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GitHub - Carloscodix/qapla: A char-level transformer trained from scratch on an $8 ESP32-S3. Not inference: the chip runs the full training loop, with backprop written by hand in C.
transformersmicrocontrollersai-trainingdeveloper-tools
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An SLM trained on $8 ESP32-S3

Carloscodix/qapla is a char-level transformer model trained from scratch on an $8 ESP32-S3 chip, capable of running the full training loop with backpropagation implemented in C. This project demonstrates that training a model does not always require expensive GPUs or datacenters, as a low-cost microcontroller can suffice.

github.com

πŸ”₯πŸ”₯πŸ”₯πŸ”₯πŸ”₯

12 min

8/5/2026

Running a 28.9M parameter LLM on an $8 microcontroller

A 28.9 million parameter language model generates text on the ESP32-S3 microcontroller, which costs about $8. The model operates entirely on the chip without server communication, displaying output on a small screen at approximately 9 tokens per second.

github.com

πŸ”₯πŸ”₯πŸ”₯πŸ”₯πŸ”₯

3 min

7/26/2026

An SLM trained on $8 ESP32-S3

Carloscodix/qapla is a char-level transformer model trained from scratch on an $8 ESP32-S3 chip, capable of running the full training loop with backpropagation implemented in C. This project demonstrates that training a model does not always require expensive GPUs or datacenters, as a low-cost microcontroller can suffice.

github.com

πŸ”₯πŸ”₯πŸ”₯πŸ”₯πŸ”₯

12 min

8/5/2026

Running a 28.9M parameter LLM on an $8 microcontroller

A 28.9 million parameter language model generates text on the ESP32-S3 microcontroller, which costs about $8. The model operates entirely on the chip without server communication, displaying output on a small screen at approximately 9 tokens per second.

github.com

πŸ”₯πŸ”₯πŸ”₯πŸ”₯πŸ”₯

3 min

7/26/2026

An SLM trained on $8 ESP32-S3

Carloscodix/qapla is a char-level transformer model trained from scratch on an $8 ESP32-S3 chip, capable of running the full training loop with backpropagation implemented in C. This project demonstrates that training a model does not always require expensive GPUs or datacenters, as a low-cost microcontroller can suffice.

github.com

πŸ”₯πŸ”₯πŸ”₯πŸ”₯πŸ”₯

12 min

8/5/2026

Running a 28.9M parameter LLM on an $8 microcontroller

A 28.9 million parameter language model generates text on the ESP32-S3 microcontroller, which costs about $8. The model operates entirely on the chip without server communication, displaying output on a small screen at approximately 9 tokens per second.

github.com

πŸ”₯πŸ”₯πŸ”₯πŸ”₯πŸ”₯

3 min

7/26/2026

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