How Small Language Models Drive Business Efficiency

Google’s Gemma 3, based on Gemini 2.0, is a collection of lightweight, state-of-the-art open models designed to run fast, directly on devices — from phones and laptops to workstations. With smaller language models, the option to run on local hardware brings a measure of cost control. But once the model is built, there should not be significant cost increases due to usage. Another factor driving the interest in small language models is their lower cost. Most LLMs operate on a pay-as-you-go, cloud-based model, and users are charged per token (a number of characters) sent or received.

How SLMs are revolutionising AI applications

Though they also suffer from bias, LLMs’ broader training and generalisation capabilities allow them to learn from other sources without as much dataset-specific bias as SLMs encounter. The most common types of LLMs are language representation, zero-shot model, multimodal, and fine-tuned. While these four types of models have much in common, their differences revolve around their ability to make predictions, the type of media they’re trained on, and their degree of customization. Even though the big AI players offer versions of SLMs through a service model where they provide the underlying engine, “you still need people who know what the right data is.

By facilitating sophisticated natural language processing tasks such as translation, content creation, and chat-based interactions, LLMs have revolutionized many industries. However, despite their many benefits, LLMs have challenges and limitations that may affect their efficacy and real-world usefulness. LLMs offer an enormous potential productivity boost for organizations, making them a valuable asset for organizations that generate large volumes of data. Below are some of the benefits LLMs deliver to companies that leverage their capabilities. What’s more, SLMs present many of the same challenges as LLMs when it comes to governance and security.

Large Language Model Operations (LLMOps) Specialization, by Duke University

The courses below offer guidance on techniques ranging from fine-tuning LLMs to training LLMs using various datasets. These courses by Google, DeepMind, and Duke University are all available on the Coursera platform. SLMs can be very accurate about straightforward questions, like an inquiry into current benefits. But if an employee says “I would like to pay a third mortgage; can I draw off my 401(k)? An LLM might be better at handling this type of question, as it could include information on HR and tax standards for 401(k) use.

If LLMs are intentionally built to achieve Artificial General Intelligence (AGI), small language models are made for specific use cases. The ability to deploy SLMs in complex reasoning tasks can be very useful as enterprises are looking for new ways to use these new models in different environments and applications. “SLMs are designed to be more compact and efficient, typically containing fewer parameters than LLMs. This smaller size doesn’t necessarily mean reduced capability; rather, it often translates to faster processing and lower computational costs, especially in resource-constrained environments,” says Dr Patil. Unlike SLMs, LLMs can perform reasonably well on tasks beyond their training scope and are better suited to tackle complex tasks.

Dr. Magesh Kasthuri, a member of the technical staff at Wipro in India, says he doesn’t think LLMs are more error-prone than SLMs but agrees that LLM hallucinations can be a concern. As devices grow in power and SLMs become more efficient, the trend is to push more powerful models ever closer to the end user. Microsoft, for example, trained its Phi-1 transformer-based model to write Python code with a high level of accuracy – by some estimates, it was 25 times better. In other experiments, they found that a Qwen2.5 model with 500 million parameters can outperform GPT-4o with the right compute-optimal TTS strategy. Using the same strategy, the 1.5B distilled version of DeepSeek-R1 outperformed o1-preview and o1-mini on MATH-500 and AIME24. Based on these findings, developers can create compute-optimal TTS strategies that take into account the policy model, PRM and problem difficulty to make the best use of compute budget to solve reasoning problems.

Fine-Tuned or Domain-Specific Models

IBM’s Granite 13 billion parameter model, despite being more than five times smaller, performed better than Llama 2 with 70 billion parameters in 9 out of 11 finance-related tasks. It takes deep problem knowledge – as well as an understanding of each model type’s optimal use cases – to determine where in your AI system to deploy SLMs, LLMs, or both. With intelligent routing to direct tasks to the right model, a multi-model system can make a big impact on both your bottom line and the user experience. SLMs can be impactful on key verticals including customer relationship management, finance and retail. As the number and type of available AI models continue to grow, businesses will need to understand the range of what’s available to create their AI model portfolio. Sales representatives might need to access a generative AI model containing sensitive data at a client site to provide tailored recommendations.

What Is an LLM and How Does It Work?

The models ingest immense volumes of text, sounds and visual data and train themselves to learn from hundreds of billions or even trillions of variables, called parameters, according to IBM. Small language models (SLMs), usually defined as using no more than 10 to 15 billion parameters, are attracting interest, both from commercial enterprises and in the public sector. An alternative approach is “external TTS,” where model performance is enhanced with (as the name implies) outside help.

He stated, «You can build a model for a particular use case… with just 10 hours of recording.» They can exhibit bias and «hallucinations,» generating plausible but factually incorrect or nonsensical information. SLMs can minimize the risk of these issues by training on carefully curated, domain-specific datasets. This is crucial for businesses where accuracy is paramount, from customer service to financial analysis. Additionally, to adapt to evolving business needs, SLMs can be quickly fine-tuned and updated.

LLMs like OpenAI’s GPT-4 or Meta’s Llama 3.1 are trained on vast amounts of data with the end goal of performing a wide range of tasks across various domains. Advancements in artificial intelligence and generative AI are pushing the boundaries of what was once considered far-fetched in the computing sector. LLMs trained on hundreds of billions of parameters can navigate the obstacles of interacting with machines in a human-like manner. Small language models, known as SLMs, create intriguing possibilities for business leaders looking to take advantage of artificial intelligence and machine learning. The study validates that SLMs can perform better than larger models when applying compute-optimal test-time scaling methods.

• Microsoft, for example, trained its Phi-1 transformer-based model to write Python code with a high level of accuracy – by some estimates, it was 25 times better.
• For all their benefits, SLMs still require solid data governance to ensure high-quality results.
• If the dataset is very small, controlled, and available, such as HR documents or product descriptions, it makes great sense to use an SLM.
• It takes deep problem knowledge – as well as an understanding of each model type’s optimal use cases – to determine where in your AI system to deploy SLMs, LLMs, or both.
• A model trained on a more limited data set is less likely to produce some of the ambiguous and occasionally embarrassing results attributed to LLMs.

SLM vs LLM: Bigger isn’t always better

“We are seeing a lot of focus on generative AI throughout the drugs discovery process. We are talking about LLMs and SLMs, as well as machine learning,” says Tamersoy. Explore the future of AI on August 5 in San Francisco—join Block, GSK, and SAP at Autonomous Workforces to discover how enterprises are scaling multi-agent systems with real-world results. Join leaders from Block, GSK, and SAP for an exclusive look at how autonomous agents are reshaping enterprise workflows – from real-time decision-making to end-to-end automation. Digit.in is one of the most trusted and popular technology media portals in India. At Digit it is our goal to help Indian technology users decide what tech products they should buy.

If it’s a broad, more complex task where there is heavy reasoning required and a need to understand context, that is maybe where you would stick to an LLM. SLMs, in contrast, require significantly fewer resources, slashing training costs. Maheshwari notes that «SLMs cost just 1/10th of what LLMs require, offering a highly cost-effective solution for many enterprise applications.»