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Seven ESP32 boards run a distributed 1.58-bit, 0.5B language model

github.com 3 min read Editor score 6/10

The project slices a 0.5B model across seven ESP32-S3 boards, with a master handling tokenization and embeddings and other nodes running transformer layers over SPI. It offers a concrete look at the trade-offs in highly constrained distributed inference.

What

A project runs a sliced 0.5B language model across seven ESP32S3 boards using 1.58-bit weights.

How

The master handles tokenization and embeddings; compute nodes run transformer layers over a SPI daisy-chain before the master samples output.

Why it matters

The design shows how engineers can partition inference across constrained devices using low-bit computation and inter-board communication.

Read on github.com → Hacker News · 16 comments

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