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title: README
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Brainquiver is rethinking where language models run. We design efficient architectures that fit on microcontrollers smaller than a fingernail, bringing genuine intelligence to the edge.
The result is AI that is faster, private, and far more sustainable, drawing a fraction of the energy with no water or data-center footprint, bringing capabilities within reach of the people and places centralized infrastructure has left behind, offline, off-grid, and low-cost.
AURI
AURI is a family of language models compact enough to run on a single chip, yet capable enough to feel like far more, calling on only the part of itself each moment needs, like stars lighting up to form a constellation. No cloud, no in-flight data, no waiting. Just a mind that lives on the device and answers the instant you ask.
What you will find here
- AURI checkpoints - compact, edge-deployable language models
- Quantized weights and conversion tooling for microcontroller targets
- Notes and reproducible configs for embedded inference
Status
Early and active. Follow the org to see models as they are released.