Developer runs 180M-parameter MoE language model on a $10 ESP32-P4 microcontroller

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A developer named cyfrit published a project called p-for-llm in early August 2026, demonstrating a 180.9-million-parameter mixture-of-experts language model running inference on an ESP32-P4 microcontroller priced between six and ten dollars. The model uses ternary-weight quantization and top-1 expert routing to keep compute costs low, generating around 9 tokens per second within the chip's limited memory. Notably, cyfrit trained the model on roughly 12 billion tokens using a single consumer RTX 5060 Ti GPU, without institutional backing or a compute cluster. The project carries one significant caveat: weights must currently be loaded onto the board via USB from a host machine at startup, meaning the device is not yet fully self-contained. SD card loading, which would make the board fully independent, is listed by the author as planned future work rather than a completed feature.

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