7 ESP32-S3 Chips Run a 0.5B LLM with 1.58-bit Quantization
ESP32S3 cluster running 1.58-bit (BitNet) Language model
A GitHub project demonstrates a distributed pipeline inference engine that splits a 0.5B language model across seven ESP32-S3 microcontrollers. One master node handles tokenization and embedding, while six compute nodes process transformer layers using 1.58-bit ternary quantization (BitNet). Communication occurs over a high-speed SPI daisy-chain, enabling the cluster to run the model entirely on low-cost hardware.
This project runs a sliced 0.5B LLM across a cluster of 7 ESP32s3.
- ladyanita22
This is something I've been fantasizing about for long.
Let's say we took Rust, a language that makes parallelization easier than others (as it helps you avoid some common footguns). How difficult would it be to have a massively parallel computer system made out of many tiny, simple microcontroller-like chips? Let's say we picked many little Risc-V's. Surely this would be an interesting experiment (though I'm not sure whether it'd make economic sense or not...)
- tdhz77
Soon ai in every lightbulb running Kubernetes
- cameron_b
It is a bit of a bummer to see that the degree of 'compression' makes it a fancy llm noise-maker. It is still charming.
- NDlurker
I'm curious how this would handle grammar checking on a basic word processor. Or maybe generate worlds for small text based games. I have no idea what the capabilities are of a cluster like this.
- librasteve
haha … this is precisely the kind of project that https://bil-lang.org is aimed at: Go for parallel (ie in this case pipeline processing).
don’t get too excited until we get the TinyGo backend built though ;-)