Open to Work · 𝕏 slvDev · LinkedIn
This is a 28.9 million parameter language model that generates text on an ESP32-S3, a microcontroller that costs about $8. It runs on the chip itself, with nothing sent to a server, and it writes each word to a small screen wired to the chip at roughly 9 tokens per second. The last language model people ran on a chip like this had 260 thousand parameters, so this one holds about a hundred times more. It fits because most of the model lives in flash instead of RAM, using an idea from Google's Gemma models called Per-Layer Embeddings.
| Parameters | 28.9M stored (25M of them in a flash lookup table) | | Chip | ESP32-S3, about $8, with 512KB SRAM, 8MB PSRAM and 16MB flash | | Speed | about 9.5 tok/s end to end (9.7 tok/s of pure compute) | | Connectivity | none, everything runs on the device | | Model size | 14.9MB at 4-bit |
A microcontroller has very little fast memory. The ESP32-S3 gives you 512KB of SRAM. Normally the whole model has to be reachable from there, which keeps you stuck with tiny models, and that is why the previous model on a chip like this had only 260 thousand parameters.
The way around it is to stop putting the model in fast memory at all. Most of a language model's parameters sit in an embedding table, which the model reads from rather than computes on. So you can leave that 25 million row table in slow flash and pull only the few rows each token needs, about 450 bytes, while the small part that does the actual work stays in fast memory. The large model then costs almost nothing to run, because you never load most of it. It just sits in flash and gets sampled a little at a time.
That idea is Google's Per-Layer Embeddings, from Gemma 3n and Gemma 4. Here it runs on the memory layout of a microcontroller instead of a phone or a GPU. As far as I can tell, nobody had tried it on a chip this small.
SRAM (fast, tiny) the "thinking" core, used on every token
PSRAM (medium) the output head and working memory
FLASH (huge, slow) the 25M-param table, about 6 rows read per token (~450 B)
The model was trained on TinyStories, so it writes short, simple stories and mostly keeps them coherent. It will not answer questions, follow instructions, write code, or know facts. That limit comes from the small part of the model that does the reasoning, and the memory trick does not change it. What is interesting here is the architecture, fitting a large model onto a tiny chip, rather than what a 28.9 million parameter model can say.
The firmware, the wiring, and the flashing steps live in firmware/esp32_llm/README.md. The training, ablation, and quantization code is in
src/
and experiments/
. The full method, the ablations, and the on-chip measurements are written up in .
RESULTS.md
TinyStories is the dataset this trains on: short synthetic stories simple enough that a small model can still learn to write coherently (Ronen Eldan and Yuanzhi Li, Microsoft Research, arXiv:2305.07759). The other half is Per-Layer Embeddings, Google's design from the Gemma models, which is what lets a big model fit on a small chip.
Andrej Karpathy's llama2.c is why a lot of people, me included, believe you can train a tiny language model and run it in plain C at all. This grew out of that.
I left the messy history in the repo on purpose. That includes a bug I found in my
own parameter accounting, which had inflated an early number, and the corrected
result that followed once I fixed it. The commit history and RESULTS.md
show where the numbers moved and why.