ESP32 vs Pi Zero vs Jetson Nano: First TinyML Pick

#tinyml #esp32 #raspberrypi #jetsonnano

The $5 Board Beat the $150 One

I ran the same MobileNet v2 quantized model across three edge boards — ESP32-S3, Raspberry Pi Zero 2 W, and Jetson Nano — expecting the Jetson to dominate. It didn't. For a wake-word detection task running 24/7, the ESP32 pulled 0.3W idle versus the Nano's 5W, making it 16x more power-efficient for always-on inference. The Pi Zero sat in the middle at 1.2W but choked on anything beyond INT8 models.

If you're building your first TinyML project, the board choice matters more than the model architecture. Pick wrong and you'll spend weeks fighting thermal throttling, power budgets, or toolchain hell. Here's what actually happened when I deployed the same 10-class audio classifier on each platform.