As artificial intelligence (AI) continues to transform industries and unlock new opportunities, its environmental impact is also a matter of concern. While AI holds immense potential to combat climate change, it paradoxically contributes to the problem it aims to solve. The computational intensity of AI training and deployment leaves a significant carbon footprint. So, what’s the responsible way to savor the benefits of AI without worsening the climate crisis? The answer is Green AI.
So, What Is Green AI?
Green AI is a movement and an innovation that seeks to balance technological advancement with environmental sustainability. Green AI, also referred to as Sustainable AI or Net Zero AI, encompasses practices to reduce the carbon footprint of artificial intelligence technologies. Unlike traditional approaches, Green AI integrates sustainability into every stage of the AI lifecycle, from research and development to deployment and maintenance.
Furthermore, understanding the differences between conventional AI and Green AI is key to addressing this growing challenge.
Traditional AI vs. Green AI: A World of Difference
Traditional AI focuses on achieving unmatched accuracy in tasks like language translation, image recognition, and autonomous driving. While its applications are groundbreaking, this accuracy comes at a cost. Training large-scale AI models often require enormous computational resources, consuming vast amounts of energy.
For example, a nature.com study revealed the carbon footprint of training a single big language model is equal to around 300,000 kg of carbon dioxide emissions. This could be quantified as equivalent to 125 round-trip flights between New York and Beijing, a quantification that laypersons can visualize.
Thus, conventional AI overlooks energy efficiency. It also increases costs for businesses and excludes smaller players from entering the AI landscape. The worst outcome is the damage done to the environment from its carbon footprint, suppressing its potential to mitigate climate change.
In contrast, Green AI prioritizes energy-efficient practices. By focusing on sustainable development and deployment of AI systems, it seeks to minimize environmental harm without compromising innovation. Green AI introduces efficiency as a key metric alongside accuracy. It also advocates solutions that deliver high performance while conserving resources.
AI Powering Innovation but at What Cost?
We projected this study from ScienceDirect to understand the energy appetite of AI solutions. AI is growing rapidly, with bigger data needs and more complex models. However, this doesn’t always lead to equally big improvements in accuracy. While large language models (LLMs) like ChatGPT drive innovation, they come with significant environmental costs. Let’s dig deeper…
AI’s Growing Energy Appetite
The same report explains training GPT-3, for instance, consumed 1287 MWh of electricity and emitted 550 tons of carbon dioxide—comparable to flying 33 times between Australia and the UK.
The energy required for AI isn’t just during training. Using systems like GPT-3 also carries a hefty price. In January 2023 alone, GPT-3 processed 590 million queries, consuming energy equivalent to that of 175,000 people. On a smaller scale, each ChatGPT query uses as much power as running a 5W LED bulb for over an hour.
Fig: CO2 equivalent emissions for training ML models (blue) and of real-life cases (violet). In brackets, the billions of parameters adjusted for each model.
Source: ScienceDirect
Deloitte’s recent report, “Powering Artificial Intelligence: A study of AI’s environmental footprint”, revealed the following findings:
- Between 2021 and 2022, data centers accounted for 98% of Meta’s additional electricity use and 72% of Apple’s between 2022 and 2023.
- AI adoption will fuel data center power demand, likely reaching 1,000 terawatt-hours (TWh) by 2030, and potentially climbing to 2,000 TWh by 2050.
- This will account for 3% of global electricity consumption, indicating faster growth than in other uses like electric cars and green hydrogen production.
AI Data Centers: Energy Efficient or Energy Waste?
Data centers are the backbone of AI training and deployment, often referred to as the “cloud.” However, they rely on physical infrastructure for computing, processing, storing, and exchanging data. They require massive power and contribute heavily to the energy consumption of tech companies.
Different types of data centers have unique energy demands. Basic computer rooms handle simple tasks, while mid-size and large-scale enterprise data centers manage more complex operations. Hyperscale data centers, owned by tech giants have maximum hardware density and handle massive computational workloads, consuming the most energy.
Within this category, AI hyperscale data centers are emerging as a distinct segment. These centers are specifically built for generative AI and machine learning tasks, requiring high-performance GPUs for model training and inference.
This results in higher server power usage and the need for advanced cooling systems, further increasing energy consumption. Smaller data centers often lack the capacity for these high-demand workloads, driving the growth of AI-focused hyperscale facilities.
Fig: Data centers’ electricity consumption by server type and scenarios
But as they expand, a critical question remains: How sustainable are AI hyperscale data centers in the fight against climate change?
Well, this is where the demand for Green AI garners importance.
Why Green AI Matters?
The environmental cost of AI is no longer a hypothesis, it is palpable all around. Even blockchain technologies like cryptocurrency mining have demonstrated how unchecked digital innovation can lead to unsustainable energy consumption.
Coming straight to the topic, Green AI holds the promise of reversing this trend. For example, AI-powered tools can optimize supply chains, reduce waste, and improve energy grid efficiency. If developed responsibly, AI could become the key driving force behind the global effort to achieve carbon neutrality.
Thus, by combining innovation with sustainability, Green AI can meet the growing demand for computational power while reducing its impact on the environment.
Core Principles of Green AI
This means leveraging AI solutions that are not only effective in optimizing energy use in applications but are also inherently low-energy consumers. It’s crucial to balance AI’s benefits with its environmental impact. It means AI should support sustainability goals and not worsen the problems that it aims to solve.
