How SLMs are transforming enterprise AI

The adoption of artificial intelligence (AI) is no longer something of the future: it is happening now. Sectors such as banking, education, and advertising are already using generative AI to optimize processes, improve productivity, and make faster and more accurate decisions.

This article explores how generative language models, both large (LLMs) and small (SLMs), are impacting businesses, and how fine-tuning SLMs opens the door to more efficient solutions tailored to specific needs.

What are LLMs and SLMs?

Large Language Models (LLMs), such as GPT-4 or Claude, are trained with enormous amounts of data and have the ability to generate text, translate, or answer questions. However, their generalist nature means that in industrial environments they may lack the necessary precision and consistency.

This is where SLMs (Small Language Models) come in: smaller, specialized, and optimized models that allow specific tasks to be performed with fewer resources, less energy consumption, and greater speed.

Benefits of SLMs

• Low computing requirements.
• Lower energy consumption.
• Faster responses, ideal for real-time applications.
• Greater privacy thanks to local execution, without relying on the cloud.
• Possibility of customization for specific sectors (legal, healthcare, infrastructure, etc.).
• Real-world applications of SLMs

Real-world applications of SLMs

Despite their limitations, SLMs are already finding practical uses in multiple sectors:

• Chatbots and virtual assistants: lightweight enough to run on mobile phones or embedded devices, maintaining real-time interaction.
• Code generation: models such as Phi-3.5 Mini help developers write and debug software more efficiently.
• Content summarization and generation: used in marketing, social media, and reporting.
• Healthcare: enable symptom checks and preliminary analyses to be performed directly on devices, without a cloud connection.
• IoT and Edge Computing: they power smart devices in the home or industry, reducing latency and improving privacy.

These cases show that SLMs are not just a “cut-down” version of LLMs, but a technology with its own space and enormous growth potential.

Model fine-tuning with LoRA and QLoRA

One of the keys to the success of SLMs is their fine-tuning capability. Thanks to techniques such as LoRA or QLoRA, it is possible to adapt a base model to a specific domain efficiently, without the need to train from scratch and with much lower resource consumption.

This opens the door to micro-agents: lightweight models designed to perform specific tasks, such as extracting fields from invoices or standardizing addresses, in a modular, fast, and cost-effective way.

Looking to the future

Generative AI is ushering in a new era in the industry, where efficiency and specialization are key. LLMs, SLMs, and AI agents all have a key role to play in this landscape. Each approach has specific advantages: LLMs are broad, SLMs are efficient, and Agents offer planning and adaptability.

Combining generative AI with human expertise can help companies recover lost tacit knowledge and lead the next industrial revolution. AI agents, with their ability to plan, reason, and act, are essential for addressing complex and dynamic challenges in today’s business and industrial environment.

Implementing LLMs, SLMs, and AI agents is not just a matter of keeping up, but of positioning yourself as a leader in innovation and efficiency within the industry.