What Is a Small Language Model (SLM)—and Can It Really Run on a Raspberry Pi 5?
So…you’re curious about running AI on a Raspberry Pi?
You're not alone!
We’ve all seen the headlines about large language models doing everything from writing code to making music. But here’s the real twist: not all AI models are large, some are lightweight enough to run on a device that literally fits in the palm of your hand.
Yes, I’m talking about Small Language Models (SLMs)!
Yes, they can run on a Raspberry Pi 5.
And yes, I’ll walk you through exactly what that means.
Let’s Start With the Basics: What Is a Small Language Model?
At its core, a Small Language Model (SLM) is a scaled-down version of the large language models (LLMs) we hear so much about, like GPT-4 or Claude. But smaller doesn’t mean useless. In fact, smaller often means:
Faster responses
Less hardware dependency
More privacy (since data can stay local)
Ideal for on-device applications like personal assistants, sensors, and local chatbots
These models are typically under a few billion parameters (versus 100+ billion in LLMs) and are trained to be lean, efficient, and focused on common tasks like summarizing text, answering questions, and even translations.
Why Raspberry Pi 5?
Let’s address the Pi in the room.
The Raspberry Pi 5 is the most powerful Pi to date. With a quad-core ARM Cortex-A76 processor clocked at 2.4GHz and support for up to 16GB of RAM, it finally enters the realm where it can realistically run small AI models locally, which means no cloud (or internet) required.
Here’s what makes the Raspberry Pi 5 a good fit:
Better thermal management which means less risk of overheating during AI tasks
Faster I/O which is crucial for loading models
USB 3.0 ports that are useful for adding external drives, microphones, or even cameras
GPIO pins if you want to trigger AI responses from physical inputs
Not to mention, the Raspberry Pi 5 starts at $50!
What Can You Actually Do With It?
If you’re wondering what this setup can power, here are a few beginner-friendly project ideas:
A local voice assistant that runs without internet
A smart mirror that responds to voice prompts
A mini chatbot for writing reminders or journaling
A context-aware notification system using sensors and text generation
A classroom-friendly AI tutor (great for hands-on learning)
You’ll need a compatible SLM and a lightweight runtime like Ollama or GGML-based models. These frameworks are designed for running LLMs on CPUs (no GPU required), which makes them perfect for the Raspberry Pi.
Sidebar: What even is a GGML-based model? I’m glad that you asked! GGML is a lightweight, C/C++-based library for running machine learning models, particularly language models, on devices with limited resources — like your laptop, phone, or Raspberry Pi.
The Raspberry Pi doesn't have a powerful GPU like most desktop PCs. GGML makes it possible to run small language models on CPU by:
Using less memory (through quantization like int8/int4)
Optimizing model execution for low-power hardware
You can find GGML-based models via Ollama or LM Studio.
Yes, SLMs run, but with limits
Before you get too excited, let’s be clear: performance will vary.
You won’t get lightning-fast responses or be able to load huge models if you’re relying on the built-in CPU. But you can run models like Phi-2, TinyLLaMA, Mistral Tiny with the right optimizations.
Here’s what helps:
Use higher RAM models of the Raspberry Pi 5
Keep your model files on a fast SSD (via USB 3.0)
Reduce model precision (use int4 or int8 quantization)
Be patient, it may take a few seconds to respond
Running an SLM on a Raspberry Pi 5 is less about speed and more about autonomy. You're learning what it means to truly own your AI system.
But…if you really want to increase speed
Full disclaimer: What I’m about to share is specifically for anyone who considers themselves to be an advanced user.
If you want to squeeze out even more performance, there’s one option worth mentioning: NVMe SSDs.
The Raspberry Pi 5 introduces support for PCIe storage, which means you can technically connect a NVMe drive using an adapter. This setup gives you much faster read/write speeds than an SD card or even a USB SSD, which is especially helpful if you're running larger models or want to reduce model loading times.
That said, this is an advanced use case. It requires additional hardware, power considerations, and possibly a custom case setup. I won’t go into the full build here, but if you’re exploring more demanding AI projects or want a dedicated device for local models, NVMe might be something to look into down the line.
Final Thoughts
So yes, small language models are actually small enough to run on something as compact as a Raspberry Pi 5. This ability is a real shift in how we think about AI at the edge! Whether you’re building a local assistant, experimenting with privacy-first tools, or just curious about what your Raspberry Pi can handle, SLMs open a whole new playground for tinkerers and builders alike. And honestly? It’s kind of exciting to see AI go from massive data centers to a tiny board on your desk.
