Quantization changes how numbers such as model weights or activations are stored and processed. Lower-precision representations can make a model fit on smaller hardware or reduce data movement during inference.
The practical benefit depends on the model, hardware and quantization method. Reduced precision can affect output quality, and smaller files do not automatically guarantee faster execution, so the intended workload needs evaluation.
ELI5
Quantization is like writing numbers with less detail to save space. The shorter version can be useful as long as the lost detail does not change the answer too much.
For example, an AI model might store an approximate weight using fewer bits so more of the model fits in device memory. Testing checks whether the smaller version still handles the required tasks well.
Does quantization always make a model faster?
No. Speed depends on hardware support, implementation and the workload as well as the numerical format.
Can quantization reduce accuracy?
Yes. The effect varies, so compare quality and resource use on representative tasks.
