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A practical, measured look at running local language models on a Raspberry Pi 5: which model sizes and quantization levels are realistic, what power and thermal limits to expect, and when a mini-PC is the better call.
A measurement-first look at running local language models at home: where quantization helps, what power draw actually looks like, and when the cloud still wins.
Running AI inference on Raspberry Pi is possible, but stable operation needs careful pipeline design. Most failures come from bottlenecks in capture, preprocessing, or storage, not from the model itself.
BatGuard is an urban biodiversity platform designed for one hard reality: ecological signals are noisy, and policy action requires credible evidence.
