What do leading-edge CDOs already in ML production say works best? Download this report to find out.

AI is the hottest technology in town. Touted as a near-magical solution, it solves myriad business problems, from automating customer service conversations to detecting fraud and even writing blogs much like this one.

But there’s a hidden cost to the incredible power of AI that most people don’t think about — its unprecedented energy demands. 

You thought Bitcoin‘s carbon footprint was terrible? AI, given its immense computational needs, is energy-intensive on an entirely different scale. It uses more energy than other forms of computing, and training a single model can eat up more electricity than 100 U.S. homes use in an entire year. It doesn’t stop there: Gartner predicts that by 2025, the energy use of AI could surpass that of the entire global human workforce.

For businesses trying to reduce their carbon footprint and still take advantage of AI, this poses a pressing problem – how do you make sure your company meets its environmental, social, and corporate governance (ESG) goals without sacrificing AI’s ability to deliver results?

To answer that question, you need to know several things: How much energy does AI actually use? And are there ways to make it more energy efficient? Let’s explore.

AI’s Dirty Little Secret: High Energy Demands

First, let’s talk about why AI is so power-hungry. AI, specifically in deep learning workloads, is energy-intensive due to several factors:

The bottom line is that AI uses a lot of energy. The energy required to power these machines has already become an issue for cloud providers. And, as more companies adopt AI, the energy demand will only increase.

Measuring the Carbon Footprint of AI Models

So how much energy does AI actually use? Measuring an AI model’s energy and carbon footprint can be challenging as it involves numerous variables. On top of that, the amount of energy used depends on the type of hardware and software used in a particular environment.

Nevertheless, several studies have tried to estimate the energy required by typical deep-learning models. Researchers at the University of Massachusetts, Amherst, studied the energy consumption of four key AI models – Transformer, ELMo, BERT, and GPT-2. They trained each model on a single GPU for up to a day and measured its power consumption.

They discovered that training a large AI model can generate over 626,000 pounds of CO2 —  nearly five times the total emissions from an average American car over its lifespan, factoring in the emissions from manufacturing the car as well!

Source: MIT Technology Review

Without additional tuning, BERT, the most energy-intensive model, produced about 1,400 pounds of CO2 equivalent – emissions generated by a trans-America round-trip flight.

However, this is just the baseline — developing or adapting AI models requires many training and tuning rounds. A case study revealed that creating a final model for academic publication – involving training nearly 4,800 models over six months – emitted over 78,000 pounds of CO2.

Clearly, there’s an urgent need for sustainable AI solutions. If we don’t refine our AI strategies with the environment in mind, it could have wide-ranging effects – from increased carbon emissions to rising utility costs.

Could AI Hinder the Progress on ESG Goals?

Tech industry frontrunners have made significant strides in recent years, initiating comprehensive strategies to transition towards renewable energy sources:

AI’s high energy consumption will undoubtedly add pressure to these ongoing initiatives. The issue of energy-efficient AI could become an important part of the broader conversation about sustainable development. So how can businesses reduce energy costs while still meeting their corporate energy, sustainability, and governance (ESG) goals?

Can Energy-Efficient AI & ESG Policies Pave the Way for a Sustainable Future?

As AI practitioners become aware of their growing energy footprint, sustainable AI practices are emerging, such as the use of specialized hardware to reduce energy consumption, energy-efficient MLops practices, and algorithmic efficiency tactics: 

Wallaroo & Ampere: Energy-Efficient, Low-Cost Machine Learning Inferencing in the Cloud

While these strategies can help reduce energy consumption, they are challenging to execute on your own. Fortunately, thanks to a joint solution from Ampere and Wallaroo.AI, it is possible to have energy-efficient AI and still meet ESG goals.

Now organizations can combine both specialized hardware (Ampere® Altra® 64-bit processors) and an ML production platform from Wallaroo.AI to deliver low-cost, energy-efficient ML inferencing in the cloud.

Ampere’s Altra® 64-bit processors are optimized for AI workflows and provide up to 6x higher performance density compared with traditional servers. 

Wallaroo.AI’s highly optimized inference server, which can cost-efficiently run many AI/ML workloads, reduces the manual effort required and helps teams focus on more important tasks, such as optimization and strategy.

By leveraging the powerful synergy arising from these innovative solutions, enterprises can leverage AI without compromising on their corporate ESG goals.

The Future of Sustainable AI: Energy-Efficient AI & ESG

In a world fueled by rapidly advancing technology, the adage “with great power comes great responsibility” resonates more profoundly than ever. With each groundbreaking innovation and disruptive idea, we witness the immense potential that technology holds. Yet, it’s imperative that we remain aware of the environmental, social, and governance (ESG) implications accompanying these technological leaps.

The collaboration between Ampere and Wallaroo.AI is a critical milestone in the journey toward responsible growth and sustainable development. It demonstrates that with the right technology and cutting-edge innovation, we can have both AI and sustainability coexist harmoniously. And this is just the beginning.

For businesses, this is an exciting time. The advent of solutions aimed at energy-efficient AI means that they can genuinely harness the power of AI without the nagging worry of an ever-increasing carbon footprint and without compromising on ESG goals. It’s a win-win.

The collaboration between Ampere and Wallaroo.AI symbolizes more than a technological advancement; it’s a testament to our commitment to a future where technology and sustainability walk hand in hand.

Contact us today to discover how you can make AI part of your sustainability strategy.

Your data. Your tools.
Your ecosystem.

No need to replace your data platform or change the popular tools your data scientists use to build models.

Keep up to date with the latest ML production news sign up for the Wallaroo.AI newsletter

Platform Learn how our unified platform enables ML deployment, serving, observability and optimization
Technology Get a deeper dive into the unique technology behind our ML production platform
Solutions See how our unified ML platform supports any model for any use case
Computer Vision (AI) Run even complex models in constrained environments, with hundreds or thousands of endpoints