LLMs vs. SLMs : The Differences Between Large and Small Language Models
Generative AI, including ChatGPT, has quickly taken a deep place in our work and daily lives. Understanding how AI is built and how it should be used has now become an important challenge not only in the technology field but across a wide range of industries.In particular, the "size" of a language model goes beyond a simple technical difference and is a key criterion directly tied to many factors, including accuracy, efficiency, and resource consumption. SLMs (Small Language Models), which have fewer parameters and are fine-tuned on small datasets for specific purposes, and LLMs (Large Language Models), which are built on vast datasets and use large-scale resources, are representative examples.In this blog, based on official content from Splunk, we look at how these two types of language models are built and work, how they differ, and which situations each is suited for.✅What Is a Language Model?A language model is an AI computational model that can generate natural human language. This is no easy task. These models are trained as probabilistic machine learning models and attempt to mimic human intelligence by predicting the probability distribution of words suitable for generation in a sequence of phrases. In the scientific domain, the focus of language models is twofold:1. Understanding the nature of intelligence2. And implementing that nature in the form of meaningful, intelligent communication with real humansIn terms of demonstrating human intelligence, today's cutting-edge AI models in natural language processing (NLP) have not yet passed the Turing test. (A machine is considered to have passed the Turing test if it is impossible to tell whether a communication comes from a human or a computer.)What is especially interesting is that we are getting quite close to this benchmark. There are certainly the overhyped large language models (LLMs), and the less hyped but promising SLMs. (SLM can stand for "Small Language Model" or "Short Language Model.")✅ SLMs vs LLMs You are probably already familiar with large language models (LLMs) such as ChatGPT. This generative AI has attracted great interest across a wide range of fields, spanning academic, industrial, and consumer domains. This is mainly due to its ability to carry out relatively complex interactions in the form of conversational communication.Today, LLM tools are used as intelligent machine interfaces that draw on knowledge available on the internet. LLMs extract relevant information from the internet that was used in their training and present it to users in a concise, easy-to-understand form. This is an alternative to searching the internet for a query, reading through thousands of web pages, and arriving at a concise, conclusive answer.ChatGPT is the first consumer-facing example of an LLM, a technology previously limited to OpenAI's GPT and Google's BERT. Recent versions, including ChatGPT, have been trained and developed on programming scripts. Developers use ChatGPT to write complete program functions, provided they can properly specify requirements and constraints through text user prompts.Popular LLMs TodayMore than two years into the AI era, there are far more LLMs than ChatGPT. Here are some representative examples.· Claude LLM is an AI model developed by Anthropic, designed around the concept of Constitutional AI. Claude is available online, on mobile, and through an API, and certain versions can emphasize humor and subtle nuance. It can also be widely used for software programming, and Claude (the LLM) can use a computer just as a user does.· DeepSeek-R1 is an open-source tool that handles critical problem-solving through self-verification, chain-of-thought reasoning, and reflection.· Gemini is the name of Google's family of LLMs. If you use Google products such as Google Drive or Gmail, you have probably already seen Gemini rolled out.· GPT-4o is the latest version of the GPT series. A major upgrade from GPT-4, this version enables more natural "human" interactions, can interpret human emotions, and can ask questions about provided photos or screen shares.Other models worth noting include Meta's Llama, IBM Granite, Mistral, Microsoft's Orca, and Baidu's Ernie.In addition to content generation, the most common use case today, SLMs and LLMs are used for text classification tasks such as document and email classification, summarizing long documents and reports, sentiment analysis, anomaly detection, and coding.✅How Language Models WorkHow do language models work? SLMs and LLMs follow similar concepts of probabilistic machine learning throughout the entire process, including architecture design, training, data generation, and model evaluation. Let's review the main steps in generating natural language using an LLM. We will explain them at a high level, leaving out technical or detailed specifics.Step 1. General probabilistic machine learningThe goal of this step is to develop a mathematical model with parameters that can represent true predictions with the highest probability. In the context of language models, these predictions are the distribution of natural language data. The goal is to use the learned probability distribution of natural language to generate the most likely sequence of sentences based on available contextual knowledge, including the user's prompt query.Step 2. Architecture transformers and self-attentionTo learn the complex relationships between words and sequential sentences, modern language models use so-called Transformers-based deep learning architectures. Transformers convert text into numerical representations weighed according to importance and use them when predicting sequences.Step 3. Pretraining and fine tuningLanguage models are deeply fine-tuned and engineered for specific task domains. This process involves adjusting model parameters in the following ways:1. Train the model with domain-specific knowledge.2. Initialize model parameters based on pretrained data.3. Monitor model performance.4. Further tune model hyperparameters.Another important use of language model engineering is removing bias toward unwanted language outcomes (for example, hate speech and discrimination).Step 4. Continuous evaluation of the modelEvaluating LLMs and SLMs involves both qualitative and quantitative assessment. This includes the following:· The perplexity score measures the model's ability to predict word sequences. The lower the score, the better the model performs.