How to Set Up GPT API on a Raspberry Pi

ByRasim

Running AI on a tiny computer sounds futuristic, but with the Raspberry Pi and the OpenAI GPT API, it’s easier than ever. In this guide, we’ll cover step by step how to set up GPT API on a Raspberry Pi, plus the exact supplies you need to get started.

Supplies You’ll Need

Here’s everything required to follow this tutorial. These links include my Amazon affiliate tag rasmurtech-20:

Step 1: Update Your Raspberry Pi

Open the terminal and run:

sudo apt update && sudo apt upgrade -y

Step 2: Install Python and Pip

Check if Python is installed:

python3 --version

If missing, install it with:

sudo apt install python3 python3-pip -y

Step 3: Create a Virtual Environment

This step keeps your GPT project dependencies clean:

sudo apt install python3-venv -y
python3 -m venv gpt_env
source gpt_env/bin/activate

Step 4: Install the OpenAI Library

Inside the environment, install:

pip install openai

Step 5: Get and Save Your API Key

Save it as an environment variable:

echo "export OPENAI_API_KEY='your_api_key_here'" >> ~/.bashrc
source ~/.bashrc

Step 6: Write a Test Script

How to Set Up GPT API on a Raspberry Pi

Create gpt_test.py:

nano gpt_test.py

Paste this code:

import os
from openai import OpenAI

client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))

response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Hello, Raspberry Pi world!"}
    ]
)

print(response.choices[0].message.content)

Save and exit (CTRL+O, Enter, CTRL+X).

Step 7: Run the Script

python3 gpt_test.py

You should see GPT reply directly in your terminal 🎉.

Expand Your Project

Once you know how to set up GPT API on a Raspberry Pi, you can build:

  • A voice assistant (using a USB mic and speaker).
  • An IoT controller (link GPT to smart devices).
  • A web-based chatbot (with Flask or FastAPI).
  • A debugging helper for your Raspberry Pi coding projects.

Troubleshooting

  • No module named openai → Activate your virtual environment.
  • API key not found → Run echo $OPENAI_API_KEY to verify.
  • Slow responses → Use lighter models like gpt-4o-mini.

Conclusion

By following this guide, you’ve learned how to set up GPT API on a Raspberry Pi. With just a few commands, you can now bring AI-powered projects to life on one of the smallest, most versatile computers available.

How to Set Up GPT API on a Raspberry Pi – FAQ

Q1: Do I need a Raspberry Pi 5, or will a Pi 4 work?
A: Both work. A Pi 4 (2GB+) is fine for API calls, while a Pi 5 is snappier for multitasking or running web apps alongside the API.

Q2: Which Python package should I install?
A: Use the official openai Python SDK. Install it inside a virtual environment with pip install openai.

Q3: Where do I put my OpenAI API key?
A: Store it as an environment variable for safety:
echo "export OPENAI_API_KEY='YOUR_KEY'" >> ~/.bashrc && source ~/.bashrc

Q4: I get “ModuleNotFoundError: openai.” What’s wrong?
A: You likely didn’t activate your venv. Run source gpt_env/bin/activate, then reinstall with pip install openai.

Q5: Which model should I call from the Pi?
A: For speed and low cost, start with a lightweight model such as gpt-4o-mini. You can change models later if you need more capability.

Q6: Can I run the model locally on the Pi?
A: This guide uses the cloud API (recommended). Full LLMs are heavy; local models on a Pi are possible but much slower and more complex to set up.

Q7: How much will this cost?
A: Costs depend on tokens used. Keep prompts short, cache system messages, and log usage while testing.

Q8: How do I secure my API key on a shared Pi?
A: Don’t hard-code keys. Use environment variables, .env files with strict permissions, or systemd service files that inject the key at runtime.

Q9: Can I build a voice assistant with this setup?
A: Yes. Add speech-to-text (e.g., Vosk or Whisper API) and text-to-speech (e.g., pyttsx3 or an online TTS) around the same GPT call.

Q10: My requests are slow—any tips?
A: Use a faster model, reduce prompt size, avoid large response tokens, and keep your Pi wired via Ethernet for better latency.

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