After training AI models, a variety of compression techniques can be used to optimize them for deployment. The most common is post-training quantization (PTQ),…
After training AI models, a variety of compression techniques can be used to optimize them for deployment. The most common is post-training quantization (PTQ), which applies numerical scaling techniques to approximate model weights in lower-precision data types. But two other strategies—quantization aware training (QAT) and quantization aware distillation (QAD)—can succeed where PTQ falls short by…
