AI models consist of several components that can be quantized — in particular parameters, the internal variables models use to make predictions or decisions. This is convenient, considering models perform millions of calculations when run. Quantized models with fewer bits representing their parameters are less demanding mathematically, and therefore computationally. (To be clear, this is a different process from “distilling,” which is a more involved and selective pruning of parameters.)
But quantization may have more trade-offs than previously assumed.