What happens when the cost of running AI is no longer hidden behind a cloud bill—but something you can physically feel?

In this episode of Domesticating AI, Kat Tomanek and Alex Kauffman of Squeez Labs introduce CrankGPT, a fully offline voice assistant that runs on a Raspberry Pi and is powered by a hand crank. As the language model performs inference and generates speech, the crank becomes harder to turn, converting an abstract computational cost into physical exertion.

CrankGPT began as an experiment: could Squeez Labs build a responsive, completely self-contained voice AI system without a cloud connection, wall outlet, or battery? Answering that question required much more than placing a small language model on a Raspberry Pi. Kat and Alex had to engineer the complete system, including speech recognition, voice activity detection, language-model inference, text-to-speech, boot time, memory usage, power delivery, and conversational latency.

The result is a modular voice stack that uses specialized components rather than one enormous multimodal model. That architecture allowed the team to select each model according to the actual requirements of the device. The voice only needs to be understandable. The language model needs to respond quickly enough for conversation. The speech recognizer needs to run efficiently on a CPU. Anything beyond those requirements consumes resources without necessarily improving the experience.

We discuss why local AI is not simply about buying an expensive GPU, why developers should begin with the machines they already own, and how constraints reveal the hardware characteristics that actually matter. In CrankGPT’s case, memory bandwidth and cold-start behavior created more significant limitations than several of the specifications the team initially expected to matter.

The conversation also examines a larger problem in the AI industry: developers frequently select the largest available model before defining the problem they need to solve. Cloud services conceal the energy, infrastructure, privacy, and financial costs of those decisions. CrankGPT makes those costs visible—and makes a strong case for engineering systems around specific requirements instead of throwing more compute at every problem.

We also cover Pinokio as an accessible entry point for experimenting with local AI, Whisper and whisper.cpp for speech recognition, modular voice-agent architecture, Raspberry Pi optimization, and why some supposedly ideal AI use cases are still better solved with a book, calculator, or conventional software.

CrankGPT and build documentation:https://squeezlabs.github.io/handcrank/

CrankGPT:https://crankgpt.com/

Squeez Labs:https://squeezlabs.com/

Edge Voice Agent:https://github.com/ktomanek/edge_voice_agent

Pinokio:https://pinokio.co/

whisper.cpp:https://github.com/ggml-org/whisper.cpp

OpenAI Whisper:https://github.com/openai/whisper

llama.cpp:https://github.com/ggml-org/llama.cpp

Liquid AI:https://www.liquid.ai/

Gemma:https://deepmind.google/models/gemma/

DietPi:https://dietpi.com/

Raspberry Pi:https://www.raspberrypi.com/

Domesticating AI is a biweekly podcast about practical AI for developers. We help you brave the feral open-source AI landscape so you can tame it instead of getting dragged along by it.

Keep your AI on a leash.

Resources