---
title: "Developer runs 28.9M-parameter model on $10 microcontroller"
url: https://www.parallelquant.com/posts/developer-runs-28-9m-parameter-model-on-10-microcontroller-7b192d
source_name: "Tom's Hardware"
source_url: https://www.tomshardware.com/tech-industry/artificial-intelligence/ai-developer-runs-28-9-million-parameter-model-on-usd10-esp32-s3-microcontroller-uses-googles-per-layer-embeddings-technique-stores-table-on-16mb-flash-memory
published: 2026-07-27T13:07:53.000Z
topics: ["research", "chips"]
publisher: "Parallel Quant"
---

# Developer runs 28.9M-parameter model on $10 microcontroller

*2026-07-27 · Source: [Tom's Hardware](https://www.tomshardware.com/tech-industry/artificial-intelligence/ai-developer-runs-28-9-million-parameter-model-on-usd10-esp32-s3-microcontroller-uses-googles-per-layer-embeddings-technique-stores-table-on-16mb-flash-memory)*

A developer got a 28.9-million-parameter language model running on a $10 ESP32-S3 microcontroller. The setup uses Google's Per-Layer Embeddings technique and stores its embedding table on 16MB of flash memory to fit within the chip's limited RAM.

**Why it matters:** It's a concrete demonstration of how far model compression can push language models onto extremely cheap edge hardware, well below the microcontrollers typically discussed for on-device AI. That has implications for cost-sensitive embedded applications like sensors, toys, and appliances that previously couldn't justify any onboard language capability.

**Topics:** research, chips

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Read the original: https://www.tomshardware.com/tech-industry/artificial-intelligence/ai-developer-runs-28-9-million-parameter-model-on-usd10-esp32-s3-microcontroller-uses-googles-per-layer-embeddings-technique-stores-table-on-16mb-flash-memory
Canonical: https://www.parallelquant.com/posts/developer-runs-28-9m-parameter-model-on-10-microcontroller-7b192d
Published by Parallel Quant — https://www.parallelquant.com