· The BLEU score evaluates text generation by comparing model output with human-written content.· Human evaluation involves human experts assessing whether the model's responses meet standards of relevance and accuracy.· Bias and fairness testing identifies bias present in the model's responses.✅Differences Between LLMs and SLMsNow let's look at the differences between SLM and LLM technologies. The important point here is that the difference is not simply about how much data a model has been trained on, that is, whether the dataset is large or small, but is a more complex matter.Size and Model ComplexityPerhaps the most noticeable difference between SLMs and LLMs is model size.· LLMs such as ChatGPT (GPT-4) are reported to contain 1.76 trillion parameters.· Open-source SLMs such as Mistral 7B can contain 7.3 billion model parameters.The difference stems from the training process of the model architecture. ChatGPT uses a self-attention mechanism in an encoder-decoder model scheme, while Mistral 7B uses a sliding window attention mechanism that enables efficient training in a decoder-only model.Contextual Understanding and Domain SpecificitySLMs are trained on data from specific domains. They may lack holistic contextual information across multiple knowledge areas, but they are likely to excel in their chosen area.LLMs, on the other hand, aim to mimic human intelligence at a broader level. They are trained on larger data sources and are expected to perform relatively well across all domains compared to domain-specific SLMs. In other words, LLMs are more versatile and can be adapted, improved, and engineered for better downstream tasks such as programming.Resource ConsumptionTraining an LLM is a resource-intensive process that requires GPU compute resources in large-scale clouds. Training ChatGPT from scratch requires thousands of GPUs.By contrast, the Mistral 7B SLM can run on a local machine with a suitable GPU, and training a 7B-parameter model still requires several hours of compute time across multiple GPUs.BiasLLMs tend to be biased. This is because they are not properly fine-tuned and are trained on raw data that is publicly accessible and published on the internet. Because of where this training data comes from, it may:· Underrepresent or misrepresent certain groups or ideas.· Be mislabeled.Further complexity arises from the fact that language itself introduces its own biases based on various factors such as dialect, geographic location, and grammar rules. Another common problem is that the model architecture itself can unintentionally apply bias, which may go unnoticed.With SLMs, the risk of bias is lower. Because SLMs are trained on relatively small, domain-specific datasets, their risk of bias is naturally lower than that of LLMs.Inference SpeedThe small model size of SLMs means that users can run the model on a local computer and still generate data within an acceptable amount of time. LLMs require multiple parallel processing units to generate data. Depending on the number of concurrent users accessing an LLM, model inference speed tends to slow down.Data SetsAs we have seen, the difference between SLMs and LLMs goes far beyond the data they are trained on. However, there is some nuance to the question of "what data was it trained on?"· If a smaller model is trained on the same data as an LLM but optimized for domain specificity, it can still be considered an SLM.· If a smaller model takes a general-purpose approach, it is fair to consider it a scaled-down LLM rather than an SLM.✅So, Is an LLM the Right Choice for Every Field?The answer to this question depends entirely on the language model's use case and the resources available. In a business environment, LLMs are more likely to be suitable as chatbots for call centers and customer support teams. However, for function-specific use cases or when you want to make a model resemble yourself, SLMs are likely to perform better.✅Choosing a Language Model for Different Use CasesThe effectiveness of a language model depends on how it is used. LLMs are suited to general applications that require versatility, while SLMs are models that excel in specific areas requiring efficiency and precision. Take the medical, legal, and financial fields as examples. Each application in these fields requires highly specialized and proprietary knowledge. By training an SLM in-house on this knowledge and fine-tuning it for internal use, it can be used as an intelligent agent for domain-specific use cases in industries with strict regulation and specialization.In summary, LLMs are general-purpose large models that can handle a wide variety of tasks, delivering powerful performance but requiring significant resources. SLMs, on the other hand, are efficient models optimized for specific purposes, enabling fast inference with fewer resources and carrying a relatively lower risk of bias. Ultimately, which language model is right depends on whether you place more weight on versatility or precision, how much infrastructure the organization has available, and what real-world work environment you intend to apply it to.Splunk is a unified data platform that helps apply these AI models effectively to security and operations environments. If you are considering AI-driven security operations and data analytics frameworks, we invite you to work with CloudNetworks, an official Splunk partner.▶ Splunk : Learn MoreSplunk is an AI-powered unified security and observability platform that improves data accessibility, delivers data-driven insights, and eliminates data silos. With an industry-leading data platform, advanced analytics, and automated investigation and response, it helps protect the business and strengthen security operations, and with observability across the entire enterprise, it helps identify the business impact of every performance issue and resolve it faster. Drawing on its expertise in big data, CloudNetworks operates a dedicated team specializing in the implementation and technical support of the Splunk platform. For inquiries about adopting and using Splunk, please contact CloudNetworks, an official partner.
August 12, 2025
