What Are SLMs? Small Language Models, Explained

Large language models (LLMs) are AI models with billions of parameters, trained on vast amounts of data. These models are typically flexible and generalized. The volume and distribution of training data determines what kind of knowledge a large language model can demonstrate.

By training these large models on a variety of information from all knowledge domains, these models can perform sufficiently well on all tasks. This remarkable ability makes LLMs great tools for:

• Common intelligence-based conversational tasks
• Programming assistance

But what about the specialized tasks in highly regulated industries such as healthcare, legal and finance?

Specialized use cases don’t require generalized knowledge

For specialized use cases, you don’t need a large, generalized knowledge base—instead, you need a specialized and accurate knowledge base. In an ideal world, such a specialized knowledge base (to serve as training data for AI models) could be:

• Just large enough
• Widely available
• Not subject to privacy or security concerns

In that case, we could have billions of data points and resources to train a large language model that has hundreds of billions of parameters.

Of course, I will burst your bubble: this scenario is not the case in the real world. We all know that domain-specific data may be unavailable for a variety of reasons, like its proprietary nature, privacy regulations, security concerns, or limitations surrounding data generation.

The solution? You build small AI models based on domain-specific information and tune it to perform well on the desired downstream tasks — such as personal chatbot for financial insights and support or patient diagnostics support for doctors.

What are SLMs?

So, what are SLMs? Small language models are AI models that are relatively small in size compared to LLMs. Done right, SLMs can deliver:

• Unprecedented accuracy
• Reduced operating costs (OpEx) and environmental footprint

While general-purpose LLMs will maintain a place in most organizations, there’s a strong case that strategic AI investments and the rise of domain-specific SLMs will lead businesses to tangible returns by year’s end. For example, you may build a domain-specific model that:

• Understands network configuration.
• Can assist with automation and assurance.

Another reason to pivot to SLMs for many projects? Data limits. Indeed, we could run out of high-quality, publicly available training data by 2026. In this way, SLMs will better serve organizations looking to employ AI for specific applications — similar to a subject matter expert. As Mark Patterson, chief strategy officer at Cisco, explains in our 2025 Predictions:

Characteristics and attributes of SLMs

The following key attributes make SLMs different from large language models:

Size

An SLM may have a few billion parameters. In comparison, an LLM that may have a few trillion model parameters. This comparison is relative:

• LLMs are increasing in size as computational power becomes widely available at lower cost.
• And SLMs are not exactly designed to run on your personal devices with small GPUs.

As of now, AI language models with a few billion parameters are still considered to be SLM, which are not yet suitable for low-power IoT and edge computing devices. Still, lightweight versions for smartphones (typically offline and on-device inference) is possible.

Want some examples? Take a look at this list of popular open-source SLMs here.

Knowledge

SLMs demonstrate specialized and domain-specific knowledge, as compared to the generalized and universal knowledge base of an LLM.

This largely comes down to the data used to train these models. Unlike the large models, SLMs models may be:

1. Pretrained on domain-specific datasets.
2. Then fine-tined on specialized datasets that may be proprietary or unique to a knowledge domain.

Specialized downstream tasks

An SLM may be tuned and adapted to perform specialized conversational tasks. For example:

• A programming support agent for specific programming languages, libraries, and use cases.
• A vision model that can interact with radiologists and extract useful knowledge from medical imagery.

Architecture

SLMs may be derived from similar model architectures as the LLM. The training regime and learning algorithms may also be similar. The model architecture may only vary in the network size and scale in different architecture blocks.

For example, an SLM may have a transformer block with fewer layers and full attention, whereas an LLM may have sparse connections in a larger transformer module with longer context length.

(Learn about the transformer model in GenAI.)

Inference speed and resource Consumption

LLMs have a larger architecture and can take more time and computing resources at inference (that is, responding to a user prompt). While the converse is true for SLMs — less time, less resources to response — LLMs are typically optimized to:

• Respond faster (as a tradeoff to inference accuracy)
• Run on high-performance computing systems that allow for faster inference

In contrast, SLMs are typically used on standard AI machines with limited GPUs.

Bias and the learning gap

Both SLMs and LLMs are prone to bias that is subject to the available training data. It may be possible that the training data is heavily skewed toward an outcome or attribute.

For example, disease-specific information relevant to the most affected demographics and region may be used to train an AI model. The resulting knowledge may not apply to other demographics or regions.

If you look at how an AI model works in simple words, it makes generalized assumptions about behaviors based on the information used to train it. So, in this case, due to the data bias and limited knowledge of healthcare data on all populations, the outcome of the user prompt is likely to be skewed in the direction of available data points.

(Related reading: AI frameworks, AI ethics, and AI governance can all help your models avoid bias.)

The future of AI is specific, not generalized

We have already observed how well large language models perform on generalized conversational tasks. But my belief is that the future of conversational intelligence is heading toward specialized use cases with SLMs.

Yes, LLMs have been great at hyping up the AI and language intelligence trends in the consumer market. But the most meaningful use cases for language models will require domain-specific datasets, specialized knowledge, as well as SLM architectures and training algorithms designed to optimize specific downstream tasks.