In the rapidly evolving field of artificial intelligence, Small Language Models (SLMs) have emerged as a significant alternative to their larger counterparts. Giants like GPT 5, Claude 4, Gemini 2.5, and Grok 4 dominate headlines with their billions or even trillions of parameters. Yet, SLMs are quietly revolutionizing the deployment of AI in practical and resource-constrained environments.
Simply put, while gigantic models generally outperform smaller ones, they are not always the best choice. In many cases, a smaller model optimized for efficiency rather than raw capability is exactly what we need. This article will explain the importance of smaller models and why you should consider using them. It will also demonstrate how to run one of these models locally.
What Are Small Language Models
Small Language Models are neural network-based models designed for natural language processing tasks, typically containing anywhere from a few million to a few billion parameters. The term "small" is relative and has shifted as model sizes have grown. What we consider small today might have been viewed as enormous only a few years ago.
Generally, models with fewer than 10 billion parameters are now considered small, although this threshold is subject to change as computational capabilities continue to advance. The terminology itself reflects the dramatic scaling that has occurred in language modeling. When BERT was released in 2018 with 340 million parameters in its largest variant, it was considered substantial. Today, however, even a model with 2 billion parameters is regarded as remarkably small compared to the trillions of parameters found in the largest and most powerful models.
Another reason these models are called Small Language Models is that they are essentially scaled-down versions of their larger counterparts. While there are certain architectural differences, on a broader level, they often mirror the architecture of larger models.
Like Large Language Models, they are built on transformer-based architectures and use many of the same fundamental building blocks. Their training process is also notably similar to that of larger models. However, this does not mean there are no distinctions between small and large models.
Small Language Models are deliberately limited in size by using techniques such as fewer layers, smaller hidden dimensions, and more extensive parameter sharing. These design choices make them inherently different from larger models and well-suited for specific use cases.
What Is the Appeal of Small Language Models
Organizations and developers are increasingly recognizing that bigger is not always better. In many cases, the right tool for the job prioritizes efficiency over maximum capability.
Cost efficiency is perhaps the most important factor driving the adoption of Small Language Models. Running Large Language Models requires significant computational resources, which directly translates into high operational costs. Cloud providers charge based on compute time and memory usage, and running a model with hundreds of billions of parameters can become prohibitively expensive.
Smaller models, by contrast, often deliver acceptable performance at a fraction of the cost. This makes AI more accessible to smaller organizations and enables large-scale deployment for larger ones.
Deployment flexibility represents another key advantage of Small Language Models. Unlike their larger counterparts, these models can typically run on edge devices, embedded systems, and other resource-constrained environments. Being small enough to fit on local devices allows them to operate offline, without a constant cloud connection. At first, this might not seem significant, but in practice, there are many situations where Internet access is either unavailable or poses a security risk.
Latency requirements also favor smaller, locally deployed models over larger cloud-hosted models. In real-time applications such as autocomplete, conversational AI, and autonomous vehicles, response time matters as much as accuracy. A smaller model that responds in milliseconds can deliver a better user experience than a larger model that takes seconds, even if the larger model's output is slightly more accurate.
Privacy and data sovereignty concerns are another key factor driving SLM adoption. Organizations that handle sensitive data often prefer to run models on-premises or on controlled infrastructure. Small Language Models make this feasible without requiring large capital investments in specialized hardware. This capability is especially valued by healthcare providers, financial institutions, and government agencies.
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What Are the Challenges of Small Language Models
The main disadvantage of Small Language Models lies in their limited capabilities. In the world of AI, generalization typically decreases as model size diminishes. Large models can often handle novel situations by drawing on vast amounts of encoded knowledge. Smaller models, however, may struggle when faced with scenarios outside their training data. This limitation is especially noticeable in open-ended tasks that require creativity and broad knowledge.
Simply put, complex reasoning, multi-step problem solving, and nuanced understanding of context often challenge smaller models. They frequently struggle with tasks that require extensive world knowledge. Their limited number of parameters constrains how much information can be effectively encoded during training.
