Local Inference on a Raspberry Pi (keepitwiel.github.io)

In a recent experiment, an AI enthusiast benchmarked various small language models (LLMs) on Raspberry Pi 3B+ devices to evaluate their performance in real-world applications, despite acknowledging the hardware's limitations. Key metrics included RAM consumption, token generation speed, and model perplexity, revealing that the Raspberry Pi, with its 1 GB of RAM and aging Cortex-A53 architecture, struggles to run larger models effectively. The tests concluded that resident set sizes for these models exceeded practical limits, ultimately making them unsuitable for serious AI tasks. This experiment underscores significant challenges in deploying hardware like the Raspberry Pi for AI-driven applications, particularly due to memory constraints and slower processing speeds compared to modern GPUs. While the findings highlight the barriers faced in local inference on low-powered devices, they also point to the potential for further exploration, such as optimizing model architectures or parallelizing processes across multiple Raspberry Pis. Overall, the exploration serves as a playful reminder of the technological limitations still at play in the pursuit of powerful and accessible AI solutions.