Dynamic Quantization description in Torch says that it is used for situations where the model execution time is dominated by loading weights from memory rather than computing the matrix multiplications. For live applications, the model is already loaded so the bottleneck is matrix multiplications for inference at runtime. Am I right or missing out on some details here?