The Case for Edge AI: Are Small Language Models the Future of AI?

Background

Large Language Models (LLMs) have dominated AI discussions, but Small Language Models (SLMs) are quietly becoming a game-changer. They still significantly lag behind their larger counterparts in raw capability, research suggests they are catching up fast. Recent advancements in open-source models like Mistral-7B show that well-optimized smaller models can outperform larger ones in specific tasks. With improvements in efficiency, on-device processing, and fine-tuning, SLMs are becoming viable for real-world applications where size, speed, and privacy matter.

We came up with this for a client who was building an AI chat on top of a medical app that handled sensitive, HIPAA-compliant data. Their goal was to provide intelligent, on-device AI assistance while ensuring complete data privacy. By combining a Retrieval-Augmented Generation (RAG) system with a small model running locally on a mobile device, they eliminated reliance on cloud-based processing. This setup allowed them to deliver real-time AI responses while keeping all patient information securely on the user’s device.

Why Small Models?

SLMs offer several key advantages over their massive cloud-based counterparts:

• No Cloud Inference Cost: Running a model locally eliminates the need for expensive API calls. Once deployed, inference is essentially free, making it ideal for cost-conscious businesses.
• Privacy & Security: Since SLMs run on local devices, sensitive data never leaves the user’s machine. This is crucial for industries like healthcare, finance, and defense.
• Speed & Low Latency: Without relying on cloud-based servers, SLMs can deliver faster responses, making them perfect for real-time applications like chatbots, automation tools, and voice assistants.
• Offline Functionality: Unlike cloud-reliant LLMs, SLMs can operate without an internet connection, making them useful for scenarios where connectivity is unreliable—think airplanes, submarines, or remote locations.
• Customizability: SLMs can be fine-tuned for specific tasks without requiring the massive infrastructure needed for training large-scale models.

Use cases for SLMs span across industries—medical diagnostics, on-device personal assistants, airline systems, military applications, and embedded AI solutions in consumer devices.

The Challenge: Local Storage and Processing

The biggest challenge of deploying SLMs locally is balancing performance with device constraints. Even a relatively small 7B parameter model requires significant memory and processing power. Quantization and pruning techniques can reduce model size while preserving performance, but careful optimization is needed to ensure smooth real-world deployment.

Another key challenge is handling local storage. Traditional cloud-backed AI systems store and retrieve data externally, while SLMs must manage this data on-device efficiently. This is where lightweight, edge-optimized databases like GoatDB come in.

The Experiment: Running an SLM Locally with GoatDB

This is a demo of a ChatGPT-like interface that runs completely in the browser, no network connection needed. It uses GoatDB for storing chat history, and wllama for running the models.

Currently, it runs the Stories15M model which generates fun stories but can easily be adapted to specific use cases.

The experiment involved:

1. Setting up GoatDB for local data storage.
2. Running an SLM locally for text generation.
3. Integrating the two to create a self-contained AI assistant.

The Results: Local AI Chat

👉 Live demo is available at https://chat.goatdb.dev/

The demo is deployed on a single AWS t4g.nano machine with 2 ARM VCPUs, 0.5GB RAM and 8GB EBS.

Getting Started

Before continuing, make sure you have Deno 2+ installed. If not, install it from here. Then, run the following commands inside your project's directory.

Running the Demo

deno task debug

Cleaning the Server Data

deno task clean

Building the Server

deno task build

Conclusion

Small Language Models are proving to be a viable alternative to massive cloud-based AI systems. With zero inference costs, better privacy, and offline capabilities, they offer an attractive solution for edge computing applications. Tools like GoatDB make local AI even more practical by handling data storage seamlessly. While challenges like memory constraints and inference speed remain, rapid advancements in model optimization are making SLMs more powerful than ever.

If you’re a developer looking to explore offline AI, now is the perfect time to experiment with SLMs. Check out the GoatDB GitHub and start building your own local AI-powered applications.