AI Explained: What’s a Small Language Model and How Can Business Use It?

Artificial intelligence (AI) is now a household word, thanks to the popularity of large language models like ChatGPT. These large models are trained on the whole internet and often have hundreds of billions of parameters — settings inside the model that help it guess what word comes next in a sequence. The more parameters, the more sophisticated the model.

A small language model (SLM) is a scaled-down version of a large-language model (LLM). It doesn’t have as many parameters, but users may not need the extra power depending on the task at hand. As an analogy, people don’t need a supercomputer to do basic word processing. They just need a regular PC.

But while SLMs are smaller in size, they can still be powerful. In many cases, per IMB data, they are faster, cheaper and offer more control — key for companies looking to deploy powerful AI into their operations without breaking the bank.

Language models can have even trillions of parameters, such as OpenAI’s GPT-4. In contrast, small language models typically have between a few million and a few billion parameters.

According to a January 2025 paper by Amazon researchers, SLMs in the range of 1 billion to 8 billion parameters performed just as well or even outperformed large models.

For example, SLMs can outperform LLMs in certain domains because they are trained on specific industries. But LLMs do better in general knowledge.

SLMs also require far less computing power. They can be deployed on PCs, mobile devices or in company servers instead of the cloud. This makes them faster, cheaper and easier to fine-tune for specific business needs.

See also: AI Explained: What Is a Large Language Model and Why Should Businesses Care?

Small language models are quickly becoming popular among businesses that want the benefits of AI without the steep cost and complexity of LLMs.

The following are advantages of SLMs over LLMs:

• Cost efficiency: Large language models are expensive to run, especially at scale. Small models, on the other hand, can operate on personal computers or devices like smartphones and IoT sensors. Using SLMs along with LLMs for more critical and complex tasks can keep AI costs down.
• Data privacy and control: When using an LLM, which means sending data to the cloud, there is always a privacy concern. Small models can be deployed entirely on premises, meaning companies retain full control over their data and workflows. This is especially important in regulated industries like finance and healthcare.
• Speed and responsiveness: Because they are lighter, small models deliver responses more quickly and can operate with less latency. This is particularly valuable in real-time settings such as customer service chatbots.

“Lower data and training requirements for SLMs can translate to fast turnaround times and expedited ROI,” according to Intel.

Disadvantages of SLMs:

• Bias learned from LLMs: Since smaller models are truncated versions of large models, bias in the parent model can be passed on.
• Lower performance on complex tasks: Since they’re not as robust as the large models, they might be less proficient in complicated tasks that require knowledge in a comprehensive range of topics.
• Not great at general tasks: SLMs tend to be more specialized so they are not as good as LLMs in general tasks.

As for hallucinations, since SLMs are built on smaller, more focused datasets, they’re well suited for use in applications by industry. As such, “training on a dataset that’s built for a specific industry, field or company helps SLMs develop a deep and nuanced understanding that can lower the risk of erroneous outputs,” according to Intel.

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The most popular SLMs in the last two years “by far” have been those in Meta’s open-source Llama 2 and 3 families, according to the Amazon research paper.

Llama 3 comes in 8 billion, 70 billion and 405 billion parameter models while Llama 2 has 7 billion, 13 billion, 34 billion and 70 billion versions. The SLMs would be the 8 billion model from Llama 3 and the 7 and 13 billion model from Llama 2. (Meta just released Llama 4 this week.)

New entrant DeepSeek R1-1.5B offers 1.5 billion parameters as the first reasoning model from the Chinese AI startup.

Other SLMs include Google’s Gemini Nano (1.8 billion and 3.25 billion parameter versions) and its Gemma family of open-source models. Last month, Google unveiled Gemma 3, which comes in 1, 4, 12 billion and 27 billion parameters.

Last October, French AI startup and OpenAI rival Mistral unveiled a new family of SLMs: Ministraux, at 3 and 8 billion parameters. Its first SLM is Mistral 7B, which has 7 billion parameters.

Another notable SLM is Phi-2 from Microsoft. Despite only being 2.7 billion parameters, Phi-2 performs well in math, code, and reasoning tasks. It was trained using a carefully curated dataset, proving that smarter data selection can make even very small models capable.

Code repository Hugging Face has hundreds of open-source SLMs available for companies to use.

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