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An SLM trained on $8 ESP32-S3
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Overview
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.
Summary from github.com.
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Hacker News · github.com
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Based on 5 sampled comments from this thread. Analyzed 2026-08-05.
- Praise: This project is a fascinating blend of human ingenuity and AI assistance — it’s a testament to how collaboration can push boundaries in AI development.
- Praise: The idea of clustering ESP32-S3s opens up exciting possibilities for distributed AI tasks, which could democratize access to powerful computing.
- Praise: The emphasis on manually derived gradients shows a unique approach that may inspire others to explore unconventional methods in AI training.
- Concern: Skepticism looms over the practicality of using Klingon as a training target — some see it as a gimmick rather than a serious application.
- Concern: Doubts arise about the effectiveness of the project, with some commenters questioning whether the approach truly demonstrates meaningful AI capabilities.