Mar 3, 2025

| Membership (fee-based) Save Article getty The world of enterprise AI is undergoing a quiet revolution. While large language models (LLMs) like ChatGPT have grabbed headlines, a new breed of AI is emerging from the shadows: small language models (SLMs). These compact powerhouses prove that bigger isn't always better, offering a compelling blend of efficiency, affordability and accuracy tailor-made for the business world. As described by FBC member Abhi Maheshwari in his 2024 article , "SLMs are smaller versions of LLM counterparts and have significantly fewer parameters, typically ranging from a few million to a few billion." SLMs are gaining popularity because they focus on specific tasks, unlike LLMs, which are trained on vast amounts of data to tackle a broad range of tasks. On the other hand, SLMs are purpose-built for specific applications. This targeted approach yields several advantages: Training an LLM like GPT-4 can cost millions of dollars and take months. 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." This makes AI accessible to businesses of all sizes, democratizing the power of this transformative technology. LLMs often struggle with accuracy. 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. SLMs can perform well in dynamic environments where rapid response is key. Using a closed-source LLM through an API may risk exposing sensitive information. In contrast, many open-source SLMs can be deployed in a private data center , offering greater control and security tailored to an organization’s needs. Challenges Of Small Language Models Trained on smaller datasets, SLMs have a narrower scope of knowledge than their larger counterparts. They may struggle with tasks requiring extensive general knowledge or deep contextual understanding. Besides this, SLMs are less adept at handling complex reasoning and problem-solving tasks that demand multistep planning and analysis. The smaller size of SLMs limits their ability to store lots of factual knowledge. This can lead to incorrect responses, more biases and irrelevant content. However, these issues can be addressed through several methods. These include using selected training data and targeted improvements after training. SLMs aren't meant to take the place of LLMs. Instead, they work alongside LLMs, creating a varied AI toolkit that meets a range of business needs. These small models can be combined to tackle even bigger challenges. Companies Embracing Small Language Models Forward-thinking companies are already reaping the benefits of SLMs. Most of the companies are trying to experiment SLM in pilot phase as this technology carries substantial potential. A report from The Times of India detailed how NoBroker, a real estate platform, developed SLMs to improve customer service interactions. These models comprehend multiple Indian languages, providing features like conversation summarization and sentiment analysis. Akhil Gupta, co-founder of NoBroker, emphasized with appropriate training data, SLMs can be highly effective for specific use cases. He stated, "You can build a model for a particular use case... with just 10 hours of recording." Future Prospects: A Bright Horizon For Tiny Titans Key trends shaping the future of SLMs include: • On-Device AI: SLMs allow AI to function directly on devices (such as smartphones), facilitating offline capabilities and enhancing privacy boundaries. • Multimodal Functionality: Recently, multimodal SLMs have begun to understand and generate not just text but also images, sounds, and various other modalities. This advancement enables applications in visual search, product recognition and audio analysis. • Personalized AI: Because SLM applications rely on user-specific data, they create opportunities for hyper-personalized AI experiences. Conclusion As enterprises investigate the possibilities presented by AI, SLMs are positioned to empower organizations to leverage the capabilities of this transformative technology in a manner that is efficient, affordable and specifically tailored to their distinct needs. Although the potential is vast, navigating this new terrain can be complex because organizations must adapt to rapid technological advancements.