Quantization in AI refers to the process of reducing the precision of numbers used to represent data, such as the weights and activations in machine learning models, without significantly sacrificing performance. Typically, AI models use 32-bit floating-point numbers (FP32) for high precision, but quantization reduces these to lower-bit formats like 16-bit floating-point (FP16), 8-bit integers (INT8), or even lower, such as 4-bit or 2-bit representations. This reduction decreases the amount of memory required and speeds up computations, making models more efficient, especially when deployed on devices with limited resources like smartphones or embedded systems.

Imagine a large neural network as a map with incredibly detailed roads. If the goal is to navigate a car, you may not need every tiny detail; a simplified map with major routes can still get you to your destination effectively. Quantization works similarly by simplifying the data without eliminating its core purpose.

Quantization is crucial for making AI models practical in real-world applications where memory and power consumption are limited. It allows models to run faster on CPUs, GPUs, or specialized AI chips, making AI accessible for applications like voice assistants, real-time object detection, or autonomous vehicles.

However, quantization can introduce a trade-off: while models become faster and smaller, the reduction in numerical precision can lead to a minor drop in accuracy. Researchers often use techniques like post-training quantization (quantizing a trained model) or quantization-aware training (training a model while simulating quantization effects) to mitigate accuracy losses.

Quantization essentially makes AI models lighter and faster, enabling efficient deployment without major sacrifices in performance, which is essential for expanding AI’s reach across various technologies and devices.