การทำควอนไทซ์เพื่อโมเดลที่เล็กและเร็วขึ้น

Big models are slow and heavy to serve. Quantization shrinks them by storing numbers with fewer bits, so they run faster and lighter. 📉

Models usually store weights as 32-bit floats. Quantization converts them to 8-bit integers, cutting size by roughly four times.

Integer math is cheaper than floating-point on most hardware, so int8 inference uses less memory bandwidth and finishes sooner.

A scale and zero-point map each float range onto integers. They let the model recover an approximate float value when computing.

Fewer bits means some precision is lost, so accuracy may dip slightly. For most models the drop is small and well worth the speed.

Dynamic quantization is the simplest path. It quantizes weights ahead of time and activations on the fly, ideal for linear and RNN layers.

Static quantization also quantizes activations ahead of time. You feed it sample data to calibrate ranges, gaining more speed on CPUs.

For the best accuracy, quantization-aware training simulates int8 during training so the model learns to tolerate the lower precision.

After quantizing, save the model and compare file sizes. An int8 version is typically about a quarter of the float32 original. 💾

Run your validation set on the quantized model and confirm accuracy is still acceptable before you deploy it to real users.

Quantization pays off most on CPUs, phones, and edge devices where memory is tight and integer math is well supported. 📱

You want the quickest quantization with no calibration step. Which fits?

You shrank a model with quantization, traded float32 for int8, picked dynamic, static, or aware training, and re-checked accuracy. 🎉