Small Language Models (SLMs) are a form of condensed Large Language Models (LLMs). SLMs have fewer parameters that make them quicker and more efficient than that of LLMs. They are designed for mobile apps with low latency and low resource requirements.  Small language models can handle NLP tasks with less memory which makes them ideal for compact offline systems. SLMs perform better than large language models (LLMs) in terms of efficiency and size because of their practically optimized architecture.

Introduction

In the last two years, large language models (LLMs) have been utilized in various applications such as natural language processing (NLP), chatbots, coding assistants, and generating automated content. LLMs require significant resources and memory which has constrained its usage for small-scale products. This blog post explains how small language models (SLMs) can offer a promising  and scalable alternative to LLMs for compact AI tasks.

How small language models work

Small language models (SLMs) aim to focus on building a compact architecture that requires a limited number of parameters. This improves system performance and makes them more efficient at handling tasks. The following techniques are used to develop such memory-efficient models:

• Knowledge distillation:
  Knowledge distillation is the process of replicating task-specific functions from a large language model (LLMs) to a smaller one (SLM). SLMs are pre-trained models that observe and learn from LLMs. Knowledge distillation allows SLMs to build optimized AI models without requiring a lot of training data.
  
• Pruning
  Pruning refers to the process of eliminating less contributing parts of a large complex model (LLM) to simplify the architecture and reduce its size (SLM) without compromising on the performance. For example, pruning helps chatbots to respond to user prompts more quickly.
  
• Quantization
  Quantization helps in compressing a model by reducing the high numerical values to low-precision values that use less memory and speed up the calculation steps. For example, an 8-bit integer can be used to store values instead of a 32-bit float.

Examples of Small Language Models

Small language models (SLMs) are being employed largely across various domains since they were introduced in 2025. For instance, small businesses and startups are using SLMs in their customer support to provide faster response. Healthcare professionals are using SLMs to improve their clinical workflows, summarize medical records, and enhance patient communication. Some of the remarkable examples of small language models are as follows:

• Phi-2
  Phi 2 is a small language model developed by Microsoft. It consists of 2.7 billion parameters and is known for its common-sense reasoning and NLP processing. It is considered faster in performance than Llama-2 models.   
  
• Google BERT 
  BERT (Bidirectional Encoder Representations from Transformers) uses bidirectional pre-training to understand the context behind ambiguous texts in user queries. For example, Google uses BERT to rank search results on Google homepage. 
  
• DistilBERT
  DistilBERT is a compact version of BERT that is trained by knowledge distillation for model compression. DistilBERT is reportedly 60% faster than BERT while retaining 97% of its performance. It is extensively used in wearables, on-device applications in smartphones, and other IoT (Internet of Things) devices.
  
• Gemma 3 
  Gemma 3 is an open-source model built by Google. They are specifically designed to be utilized for research purposes. It offers advanced state-of-the-art AI functionality. Gemma models are highly useful when you have limited computational resources. They use the same technology as Google Gemini.
  
• LLaMA 3.1 8B 
  Llama (Large Language Model Architecture) is developed by Meta AI. It comes in various sizes, with Llama 3.1 8B becoming a powerful addition to the list of small language models (SLMs). It uses 8 billion parameters and has a faster response rate for NLP processing and sentiment analysis jobs.

Differences Between Small Language Models (SLMs) and Large Language Models (LLMs)

SLMs and LLMs each have their own use cases depending on the device type, app complexity, resources, memory, and so on. It is important to understand how SLMs and LLMs perform across different tasks, as it helps in selecting the right model for your specific requirements:

Model Architecture

• SLMs are compact models with parameters that range from a few million to billion. This reduces the model training time significantly.
• LLMs are relatively larger in size with millions, or even hundreds of billions of parameters. They are perfect for large-scale applications.
• SLMs use smaller pre-trained datasets while LLMs require huge datasets to train their models.

Efficiency & Memory

• SLMs are faster, use less memory and resources, and are more efficient in performance. These models are an apt choice for on-device applications.
• LLMs are complex models that need specialized GPUs for inference and consume a lot of computational memory to meet their resource needs.

Cost

• SLMs require fewer computational resources and are cheaper to train and deploy.
• LLMs are very expensive to train due to their complex model design and high resource demand. 

Deployment 

• SLMs handle specific queries well which makes them ideal for mobile applications and wearables, where smaller tasks need to be executed.
• LLMs have a larger network and are extremely flexible to run complex tasks. They work best when deployed on servers or cloud environments since they scale with training data over time. 
• SLMs can be used offline whereas LLMs usually require an internet connection to process any queries.

Combining LLMs and SLMs

Small language models (SLMs) are economical models, the more data is fed to train, the better a SLM will perform. However, training data is not easily available. For the most part, developers fine-tune these small language models using pre-trained data. LLMs are highly flexible and are equipped with large-scale datasets on servers, but their deployment and training costs are relatively high. SLMs and LLMs have their own advantages and disadvantages, but when combined, they can create a far more potent architecture. 

Microsoft researchers recently developed an AI model for hallucination detection that is more accurate (arXiv:2407.15441). This model uses a similar approach of pairing SLMs with LLMs to build a hybrid system that balances accuracy, cost and improved model performance. Earlier, training the massive datasets of LLMs required a significant amount of computing power. In the new hybrid model, SLM carries out the initial detection step and thereafter the LLM model explains the reported hallucinations.

Benefits of small language models

• SLMs are simple compact models that take less time to respond to queries. This makes them suitable for chatbot applications on mobile devices.
• They are generally used to perform specific tasks, hence using fewer parameters. 
• They are ideal for specialized small-scale projects as are easy to customize as required.
• SLMs consume a lot less memory and resources. They are therefore more preferred for research and small-scale applications.
• Small language models can be used completely offline, on local devices, and mobile applications making them more secure and accessible.
• SLMs are substantially cheaper than LLMs while maintaining the model accuracy.
• They are used for specific jobs and can be fine-tuned as required. This improves model performance as well.
• SLMs are very useful for IoT (Internet of Things) edge devices due to their ability to handle requests locally.
• SLMs are easier to maintain and are very cost-effective, making them a preferred choice for researchers and small businesses.

Limitations of small language models

• As discussed above, SLMs are designed to work on specific tasks. Although this reduces the model training time to a great extent, it hinders their ability to perform in a broader range of jobs.
• SLMs can not handle heavy user queries because they are trained to work on smaller datasets.
• They are not as scalable as LLMs, and expanding a model can impact both their speed and performance.
• Because of having lesser parameters, SLMs tend to have a limited knowledge base. For context, they are great for handling user-specific tasks but may struggle with complex tasks with sometimes providing repeated outputs.
• SLMs also provide less accurate responses compared to LLMs.
• They are not ideal to execute longer conversations as they use limited memory and tend to wipe earlier conversations from memory to save space.
• While SLMs have state-of-the-art AI functionality, they often fail at multi-step reasoning tasks and can give incomplete responses. 

Conclusion

In recent years, there has been a rise in the AI sector. Small language models (SLMs) have made it easier to build AI-powered products that are lightweight and budget-friendly. 

This makes it accessible for startups and educational institutions that don’t have heavy computational resources. In healthcare, wearables and IoT devices are already transforming patient care. Wearables and IoT devices have transformed healthcare and patient care. But this is just the beginning, with time, small language models (SLMs) are going to contribute to various industries with their efficient and adaptable solutions.