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title: 28.9M-parameter LLM runs locally on ESP32-S3 at 9 tokens/s
description: Developer Slava S. has managed to run a 28.9M-parameter LLM entirely on an ESP32-S3 microcontroller at ~9 tokens/second, without relying on any cloud backend....
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og:description: Developer Slava S. has managed to run a 28.9M-parameter LLM entirely on an ESP32-S3 microcontroller at ~9 tokens/second, without relying on any cloud backend....
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# 28.9M-parameter LLM runs locally on ESP32-S3 at 9 tokens/s

**[CNX Software](https://daily.dev/sources/cnx-software)** · [@cnxsoft](https://daily.dev/cnxsoft) · 3 min read · 0 upvotes · 0 comments

## Summary

Developer Slava S. has managed to run a 28.9M-parameter LLM entirely on an ESP32-S3 microcontroller at ~9 tokens/second, without relying on any cloud backend. The key insight is using Google's Per-Layer Embeddings technique (from Gemma 3n/4): since most parameters reside in a large embedding table (~25M rows), they can be stored in slow 16MB flash memory rather than fast SRAM. Only the ~450 bytes needed per token are loaded into the 512KB SRAM at inference time. The model (14.9MB at 4-bit quantization) was trained on TinyStories and can only generate short stories — it cannot answer questions or follow instructions. The project runs on an ~$8 ESP32-S3 board with 8MB PSRAM and 16MB flash, connected to a small I2C display.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://www.cnx-software.com/2026/08/03/28-9m-parameter-llm-runs-locally-on-esp32-s3-at-9-tokens-s>

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---

Tags: [#llm](https://daily.dev/tags/llm), [#iot](https://daily.dev/tags/iot), [#embedded](https://daily.dev/tags/embedded), [#local-ai](https://daily.dev/tags/local-ai)

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