LLMs vs. SLMs: The Differences in Large & Small Language Models

Language models large and small are making news headlines practically every day — if you work online or in tech or pay attention to the news, then you certainly have not missed this massive, global tech development that has the potential to truly change how we work.

So, let’s ask an important question: what makes a language model large or small? The quick, basic summary is this:

• Small language models (SLMs) have fewer parameters and are fine-tuned on a subset of data for a specific use case.
• Large language models (LLMs) are trained on large-scale datasets and usually require large-scale cloud resources.

Of course, there’s a lot more to it than that. Stick around for a deep-dive into the world of language models and the difference between small and large models.

What are language models?

Language models are AI computational models that can generate natural human language. That’s no easy feat.

These models are trained as probabilistic machine learning models — predicting a probability distribution of words suitable for generation in a phrase sequence, attempting to emulate human intelligence. The focus of language models in the scientific domain has been twofold:

1. To understand the essence of intelligence.
2. And to embody that essence in the form of meaningful intelligent communications with real humans.

In terms of exhibiting human intelligence, today’s bleeding edge AI models in natural language processing (NLP) have not quite passed the Turing Test. (A machine passes the Turing Test if it is impossible to discern whether the communication is originating from a human source or a computer.)

What is particularly interesting is that we are getting pretty close to this marker: certainly, with the hyped Large Language Models (LLMs) and the promising, though less hyped SLMs. (SLM can stand for both Small Language Model or Short Language Model.)

Small language models vs. large language models

No doubt you’re already familiar with LLMs such as ChatGPT. These generative AIs are hugely interesting across academic, industrial, and consumer segments. That’s primarily due to their ability to perform relatively complex interactions in the form of speech communication.

Currently, LLM tools are being used as an intelligent machine interface to knowledge available on the internet. LLMs distill relevant information on the Internet, which has been used to train it, and provide concise and consumable knowledge to the user. This is an alternative to searching a query on the Internet, reading through thousands of Web pages and coming up with a concise and conclusive answer.

ChatGPT is the first consumer-facing use case of LLMs, which previously were limited to OpenAI’s GPT and Google’s BERT technology.

Recent iterations, including but not limited to ChatGPT, have been trained and engineered on programming scripts. Developers use ChatGPT to write complete program functions – assuming they can specify the requirements and limitations via the text user prompt adequately.

Popular LLMs today

Now that we’re two-plus years into the AI era, there are many more LLMs that ChatGPT. Here are a few common ones:

• Claude LLM, by Anthropic, centers on the idea of constitutional AI. Claude is available online, on mobile, and via API. Versions of Claude can focus on humor and nuance, and Claude can also be used widely for software programming. Claude — the LLM — can even use a computer just like you can.
• DeepSeek-R1 is an open-source tool that handle critical problem solving, particularly through self-verification, chain-of-thought reasoning, and reflection.
• Gemini is Google’s name for their LLM family. If you’re a user of Google products like Google Drive and Gmail, you’ve probably seen Gemini rolled out.
• GPT-4o is the latest in the long-line of GPTs. This significant upgrade from 4 makes for more natural “human” interaction, may interpret human emotions, and can ask questions about photos or screenshares that are provided to it.

Others you may hear about include Llama from Meta, IBM Granite, Mistral, Microsoft’s Orca, and Ernie from Baidu.

In addition to generating content — by far the most common use case today — SLMs and LLMs are used for text classification tasks like categorizing documents and emails, summarizing lengthy documents and reports, sentiment analysis, anomaly detection, coding, and more.

(Concerned about security in your LLMs? Learn how to defend against the OWASP Top 10 for LLMs and how AI can support Blue Team security operations.)

See how Splunk is using AI for the specific domains of cybersecurity and observability. Learn more about Splunk AI.

How language models work

So how do language models work? Both SLM and LLM follow similar concepts of probabilistic machine learning for their architectural design, training, data generation, and model evaluation.

Let’s review the key steps in generating natural language using LLMs — we will keep this high-level and not technical or specific.

Step 1. General probabilistic machine learning

Here, the idea is to develop a mathematical model with parameters that can represent true predictions with the highest probability.

In the context of a language model, these predictions are the distribution of natural language data. The goal is to use the learned probability distribution of natural language for generating a sequence of phrases that are most likely to occur based on the available contextual knowledge, which includes user prompt queries.

Step 2. Architecture transformers and self-attention

To learn the complex relationships between words and sequential phrases, modern language models rely on the so-called Transformers-based deep learning architectures. Transformers convert text into numerical representations weighed in terms of importance when making sequence predictions.

