Overview

Quantization is a technique used in machine learning and deep learning to reduce the precision of numerical data, typically from 32-bit floating point numbers to lower bit depths like 8-bit integers.

By reducing the number of bits needed for each parameter or activation, quantization significantly decreases model size and computational requirements, making it easier to deploy models on devices with limited resources such as mobile phones and IoT gadgets.

Key aspects

In 2026, quantization will play a crucial role in deploying AI applications that require real-time performance and low power consumption, enabling technologies like TensorFlow Lite and ONNX Runtime to support efficient inference on edge devices.

Furthermore, advances in post-training quantization techniques and automated tools provided by platforms like PyTorch and Google's Model Optimization Toolkit will continue to streamline the process of deploying high-performance models with minimal accuracy loss.