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Intellectually Curious · July 29 · 5 min

Big Intelligence on Tiny Chips

In this episode, we unpack how engineers fit a 28.9M-parameter language model into an $8 ESP32-S3. By using per-layer embeddings and moving most data to flash, the active compute stays in fast SRAM, enabling offline AI at the edge. We explore what it can (and can't) do today—short, simple stories rather than complex instructions—and why this matters for private, decentralized intelligence on everyday devices. Note: This podcast was AI-generated, and sometimes AI can make mistakes. Please double-check any critical information. Sponsored by Embersilk LLC

0:00-5:43
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In this episode, we unpack how engineers fit a 28.9M-parameter language model into an $8 ESP32-S3. By using per-layer embeddings and moving most data to flash, the active compute stays in fast SRAM, enabling offline AI at the edge. We explore what it can (and can't) do today—short, simple stories rather than complex instructions—and why this matters for private, decentralized intelligence on everyday devices.


Note:  This podcast was AI-generated, and sometimes AI can make mistakes.  Please double-check any critical information.

Sponsored by Embersilk LLC

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