TL;DR: A 2.4-4 million parameter, INT8 quantized latent flow transformer can generate images on an RP2350 microcontroller in ~20 seconds.
Summary: An indie developer implemented a latent flow transformer with 12 layers and AdaLN-Zero conditioning, quantized to INT8, on an RP2350 microcontroller. This model, with 2.4-4 million parameters, can generate images in approximately 20 seconds, utilizing DMA for weight streaming and Relu² activation for sparsity.
Why it matters: This demonstrates significant progress in running complex AI models on extremely resource-constrained edge devices, opening new possibilities for embedded AI applications. Builders should explore efficient quantization, custom inference engines, and sparse activation techniques for microcontrollers.
Source: reddit