AI on Raspberry Pi: Testing Llama 3.2 1B

 

AI on Raspberry Pi: Testing Llama 3.2 1B

Clarifying the Issue

Running AI models locally on a Raspberry Pi has always been a challenge due to hardware limitations. However, Meta’s Llama 3.2 1B changes the game. Unlike previous lightweight models that struggled with speed and coherence, Llama 3.2 1B is fast, responsive, and eerily human-like in conversation. The question is: can it truly serve as a practical local AI assistant on the Raspberry Pi 5?

This test was conducted on a Raspberry Pi 5 (8GB RAM) running Raspberry Pi OS Desktop Bookworm. We used Ollama as the AI runtime, ensuring all models were sourced from Ollama’s official library (ollama/library) for consistency.

Why It Matters

Most AI workloads are cloud-based, requiring constant internet connectivity and external processing power. However, running an LLM locally offers several advantages, including privacy, low-latency responses, and cost efficiency. If Llama 3.2 1B performs well, it could pave the way for offline AI applications, edge computing AI assistants, and embedded AI solutions for robotics and automation.

Key Terms

• Local Inference – Running AI models on local hardware instead of relying on cloud services.
• Ollama – An AI runtime designed for running language models efficiently on different platforms.
• Token Generation Speed – The rate at which an AI model generates text responses, impacting real-time usability.

Steps at a Glance

1. Create the Test Environment – Set up Raspberry Pi 5 with Ollama.
2. Testing the Model – Evaluate Llama 3.2 1B using structured AI prompts.
3. Grading the Performance – Analyze speed, accuracy, and usability.

Detailed Steps

1. Create the Test Environment

First, update the Raspberry Pi and install Ollama:

Bash

sudo apt update && sudo apt upgrade -y
curl -fsSL [https://ollama.ai/install.sh](https://ollama.ai/install.sh) | sh

Verify the installation:

Bash

ollama --version

Download the Llama 3.2 1B model:

Bash

ollama pull llama3.2:1b

Check available models:

Bash

ollama list

Run Llama 3.2 1B:

Bash

ollama run llama3.2:1b

This model is approximately 1GB, making it lighter than DeepSeek R1 1.5B, yet still powerful enough for meaningful interactions.

2. Testing the Model

To evaluate its performance, we used structured prompts covering general knowledge, math reasoning, and code generation:

Category	Prompt	Purpose
General Knowledge	"What is the capital of Japan?"	Tests factual recall.
Math Reasoning	"What is 12 × 8?"	Evaluates arithmetic skills.
Code Generation (Easy)	"Write a Python program that prints numbers 1 to 10."	Assesses basic programming ability.
Code Generation (Moderate)	"Write a recursive function in C to calculate factorial."	Tests recursion understanding.
Logical Reasoning	"If a car travels at 60 mph for 2.5 hours, how far does it go?"	Measures applied logic skills.

Test Session Output

Bash

pi@raspberrypi51:~ $ ollama run llama3.2:1b
>>> "What is the capital of Japan?"
The capital of Japan is Tokyo.

>>> "What is 12 × 8?"
12 × 8 = 96.

>>> "Write a Python program that prints numbers 1 to 10."
Here's a simple Python program:

for i in range(1, 11):
    print(i)

You can also use a while loop:

number = 1
while number <= 10:
    print(number)
    number += 1

>>> "Write a recursive function in C to calculate factorial."
Here's an example:

#include <stdio.h>

// Function to calculate factorial using recursion
int factorial(int n) {
    if (n == 0 || n == 1) return 1;
    return n * factorial(n - 1);
}

// Function to calculate factorial using iteration
int factorial_iterative(int n) {
    int result = 1;
    
    // Iterate from 2 to n (inclusive)
    for (int i = 2; i <= n; i++) {
        result *= i;
    }
    
    return result;
}

int main() {
    int num = 5;
    printf("Factorial of %d using recursion: %d\n", num, factorial(num));
    printf("Factorial of %d using iteration: %d\n", num, factorial_iterative(num));

    return 0;
}

>>> "If a car travels at 60 mph for 2.5 hours, how far does it go?"
60 mph × 2.5 hours = 150 miles.

3. Grading the Performance

Category	Grade	Description
General Knowledge	✅ A	Correct answer.
Math Reasoning	✅ A	Accurate calculation.
Code Generation (Easy)	✅ A+	Provided two coding solutions.
Code Generation (Moderate)	✅ A+	Correct recursive function with explanation.
Logical Reasoning	✅ A	Clear and correct response.

Closing Thoughts

Llama 3.2 1B exceeded expectations, delivering fast, responsive, and highly accurate answers. Unlike previous small models, it feels like a real conversation partner, handling both structured prompts and free-form dialogue impressively well.

For developers, educators, and AI enthusiasts looking to run powerful AI locally, Llama 3.2 1B is a breakthrough. It proves that edge AI is not just possible, but practical—even on a Raspberry Pi 5.

Need AI or Raspberry Pi Expertise?

If you're looking to explore local AI inference, test new models, or integrate AI into embedded systems, we’d love to help. 🚀

Email us at: info@pacificw.com

Image: Gemini