Small Language Models (SLMs): Bringing AI from the Cloud to Your Device

When OpenAI launched ChatGPT in late 2022, it became a turning point for AI. ChatGPT, which was built on a Large Language Model (LLM), provided most people their first real experience with AI that could chat, summarize, answer questions, and even write content. This breakthrough has inspired developers to create new applications using LLMs—making AI tools that are easier for anyone to use through natural language interactions, rather than requiring specifically formatted computer commands and broader innovation in conversational AI applications.

But most of these features don’t run directly on the phone. The user input goes to cloud servers where the AI model does the actual work. While it feels fast, it raises concerns about privacy and depends heavily on the internet. If people are offline, many features slow down or stop working (e.g.: editing in google photos app). Now, there’s growing demand for AI that works faster, keeps data private, and runs directly on your device. These concerns paved path to the rise of Small Language Models (SLMs)—compact, efficient AI models that work directly on the device without the need for cloud. In this blog, we’ll explore what SLMs are, how they’re built, and why they might just be the future of everyday AI.

What are Small Language Models (SLMs)?

Small Language Models (SLMs) can be considered as the compact versions of Large Language Models (LLMs). The word small refers to the number of learnable parameters in the language model. While LLMs can have hundreds of billions or even trillions of parameters, SLMs typically will have 1 million to 10 billion parameters. Hence, these SLMs can run on low-power devices like smartphones, tablets, or edge devices without needing cloud support and can still handle tasks like writing text, summarizing, translating, or answering questions.

How Are Small Language Models Made?

Converting an LLM into an SLM is not about removing some parts of the large model. The following are some of the smart techniques to reduce the model size while keeping it useful.

Knowledge Distillation: A smaller model (student) learns from a larger and more powerful model (teacher) using a specific arrangement. Instead of learning everything from the teacher model, the student model captures the essential patterns and behavior, leading to a lighter model.

Pruning: In LLMs, certain neurons or connections of the model are rarely activated or non-critical. These parts are removed away, reducing the overall size of the model without hurting performance too much. Sometimes, layer-wise pruning is done to reduce complexity.

Quantization: In LLMs the data type of the weight floating point values is converted into integer making the model to have less space and making them faster to execute. These models are more suitable for running on smartphones, tablets, or edge devices—without a big drop in accuracy.

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Focused Training: This is a method of creating the SLM from scratch. Instead of trying to master everything like LLMs, SLMs can specialize domain specific data just by getting trained on domain-specific data.

These techniques make the SLMs to run directly on low resource devices such as smartphones, edge devices and other constrained environments but also providing capable smart features without relying on constant cloud access.

Various SLMs and their Use Cases

Here are some examples of popular SLMs available today, along with their use cases.

Apart from the above popular models, SmolLM2 and MobileLLM are also worth mentioning as Small Language Models (SLMs). SmolLM2, with 1.7 billion parameters focuses on reasoning, mathematics, and instruction-following tasks through specialized training data. MobileLLM, on the other hand, is optimized for mobile-first environments and is ideal for smartphones and edge devices.

Pros and Cons of SLM

Small Language Models offer a compelling alternative to large-scale AI systems, providing efficiency and accessibility while facing certain performance trade-offs.

Pros

• Cost, energy efficient, resource-friendly with simpler fine-tuning
• Can handle key NLP tasks such as text generation, summarization, translation and classification.
• Faster responses due to On-device processing with no internet ensuring better privacy
• Democratize AI for smaller organizations and developers without major infrastructure investment

Cons

• Limited scope and not suitable for complex reasoning
• Small datasets might introduce bias risks and robustness issues
• Difficulty in creative text generation and capturing complex linguistic nuances

Despite these limitations, SLMs remain valuable for the applications where efficiency and resource optimization are prioritized over the performance.

When to use SLM

SLMs are ideal for applications requiring efficiency, privacy, and offline functionality. They are already used in many real-world situations such as chatbots and voice assistants directly on phones, giving quick responses without using the internet. Some SLMs help developers write and fix code. Travelers can use them for language translation on the go. Businesses use SLMs to create summaries, social media posts, and reports. In healthcare, they support tools like symptom checkers that protect user privacy by staying on the device. Smart home devices also use SLMs to work without cloud support. In education, they can help students by generating quizzes, explanations, and feedback instantly.

Conclusion

SLMs are becoming an important part of making AI more useful, affordable, and easy to access. As AI agents become more common, SLMs could play a vital role in these systems towards specialized tasks. SLMs don’t aim to replace LLMs completely, instead, they offer helpful AI features without always depending on cloud servers. Ultimately, we can confidently say that SLMs may not be giants, but they’re powerful companions for the future.

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