INT4 quantization is a method of quantizing neural network weights and activations to a reduced set of integer values. The INT4 quantization technique uses 4-bit integer values to represent weights and activations, which provides a good balance between model accuracy and model size.

The quantization process involves dividing the range of values for each weight or activation into a fixed number of quantization levels, and then mapping each value to the nearest quantization level. This reduces the precision of the weights and activations, but also reduces the amount of memory and computational resources required to store and process the neural network.

INT4 quantization is particularly useful for deployment on mobile devices and embedded systems with limited memory and processing power. It can also be used for other applications that require low latency and high throughput, such as real-time video processing and autonomous vehicles.
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