通过将大部分模型参数存储在闪存而非RAM中，并利用Google的逐层嵌入技术，它比之前的微控制器语言模型参数规模大了100倍。
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) t
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