Step 3. Pretraining and fine tuning

Language models are heavily fine-tuned and engineered on specific task domains. The process involves adjusting model parameters by:

1. Training the model on domain-specific knowledge.
2. Initializing model parameters based on pretrained data.
3. Monitoring model performance.
4. Further turning model hyperparameters.

Another important use case of engineering language models is to eliminate bias against unwanted language outcomes such as hate speech and discrimination.

Step 4. Evaluating the model continuously

Evaluating both LLMs and SLMs involves a number of qualitative and quantitative assessments. These include:

• Perplexity score measures how well the model predicts a sequence of words. The lower the score, the better the model's performance.
• BLUE score evaluates text generation by comparing model outputs to human-written content.
• Human evaluation involves human experts assessing the model's response for relevance and accuracy.
• Bias and fairness testing which identifies bias in the model's response.

The differences between LLMs & SLMs

Now, let’s discuss what differentiates SLM and LLM technologies. Importantly, the difference here is not simply about how much data the model was trained on — large or small datasets — it’s more complex than that.

Size and model complexity

Perhaps the most visible difference between the SLM and LLM is the model size.

• LLMs such as ChatGPT (GPT-4) purportedly contain 1.76 Trillion parameters.
• Open source SLM such as Mistral 7B can contain 7.3 billion model parameters.

The difference comes down to the training process in the model architecture. ChatGPT uses a self-attention mechanism in an encoder-decoder model scheme, whereas Mistral 7B uses sliding window attention that allows for efficient training in a decoder-only model.

Contextual understanding and domain specificity

SLMs are trained on data from specific domains. They may lack holistic contextual information from all multiple knowledge domains but are likely to excel in their chosen domain.

The goal of an LLM, on the other hand, is to emulate human intelligence on a wider level. It is trained on larger data sources and expected to perform well on all domains relatively well as compared to a domain specific SLM.

That means LLMs are also more versatile and can be adapted, improved, and engineered for better downstream tasks such as programming.

Resource consumption

Training an LLM is a resource intensive process and requires GPU compute resources in the cloud at scale. Training ChatGPT from scratch requires several thousand GPUs for training.

In contrast, the Mistral 7B SLM can be run on your local machines with a decent GPU — training a 7B parameter model still requires several computing hours across multiple GPUs.

Bias

LLMs tend to be biased. That’s because they are not adequately fine-tuned and because they train on raw data that’s openly accessible and published on the Internet. Because of the source of that training data, it is likely that the training data may…

• Under-represent or misrepresent certain groups or ideas
• Be labeled erroneously.

Further complexity emerges elsewhere: language itself introduces its own bias, depending on a variety of factors such as dialect, geographic location, and grammar rules. Another common issue is that the model architecture itself can inadvertently enforce a bias, which may go unnoticed.

The risk of bias is smaller with SLMs. Since the SLM trains on relatively smaller domain-specific data sets, the risk of bias is naturally lower when compared to LLMs.

Inference speed

The smaller model size of the SLM means that users can run the model on their local machines and still generate data within acceptable time.

An LLM requires multiple parallel processing units to generate data. Depending on the number of concurrent users accessing an LLM, the model inference tends to slow down.

Data sets

As we’ve seen, the difference between SLMs and LLMs goes far beyond the data on which they are trained. But there is some nuance in the “what data was it trained on” conversation:

• If a smaller model is trained on the same data as an LLM, but is optimized for domain specificity, then it might still be considered an SLM.
• If that smaller model has a general-purpose approach, then it wouldn't be wrong to consider it a scaled-down LLM instead of an SLM.

So, is LLM the right choice for everything?

The answer to this question entirely depends on the use case of your language models and the resources available to you. In a business context, it is likely that an LLM may be better suited as a chat agent for your call centers and customer support teams.

However, in most function-specific use cases or areas where you’re building a model to sound like yourself, an SLM is likely to excel.

Choosing language models for varied use cases

When it comes to language models, their effectiveness depends on how they're used. LLMs are great for general purpose applications where you want versatility while SLMs are ideal for when you want a model that excels in domains that require efficiency and precision.

Consider the use cases in medical, legal, and financial domains. Each application here requires highly specialized and proprietary knowledge. Training an SLM in-house with this knowledge and fine-tuned for internal use can serve as an intelligent agent for domain-specific use cases in highly regulated and specialized industries.