These limitations are most evident in the quality of the text generated. Many smaller models produce responses that are less fluent, coherent, or contextually appropriate than those of larger models. They often struggle to maintain consistency across longer passages, track complex entity relationships, or capture subtle linguistic nuances.
Finally, although these models are easier to run due to their smaller size, achieving optimal performance often requires careful optimization, quantization, and hardware-specific tuning. Organizations may need specialized knowledge to realize the efficiency gains that make smaller models appealing in the first place.
What Are the Real-World Use Cases of Small Language Models
The transition from theoretical potential to practical deployment of Small Language Models is already well underway across industries. Organizations worldwide are discovering that SLMs provide not only cost savings but also new possibilities. They enable entire categories of applications that were previously impossible with cloud-dependent large models. From edge computing in manufacturing to privacy-preserving healthcare applications, SLMs are reshaping how businesses approach AI deployment.
What Are Microsoft’s Phi Models
Microsoft’s Phi family of Small Language Models (SLMs) is designed to be scalable and focused on specific domains. One of the latest models, Phi-3, powers ITC’s Krishi Mitra app, which provides agricultural support to farmers in India. This app can be used offline and is intended to serve 300,000 farmers during its pilot phase. The long-term goal is to eventually reach 10 million users. So far, over 100,000 users have engaged with the Krishi Mitra platform.
A newer model, Phi-4, demonstrates strong mathematical reasoning capabilities. It is being used to advance education platforms and tutoring systems. Phi-4 is designed to be small enough to run on devices with limited computing power, such as smartphones. Despite its compact size, it can still handle complex reasoning tasks effectively.
In the financial sector, Phi models have potential applications such as fraud detection. Financial institutions already rely on various machine learning and AI models to identify and prevent fraudulent activities. The advanced reasoning abilities of models like Phi-4 make them well-suited for these tasks. However, widespread deployment of Phi models for this specific purpose has not yet been extensively documented.
What Is Apple’s OpenELM
Apple's OpenELM (Open-source Efficient Language Models) represents the company's move toward a privacy-focused, on-device AI approach. This family of open-source language models is designed to run locally on devices like iPhones, iPads, and Macs. They do not rely on cloud servers for processing. Running models on-device inherently enhances user privacy by keeping data localized.
A key architectural innovation in OpenELM is its use of layer-wise scaling. This approach enables more efficient allocation of parameters within the model. As a result, it achieves enhanced accuracy even with fewer pre-training resources compared to some other models. Apple has released several OpenELM models with different parameter sizes. They have also provided the complete framework for training and evaluation to encourage open research.
Small Language Models enhance personalized learning by providing grammar correction, coding feedback, and language tutoring, all offline. Governments are using them for secure citizen services, defense intelligence, and emergency response systems. In these cases, low latency and strong data security are critical.
What Is Google's Gemma
Across the industry, developers and businesses are embracing Google's Gemma models for their unique combination of high performance and lightweight architecture. This makes them ideal for a wide range of on-device and cloud-based applications.
Companies are leveraging Gemma's instruction-tuned variants for conversational AI, such as chatbots. They are also fine-tuning the pretrained models on their own data for specialized tasks like summarization, retrieval-augmented generation (RAG), and producing structured outputs, such as JSON.
Because the models are open-source and compatible with major frameworks like PyTorch, JAX, and TensorFlow, they can be easily integrated into existing workflows. They work seamlessly on platforms such as Google Cloud's Vertex AI and Google Kubernetes Engine (GKE). This accessibility allows organizations of all sizes to build and deploy customized, privacy-centric AI solutions with responsible commercial usage.
Startups can prototype new ideas, while large enterprises can enhance digital marketing and data analysis capabilities. The newest Gemma models, known as the 3n series, can also analyze images.
