Small Language Models: An Efficient and Sustainable Alternative to LLMs?

Nearly two years after the launch of ChatGPT, the hype around large language models (LLMs) shows no signs of abating. But while generative AI (GenAI) holds great promise, many IT leaders see a risk of overspending on the necessary infrastructure. And while investment in GenAI is forecast to skyrocket in coming years, CIOs and other senior execs remain concerned about the ROI of such implementations. That’s where small language models (SLMs) come in – offering a more efficient, sustainable, and lower-cost alternative to LLMs.

AI Is on the Rise – But Doubts Remain Around ROI

According to market intelligence experts IDC, GenAI spend in 2024 is up 100% year-on-year and is expected to total $151 billion in 2027. These are impressive figures, but the ROI delivered by AI implementations is still a worry. Recent Accenture research shows 35% of C?level execs now report more cost-reduction benefit from implementing AI – up from 24% only six months earlier. But while the trend is upward, there’s still no definite consensus.

This may be set to change with the adoption of more efficient, sustainable, and lower-cost small language models (SLMs). Gartner predicts that by 2027 more than 50% of GenAI models used by large businesses will be designed specifically for industry- or business-specific functions, compared with around just 1% in 2023.

Smaller, More Efficient GenAI: SLMs in a Nutshell

But what are SLMs? Sometimes also called specialized language models, SLMs offer machine learning (ML) algorithms trained on much smaller and more specific data sets than those used in LLMs. And often, this data is enterprise specific.

SLMs combine advanced AI capabilities with reduced computational demand. While LLMs generally offer millions, or even billions, of parameters, these are often unnecessary in enterprise settings. Consequently, customized SLMs are usually the more efficient solution for handling more specific tasks.

Many and Varied Use Cases

The applications for SLMs are similar to those for LLMs, only with a much narrower focus. For example, the tech is ideally suited for use with specialized image- and language-generating models. SLMs can handle many other tasks, including translation, market-trend analysis, and customer service. Companies can also leverage the tech to manage IT tickets or even as chatbots/virtual business assistants. In addition, it’s used in edge technologies such as smartphones, tablets, and laptops.

Where SLMs Shine

To gauge the pros and cons of LLMs versus SLMs, we need to compare the two in greater detail. First, let’s consider some upsides of SLMs. Based on relatively small domain-specific data sets, small language models tend to perform their tasks more efficiently and accurately. What’s more, they can do so almost twice as fast as LLMs and with 27% less memory overhead.

As shown in the table below, SLMs have the advantage of greater cost accuracy compared to LLMs, and they’re also more sustainable. And because they’re built on customized, rather than cloud-based, models, they carry a lower risk of privacy violations.

Where SLMs Do Less Well

While these benefits are appealing, it’s important to keep sight of the weaknesses of SLMs. For one thing, they tend to struggle with nuance, context, and creativity. That’s hardly surprising given the greater volumes of training data and wider variety of data sets that LLMs work with.

What’s more, SLMs can pose challenges for IT organizations – not least because of the high-quality data needed to avoid errors. Also, integrating small language models into the business can be complex and costly. And wherever client data is involved, legal and ethical considerations must be taken into account.

Cost Comparison: LLMs Versus SLMs

Large and small language models differ not only when it comes to infrastructures, implementation complexity, and sustainability, but also as regards the various costs and returns involved. The following table compares SLMs and LLMs in these respects:

?Better for Business, Better for the Environment

When considering how best to deploy AI, companies should consider whether they have the technical infrastructure, architecture, operating model, and governance structure needed to meet the high compute demands of LLMs and GenAI. But they must also keep a close eye on cost and sustainable energy consumption.

One distinct advantage of SLMs in enterprise architectures is their sustainability. AI is notoriously energy intensive, pushing up the cost of this already costly technology. By deploying SLMs where appropriate, organizations can reduce their AI footprint, thus helping them achieve their sustainability goals.

Pros and Cons of LLMs versus SLMs: An Overview

To sum up: SLMs have the edge when it comes to efficiency, in terms of cost, use cases, and energy use. However, they lag behind LLMs when it comes to accuracy, complexity, and flexibility.

LLMs, on the other hand, win out in terms of capability, performance, and versatility. But operating them involves considerable resources and infrastructure – and, therefore, higher costs.

If you’re thinking of embarking on an SLM journey, you should start by assessing the cost and benefit of the tech compared to other AI or analytical approaches. Any evaluation of this kind must consider various factors, including the need to retrofit data centers and introduce new chip set architectures, hardware innovations, and efficient algorithms.

Questions, Comments??

Want to learn more about SLMs and discover whether they’re right for your business? Then feel free to contact me. And if you have your own ideas about the relative merits of LLMs and SLMs, please share them in the comments below.