How to Run a Local AI Model on a Raspberry Pi 4

What You’ll Need

Step 1: Choose a Quantized Model

The most critical decision is selecting an appropriate model. Full-scale models like GPT-3 won’t work on the Pi 4. Instead, use a quantized model—one that reduces precision from 32-bit or 16-bit floating-point to 8-bit or 4-bit integers.

What quantization does: It drastically shrinks model size and RAM requirements. You’ll get a slight-to-moderate decrease in accuracy, but it’s the only way to make it practical on Pi hardware.

Where to find models: TheBloke on Hugging Face provides quantized versions of many popular open-source models. For your first test, look for a 3-billion parameter model in 4-bit quantization (q4_0).

Step 2: Install llama.cpp Inference Engine

Python-based frameworks like PyTorch are too heavy for the Pi. Use llama.cpp, a C++-based engine designed for running LLaMA-family models with minimal dependencies and maximum performance.

Installation steps:

1. Open a terminal on your Raspberry Pi 4
2. Clone the repository: git clone https://github.com/ggerganov/llama.cpp.git
3. Navigate to the directory: cd llama.cpp
4. Compile for your Pi’s architecture: make

The compilation process optimizes the code specifically for your Raspberry Pi’s ARM architecture, ensuring optimal performance on consumer hardware.

Step 3: Download and Run Your Model

With the inference engine compiled, download your chosen quantized model. Create a models directory in your llama.cpp folder and place your model file there.

Run your first inference:

./main -m ./models/your-model-name.gguf -p "Hello, what is a Raspberry Pi?" -n 128

Command breakdown:

• -m points to your model file
• -p provides the prompt text
• -n sets maximum tokens to generate (128 = approximately 100 words)

What to expect: Don’t expect instant responses. The Pi will take time to process—this is normal and part of the learning experience.

Step 4: Set Realistic Performance Expectations

This is not ChatGPT. On a Raspberry Pi 4, generation speeds are around 1-3 tokens per second. A short paragraph could take a minute or more to generate.

According to the official Raspberry Pi 4 specifications, the device is powerful for its size but wasn’t designed for the sustained computational load that LLMs require.

Critical: Cooling and Power Management

Running an LLM will push your Pi’s CPU to its limits for extended periods, generating significant heat.

Without proper cooling: The CPU will throttle performance to prevent damage, slowing inference speed even further. A case with a fan or passive heatsink is crucial for this project.

Power supply considerations: A standard phone charger may not provide stable, sufficient power under heavy load, leading to system instability. Use the official Raspberry Pi power supply or a high-quality equivalent rated at 3A or higher.

What You’ve Accomplished

Successfully running a local language model on a Raspberry Pi 4 demonstrates the power of software optimization and modern compact hardware. While performance won’t compete with cloud services or high-end desktops, you now have fully private, offline AI capabilities.

This project teaches valuable lessons about model quantization, efficient inference engines like llama.cpp, and real-world hardware constraints of artificial intelligence. It’s perfect for anyone looking to move beyond APIs and work directly with core AI technology.