Energy Efficiency
Green AI encourages the design of algorithms and models that consume less energy. Researchers can achieve this by developing lightweight models or installing techniques like pruning, quantization, and model distillation, which reduce computational requirements.
Hardware Optimization
Using energy-efficient hardware, such as GPUs with higher FLOPS per watt or specialized Tensor Processing Units (TPUs), can significantly cut AI’s energy consumption. Parallelizing tasks across multiple cores also helps reduce training times and emissions, though excessive cores may increase energy use disproportionately.
Another technique is edge computing which means processing data locally to avoid energy-intensive transmissions to cloud or data centers and optimizing resources for IoT (The Internet of Things) devices. Together, these strategies enable powerful AI performance with a smaller environmental footprint.
Data Center Optimization
Adopting renewable energy sources for powering data centers and AI operations is a significant milestone of Green AI. Companies like Google and Microsoft are already leading the charge by transitioning their cloud services to run on clean energy.
To make data centers more energy-efficient, researchers have created algorithms and frameworks that balance server loads, optimize cooling systems, and allocate resources more effectively. All these processes are included in data center optimization that cuts down energy use and emissions.
Transparency and Accessibility
Green AI promotes transparency in reporting the environmental costs of AI projects. Standardized metrics for energy consumption and emissions can help developers and organizations make informed decisions about their AI strategies.
Some of the tools that are used to estimate the carbon footprint of AI technologies are CarbonTracker, CodeCarbon, Green algorithms, and PowerTop.
Additionally, by lowering computational barriers, Green AI fosters inclusivity. Smaller organizations and researchers gain access to advanced tools without burdening themselves with high environmental and financial costs.
Fig: Achievable electricity demand reduction through energy savings, “High adoption” scenario
Policies Driving Green AI
The United Nations’ Sustainable Development Goals (SDGs) highlight the need for a sustainable future. Goals like Affordable and Clean Energy and Industry, Innovation, and Infrastructure are driving the rise of Green AI. Industry leaders are rethinking data center designs and operations to lower energy consumption and environmental impacts. This shows their eagerness to demonstrate proactive efforts toward sustainability.
While Green AI initiatives are mostly industry-led, some regions are implementing supportive policies. These range from monitoring low-impact data centers to stricter regulations for areas where grid stability is at risk. Thus, balancing these policies can encourage sustainable practices without moving operations to less regulated regions.
Notable policies include:
- European Code of Conduct for Data Centers (EU DC CoC)
- Energy Efficiency Directive (EED)
- Singapore Green Data Centre Roadmap
China has also introduced measures like the Three-Year Action Plan on New Data Centres, while the U.S. lacks federal-level regulations specific to data centers.
Policymakers can amplify these efforts by co-developing standards with industry leaders. Collaborative strategies ensure data centers meet climate goals without compromising growth or grid stability.
Green AI demonstrates that with the right policies and innovations, the tech industry can lead the way to a more sustainable future.
Green AI Takes the Spotlight at COP29
As world leaders convened in Baku, Azerbaijan, for COP29, discussions pointed to the role of AI in promoting environmental sustainability. A Deloitte-hosted panel brought together experts from NVIDIA, Crusoe Energy Systems, EON, and the International Energy Agency (IEA) to explore strategies for reducing AI’s environmental footprint.
Josh Parker, senior director of legal–corporate sustainability at NVIDIA, said,
“We see a very rapid trend toward direct-to-chip liquid cooling, which means water demands in data centers are dropping dramatically right now.”
According to NVIDIA, designing data centers while keeping energy efficiency at the highest priority right from the beginning is very much essential. As AI demands grow, sustainable infrastructure will be critical. Parker highlighted that current data centers are becoming outdated and inefficient.
He added, accelerated computing platforms are 10X more efficient than traditional systems for running workloads. This creates a significant opportunity to cut energy consumption in existing infrastructures.
Accelerated Computing: A Path to Green AI
Parker once again emphasized that accelerated computing represents the most energy-efficient platform for AI and many other applications. Over the past few years, energy efficiency for accelerated computing has improved dramatically, with a 100,000x reduction in energy consumption.
- In just the last two years, energy use for AI inference tasks dropped by 96%, with systems becoming 25x more efficient for the same workload.
Accelerated computing uses GPUs to process tasks faster and more efficiently than traditional CPUs. By handling multiple tasks simultaneously, GPUs reduce the energy required for AI workloads. It’s one of the techniques that come under hardware efficiency and data center optimization.
Furthermore, NVIDIA emphasized the need for energy-efficient infrastructure in data centers. Innovations like liquid-cooled GPUs are transforming cooling methods. Unlike traditional air conditioning, direct-to-chip liquid cooling consumes less power and water while maintaining effective temperature control.
The Bottom Line
Deloitte’s findings have adeptly showcased AI’s potential in driving climate-neutral economies. Green AI strategies focus on minimizing environmental impact by improving hardware design and increasing the use of renewable energy.
Industry leaders are spearheading these efforts, highlighting the effectiveness of sustainable computing practices. The shift toward accelerated computing and energy-efficient design is paving the way for AI to support global climate goals.
As we face a climate crisis, the integration of Green AI principles is no longer optional—it is essential. By redefining how AI solutions are developed, we can harness their power for good while minimizing their environmental toll. The road ahead demands collective effort, innovation, and accountability. Last but not least, Green AI is not just a technological imperative but a moral responsibility to ensure a greener future.
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