Reduce idle hardware, right-size clusters, and automate power management to match compute demand with utilization.
3. Adopt green coding practices. Encourage developers to write efficient algorithms, limit unnecessary computation, and leverage energy-aware APIs.
4. Leverage renewable-aware scheduling. Run non-urgent workloads when renewable energy supply is highest, aligning compute cycles with grid sustainability.
5. Invest in efficient infrastructure. Choose hardware with better performance-per-watt ratios, and data centers with advanced cooling and waste-heat reuse.

Over time, these steps compound. Even small improvements at the algorithmic or chip level can produce exponential efficiency gains when scaled across millions of operations.

Learn more about traditional IT infrastructure and infrastructure for AI >

Why energy efficiency should be on the C-suite agenda

For technical teams, energy-efficient computing is about infrastructure. For business leaders, it’s about resilience, reputation, and return on investment. Here’s why:

• Cost efficiency: Energy often represents 20–40% of data center operating costs. Cutting even 10% can translate into millions in annual savings.
• Brand and customer perception: Sustainable technology practices are becoming a differentiator in talent recruitment, customer trust, and market positioning.
• Operational continuity: As energy markets fluctuate, efficient infrastructure provides stability, ensuring your critical systems keep running even under constraint.

In short, energy efficiency is not just a technical optimization; it’s a strategic differentiator. Organizations that get this right can scale faster, spend less, and lead the conversation around responsible innovation.