Large Language Models have captured the attention of CxOs and have become the key point of discussion in boardrooms. Engineering teams are in a race to go live with their first production Generative AI use case. Though many organizations have successfully completed their PoCs, many are still reluctant to go to production.

One of the main reasons for this is the realization that running an LLM in production is extremely resource intensive. While hosted models, like Azure OpenAI, has made it easy to get started on your Generative AI journey and has also addressed the security concerns to a large extent, cost and performance are still concerning factors. The ‘per token’ pricing of these services doesn’t help the cause either. Enter Small Language Models (SLM).

Small Language Models (SLMs) are quickly emerging as a practical alternative to their larger counterparts, offering a range of benefits that make them particularly appealing for certain applications. While Large Language Models (LLMs) like GPT-4o have garnered much attention for their ability to generate human-like content, SLMs are proving to be more than just a scaled-down version. By design, they are lightweight and hence require fewer computational resources, which also makes them ideal for deployment in environments with limited processing power. Their smaller footprint also enables them to respond quickly which make them more suitable where real-time responses are essential with limited resources.

What are Small Language Models

Small Language Models (SLMs), like their larger counterparts, are machine learning models that are pre-trained. SLMs are trained on a smaller dataset, which can be highly specialized, allowing them to perform exceptionally well on specific tasks. This specialization also means they can be fine-tuned to understand organization or domain specific vocabularies and concepts, making them suitable for industry-specific applications such as underwriting or employee onboarding. Moreover, the reduced size of SLMs translates to lower resource utilization, addressing the main concerns CxOs have on LLMs that prevents them from taking their use cases to production.

Microsoft Phi-3, Mistral 7B, Google Gemma are all examples of Small Language Models. As per Microsoft, its Phi-3-mini measuring 3.8 billion parameters performs better than models twice its size. This remarkable efficiency is not just a testament to the model’s design but also to the underlying advancements in machine learning that enable such capabilities. As these models continue to improve, they promise to unlock new possibilities across various industries, from healthcare to education, by providing advanced AI capabilities without the prohibitive costs and computational demands of larger models.

Differences between a Large Language Model and Small Language Model

Let us explore some of the key differences between a Large Language Model (LLM) and a Small Language Model (SLM). We will be comparing them on the following: –

• Parameters – Technically, they are the weights and biases of the layers in a neural network. To put it more simply, it represents the number of variables the model has learned as part of its training. Parameters allow the language model to better emulate how the human brain works. Generally, the more is better.
• Training Dataset – This represents the quantity and quality of the dataset that a model is fed for its training. While large amount of data makes a model versatile, it can negatively impact the quality of the output since most of the data may not be curated.
• Versatility – This represents the different use cases for which a particular model can be used. These use cases can be across operations like content generation, sentiment analysis, summarization, code generation etc. over a wide range of languages, domains and scenarios.
• Resource Requirement – This represents how resource intensive it is to train and run a model. In general, the larger the model, the more resources it needs. The main resource needed for running a model is GPU.
• Speed – This shows how fast a model can draw inferences from an input and prepare the required output. While this is also dependent on the resources available for the model, we are looking at a situation where resources are limited, which is more likely the scenario when an enterprise is trying to make use of the model.
• Portability – This represents where you can run your model. Higher portability gives immense deployment flexibility to the organizations. A highly portable model can be deployed on a wide range of targets, and possibly offline also (e.g. on smartphones).
• Customizability – This refers to the ability of the model to be customized to suit a particular need without incurring too much expense.

	Large Language Model	Small Language Model
No. of Parameters	Trillions of Parameters	A few Billions of Parameters
Training Dataset	Large amount of text scraped from publicly available internet sources	Low amount of high-quality dataset relevant to the purpose of the model
Versatility	Can handle a wide range of use cases and scenarios	Suitable for specific use cases and scenarios
Resource Requirement	High Resource Requirement, probably hundreds or thousands of GPUs	Relatively low resource requirement
Speed	Relatively slow	Faster turnaround times
Portability	Low portability owing to the larger footprint of the model	High portability due to the smaller footprint
Customizability	Hard and Expensive	Relatively easier to Customize and fine-tune for a specific need

One of the key advantages of SLMs is their potential for customization. Organizations can train these models on their proprietary data, aligning the insights generated with their specific needs and knowledge domains. This also adds an extra layer of security, as the data and the insights derived from it remain within the control of the organization.

In terms of research and development, SLMs offer a more accessible platform for experimentation. Researchers can explore new training techniques, model interpretability, and safety improvements without the prohibitive costs associated with running LLMs. This democratization of access can accelerate innovation in the field of natural language processing.

SLMs unlock big opportunities for enterprises to ride the wave of Artificial Intelligence, offering a more accessible entry point for businesses of all sizes to integrate advanced AI capabilities into their applications. The capability of SLMs to work in an offline mode opens immense opportunities to leverage Generative AI for use cases never explored before. By prioritizing and accelerating the adoption of SLMs, companies can gain a competitive edge, fostering innovation and driving growth in the rapidly evolving digital landscape. SLMs offer significant advantages to regulated industries where data privacy is of paramount importance. All these benefits of SLMs begs a question.

Are LLMs becoming obsolete?

No, not by any stretch of imagination. LLMs will remain the gold standard in Generative AI when it comes to executing complex tasks. As we progress, LLMs will undergo transformations and will become leaner and meaner. As with anything else in technology, the definitions of “small” and “large” will change rapidly with the advancements in computing. Both LLMs and SLMs will co-exist and will have its own use cases. There will also be scenarios when application architects would want to simultaneously leverage the power of both, to enhance user experience. More frameworks will emerge that facilitates this seamless transition between SLMs and LLMs.

Closing Remarks

Small Language Models (SLMs) are quickly carving out their niche in the AI landscape. As the technology continues to evolve, we can expect SLMs to play a significant role in the future of machine learning and artificial intelligence. Their ability to deliver specialized knowledge in a more sustainable and cost-effective manner positions them as a valuable tool for both industry and research. They offer a balance between performance and efficiency, making them a more practical choice for many Organizations in their Generative AI journey.

Related: Unleash the full potential of Generative AI with Function Calling