Running Multimodal Large Language Models on the Raspberry Pi 5: Leveraging the Power of AI on The Edge

The rapid advancement of artificial intelligence (AI) and machine learning has revolutionized various industries, and the field of natural language processing (NLP) is no exception. Large language models (LLMs) have demonstrated remarkable capabilities in understanding and generating human-like text, making them invaluable tools for a wide range of applications.

However, running these models often requires significant computational resources, which can be a challenge for embedded systems and single-board computers like the Raspberry Pi.

I have seen many people talking about running the smaller models on Raspberry Pi and I wanted to do the experiment myself to find out how feasible it is to run a model on a single board computer for any practical use cases. There are several use-cases on my mind where running a model on a single board computer makes lot of sense. I will come to that later in this article.

Before that, let’s first install the Ollama. Ollama is an open-source framework that allows users to run large language models (LLMs) on their local systems. I assume you have your Pi 5 setup. If you are just getting started with Raspberry Pi, no worries — follow this official guide and continue this article. Run below…