How Does the Gemma3n Example Work
Using the Gemma3n model is straightforward, as it is one of the models available on Hugging Face. Let's demonstrate how to analyze an image with this model. The first step is to import all the libraries and tools we will need:
import torch
from transformers import pipeline
from PIL import Image
After importing PyTorch, the Transformers library, and PIL, we are ready to prepare the image for this demonstration. The Transformers library allows us to interact with Hugging Face models easily, while PIL makes it simple to load and process images. I will load an image from my PC to use in this example:
# Prepare an example image
image_path = r"C:\Users\Korisnik\Downloads\guitarist.png"
sample_image = Image.open(image_path).convert("RGB")
This is what the image looks like:
The next step is to construct a pipeline using the Transformers library:
# Create a pipeline that uses the Gemma3n model
pipe = pipeline(
task="image-text-to-text",
model="google/gemma-3n-e4b-it",
dtype=torch.bfloat16,
device=0
)
In the code above, we initialize a pipeline object from the Hugging Face Transformers library. This is a high-level, easy-to-use API that simplifies the process of using pre-trained models for inference. It handles preprocessing, model inference, and post-processing behind the scenes. As a result, we can perform specific tasks with a complex model using only a few lines of code.
In this case, we are defining that we are working on an "image-text-to-text" task. This type of task involves a multimodal model, also known as a Vision Language Model (VLM). It allows us to:
- Provide the model with an image and a text prompt as input.
- Receive a generated text string as output.
This type of task is more advanced than the simpler "image-to-text" task. It allows us not only to generate a caption for an image, but also to ask specific questions about it. We can prompt the model to identify and locate objects, or to describe particular aspects of an image.
The model argument defines which model we want to use. In this case, we will set it to the Gemma3n model. More specifically, we are using the gemma-3n-e4b-it model. Let's break down its name:
- Gemma3n - the family of models we are using.
- E4b - indicates the "effective" size of the model, meaning it has a memory footprint comparable to a traditional 4-billion-parameter model.
- IT - stands for "instruction tuned", showing that the base model has been further trained to better follow user commands and instructions.
The next argument, dtype, sets the numerical precision for the model's weights and calculations. We are using a special 16-bit format, which significantly speeds up computations. This is achieved without sacrificing the accuracy of our model.
Finally, because I have a GPU available, I am specifying in the last argument that the model should run on my GPU.
Next, we will define how the pipeline we just created will be used. Gemma-3n expects prompts in a particular format to deliver the best possible responses. Here is how we define our prompt:
# Prepare the prompt for the model
messages = [
{
"role": "user",
"content": [
{"type": "image"}, # this will be filled from the images argument
{"type": "text", "text": "What musical instrument is the person playing?"}
],
}
]
As shown in the code above, we need to specify two elements in this particular format:
- role
- content
By setting the role to user, we indicate that the following content is our input or question. Another common role is assistant, which represents a previous response from the model in an ongoing conversation.
The content is a list containing the different parts of your multimodal prompt. It includes the image we want to use and the text of the prompt itself. In this case, we will provide the model with the image we prepared earlier. Next, we will ask the model to identify which musical instrument the person in the image is playing.
Finally, to process the prompt with the model, we will call the pipeline object we defined earlier:
# Process the prompt with the model
out = pipe(
text=messages,
images=[sample_image],
return_full_text=False,
)
The code above triggers the inference process. As can be seen, we provide the specially formatted prompt as the value for the text argument. The images argument refers to the image we prepared earlier. The third argument, return_full_text, is set to False. This ensures that the model does not include our prompt at the beginning of its answer.
Once the answer has been generated, the final step is to display it. We can do this using the following code:
print(out[0]["generated_text"])The pipeline we ran earlier always returns a list of dictionaries, because it supports processing prompts in batches. Since we only sent one prompt, we are only interested in the first element of the returned dictionary, which is why we use out[0].
Each dictionary returned by the pipeline, including the one we just extracted with out[0], contains a key called generated_text. The value linked to this key is the model's textual response to our prompt. In this case, the response is going to be:
The person in the image is playing an electric guitar.
As can be seen, the model correctly identified that the person in the image is playing an electric guitar.
In this article, we discussed Small Language Models. We explained what these models are and why they are becoming more popular than ever. We also highlighted some of the challenges involved in implementing them. In addition, we looked at a few real-world use cases. Finally, we demonstrated how you can use one of the best models currently available, Gemma3n, to analyze an image and return an answer to the user.
