Geek Out Time: Experimenting with FP32 vs. FP16 Quantization on Google Colab’s Free T4 GPU

Today, I’m diving into quantization — a technique to optimize deep learning models by reducing their precision. Specifically, I’m exploring the differences between FP32 (32-bit floating point) and FP16 (16-bit floating point) quantization, using Google Colab’s free T4 GPU to see how model size, inference time, and accuracy are affected.

If you’re curious about quantization or want to try it yourself, Google Colab provides the perfect environment for experimentation at no cost. Let’s jump into the details of how FP32 compares to FP16 and what I discovered through my hands-on testing!

This is an excerpt — the full article continues on Medium.