In the last few years, many of us have started to see the benefits of using genAI in day-to-day tasks. But we’ve also been asked to reckon with the enormous environmental cost. Reporting has highlighted that these popular AI technologies have a significant environmental impact through high energy consumption, carbon emissions, and water use.
AI-focused data centers are increasing in size to accommodate larger and larger models and growing demand for AI services. According to a recent report from the International Energy Agency, a hyperscale, AI-focused data center can consume as much electricity annually as 100,000 households.
Large AI models require many energy-intensive calculations, necessitating liquid cooling systems. Large data centers can consume up to 5 million gallons per day, equivalent to the water use of a town populated by 10,000 to 50,000 people.
A greener alternative to large language models
So, what’s the alternative?
Popular genAI technologies all operate from “large language models”; advanced AI systems built on deep neural networks designed to process, understand and generate human-like outputs.
“Small language models” offer similar outputs but with a comparably smaller scope.
While large language models need massive data sets and hundreds of billions (or trillions) of parameters (learnings) to do their work, a small language model will typically require just 1 billion.
In this way, small language models require less memory, processing and storage, which are essential for reducing the overall resource footprint of AI systems. In some cases, small language models can even require 90% less energy to achieve similar results to their larger peers.
Think of it like using a jumbo jet to travel to your local shops as opposed to your car. For most of the tasks we want to use genAI to complete, there is no need to use a large language model. It’s overkill; a small language model would suffice.
Wider benefits of small language models
Not only do small language models have smaller energy requirements, they also cost far less to build and maintain. An important consideration for businesses that want to develop their own genAI technologies.
Small language models can run on devices that users or companies already own, making them easier to deploy locally, improving accessibility and reducing the need for extensive infrastructure support.
This means small language models also offer better security.
For example, using genAI in a defense context, you don’t want your national security to depend on a third-party product or be vulnerable to cyberattack.
Instead, small language models can be deployed on a local device, which allows your data to stay with you, and there’s no need for internet access.
Barriers to using small language models
So, what’s stopping us from using these greener, cheaper, safer generative AI alternatives already?
Small language models do currently face some limitations. Because they are smaller, their performance is different. They can’t help you with complex tasks and are better used for more specific topics or simple tasks.
Many of the things we look to large language models for assistance with day-to-day life can be accomplished with a properly trained small language model, like asking for a recipe, booking a hotel room or writing a report.
But so far, much more money has poured into the development of large language models, and big tech companies are now heavily invested in ensuring that investment pays off. Arguably, it’s in their business interests to maintain the performance gap their large, paid, models currently offer over smaller, open-access, alternatives.
This has left small language models somewhat overlooked. As a result, it’s mostly researchers and start-ups who are developing this space right now, which means investment is lower and progress is therefore slower.
Key concepts
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