GranitePi: Run IBM Granite 4.0 Locally on Raspberry Pi 5 for Fully Offline Privacy-Preserving AI Inference

The GranitePi project allows developers to run the IBM Granite 4.0 large language model locally on Raspberry Pi 5 via Ollama, enabling AI inference without an internet connection and ensuring data stays entirely on the local device. Its core goal is to address privacy issues in AI usage.

As large language models (LLMs) become more capable, users are increasingly concerned about privacy issues in AI usage. Processing sensitive data in the cloud requires trusting third-party service providers, so local-first solutions are more favored. However, running LLMs on resource-constrained edge devices is a technical challenge, and the GranitePi project was created to address this need.

GranitePi is an open-source project designed to enable Raspberry Pi 5 users to run the IBM Granite 4.0 large language model locally, based on the Ollama framework. IBM Granite 4.0 is an enterprise-grade open-source model with a compact size suitable for edge deployment, and its core selling point is that data never leaves the device, achieving true privacy protection.

1. Privacy-first: All inference is done locally with no network transmission, avoiding man-in-the-middle attacks, data leaks, or service provider snooping; 2. Offline availability: No reliance on the internet, usable anytime and anywhere, suitable for network-constrained scenarios like airplanes or remote areas; 3. No subscription costs: No API call fees, with long-term costs lower than cloud services; 4. Full control: Users can independently control parameters such as model version, system prompts, and generation length.

Hardware requirements: Raspberry Pi 5, compatible Linux distribution (e.g., Raspberry Pi OS), at least 2GB of available storage, minimum 1GB RAM; internet connection is only needed during the download phase. Performance expectations: Suitable for scenarios like document Q&A, text summarization, simple code generation, and creative writing assistance; performance is limited for complex reasoning or long-context tasks, but it has unique value in privacy-sensitive scenarios with moderate computing needs.

Installation steps: 1. Download the latest stable zip package from the project's Release page; 2. Extract it to an appropriate directory; 3. Navigate to the extracted folder in the terminal (e.g., cd ~/Downloads/granitepi-4-nano); 4. Grant execution permission to the script and start it (chmod +x start.sh && ./start.sh). Common issues: Permission errors (resolved by executing chmod +x), missing dependencies (sudo apt-get update && sudo apt-get install -f), insufficient memory (close other apps or use a smaller model version).

Applicable scenarios: AI assistant for processing personal privacy documents, offline writing aid, text analysis and summarization of sensitive data, teaching example for edge AI deployment, intelligent Q&A in network-constrained environments. Unsuitable scenarios: High-concurrency real-time services, complex multi-step reasoning tasks, ultra-large-scale document processing, high-quality professional writing.

GranitePi demonstrates the feasibility of running LLMs on edge devices, opening up a practical path for privacy-first AI applications. While it cannot replace all capabilities of cloud-based large models, it offers data autonomy options for privacy-sensitive scenarios. As model efficiency improves and edge hardware advances, such projects will become more practical and are worth the attention of privacy-conscious developers, researchers, and advanced users.