How Small Language Models are Democratizing AI in Business

When people talk about generative artificial intelligence, they tend to picture the large, general-purpose tools that have become part of everyday life. These systems, built on so-called large language models (LLMs), can write, summarize, code, and converse with remarkable fluency. Their rapid evolution has fuelled excitement across industries – but it has also exposed important limitations for business use.

LLMs require vast amounts of computing power, energy, and infrastructure, making them expensive to train and operate at scale. For companies, they also raise concerns around data privacy, control, and latency, particularly when sensitive information must be processed via external platforms. As a result, many organisations are now exploring an alternative approach: smaller, more focused AI models designed for specific tasks.

A growing interest in smaller models

Small language models (SLMs) are trained to perform specific tasks using far fewer computing resources. They can be used to manage sensitive company information while minimizing the risk of data leaks. They are also designed to be used consistently within a business, delivering optimal results without the heavy computational demands of larger models.

This shift is gaining momentum across sectors, according to the latest Innovation Radar report from SDG Group, with analysts pointing to the downsizing of language models. Thanks to their technical characteristics, these SLMs can be integrated into existing IT infrastructure to unlock greater value.

A report by consulting firm S&S Insider highlights that the global SLM market was valued at US $7.9 billion in 2023 and is expected to reach US $29.64 billion by 2032, with an average annual growth rate of around 16% between 2024 and 2032.

As they gain traction, organizations are beginning to ask whether it is better to use a single tool for every problem, or to deploy specialised systems for clearly defined tasks.

The case for smaller models

One major advantage of SLMs is greater specialization. Reducing the size and computational requirements makes AI faster and more affordable, and viable on hardware already used by most companies. That lowers barriers to adoption and allows smaller organisations to experiment with AI without major capital investment.

There are two main reasons why businesses turn to smaller models. The first is cost. Techniques such as quantization, pruning, and distillation let developers reduce computing requirements by, respectively, lowering numerical precision, removing redundant connections, or training a smaller model using a larger one as a reference. The guiding principle is increasingly pragmatic: choose the smallest model that can do the job reliably, rather than defaulting to the largest available system.

The second reason is efficiency. General-purpose LLMs typically perform well across a wide range of tasks, but businesses often need an extra edge to remain competitive. Achieving that margin of performance with a generic model can be expensive and inefficient.

This is when fine-tuning offers an alternative. By adapting a pre-trained model to a narrower dataset, organisations can improve performance. Techniques such as low-rank adaptation (LoRA), which is the optimization of a neural network to improve model performance on specific tasks, have made this process more efficient using a company’s existing data.

Trade-offs, not shortcuts

None of these approaches is without cost. Fine-tuning and optimisation require high-performance hardware and costly training, while more complex systems can lengthen development cycles and push up maintenance costs. In some cases, a company will require a more complex and capable model, while in others, a less sophisticated option that can be developed more quickly will fit, allowing a project to get off the ground sooner and compete effectively. The challenge is in evaluating multiple approaches and determining which will genuinely add value.

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