Energy-Efficient AI & ESG: Shaping a Sustainable Future
August 17, 2023AI 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:
- Data Volume: Deep learning models require a lot of data for training. Storing, moving, and processing these enormous amounts of data consumes significant energy.
- High Computational Demands: These models involve computationally intensive operations like matrix multiplications, vector operations, etc., often performed on floating-point numbers that require more resources.
- Iterative Training: The training process is iterative, meaning the model goes through the entire dataset multiple times (epochs) until it reaches a certain level of accuracy. Each iteration involves extensive computations which are energy-intensive.
- Hyperparameter Tuning: The process of finding the right set of hyperparameters often involves trial and error, leading to repeated model training sessions. This multiplies the energy consumption.
- Large Model Size: Models like GPT-4 or BERT have billions of parameters. Storing and updating these parameters during backpropagation requires substantial computational resources.
- Use of GPUs: GPUs, commonly used for training deep learning models due to their ability to perform parallel processing, consume a lot of power, particularly under heavy computational load.
- Lack of Optimized Software and Hardware: AI software libraries are often not optimized for the specific hardware they run on, and much of the hardware used today is also not optimized for AI computations.
- Model Serving and Inference: Although less energy-consuming than training, serving AI models for inference still requires significant computational resources, especially for large-scale, real-time applications.
- Data Center Energy Costs: Beyond the direct computational costs, there are indirect energy costs associated with running large data centers, such as cooling, power conversion losses, and maintaining redundant power supplies for reliability.
- Long Training Times: Training large-scale deep learning models can take a long time, leading to significant energy usage.
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:
- Google has developed methods to reduce their energy bills by 40% by aligning power needs with weather and consumer demand patterns. They aim to run their data centers carbon-free by 2030.
- Meta reported that they have reduced their emissions by 12.3 million metric tons of carbon dioxide equivalent (CO2e) since 2018, intending to achieve net zero emissions across their value chain in 2030.
- The EU is working towards climate-neutral data centers by 2030, urging data centers to utilize efficient cooling systems, heat reuse, improved infrastructure, and renewable energy.
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:
- Specialized hardware: Devices tailored for parallel processing, frequently used in AI computations, can perform these tasks more efficiently than conventional CPUs or GPUs, resulting in notable energy savings.
- Energy-efficient ML operations practices: ML operations (which some people call MLOps) is an evolution of DevOps principles adapted to facilitate the unique needs and challenges of machine learning. Aimed at making the lives of both data scientists and data engineers easier, it integrates practices such as efficient resource management, automated model training and evaluation, and streamlined deployment of trained models to production environments. This process also includes intelligent job scheduling, contributing to more efficient use of resources. Ultimately, these practices not only simplify the workflows for machine learning practitioners but also lead to significant energy savings.
- Algorithmic efficiency tactics: These involve using methods that require less computational resources. For instance, transfer learning involves using pre-trained models on related tasks to reduce the amount of training required, which can save energy. Similarly, federated learning involves training models on decentralized data sources, reducing the need for data transmission and central processing, which can also be more energy-efficient. Small data techniques, on the other hand, are strategies to work with smaller datasets, thereby reducing the computational load.
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.
