Why the performance of VGG-16 is better than Inception V3?
What are the cons?
It has so many weight parameters, the models are very heavy, 550 MB + of weight size.
Which also means long inference time
Why not just make the model deeper?
More heavier model
More training time
Vanishing gradient problem

With a given receptive field(the effective area size of input image on which output depends), multiple stacked smaller size kernel is better than the one with a larger size kernel because multiple non-linear layers increases the depth of the network which enables it to learn more complex features, and that too at a lower cost. 3X3 kernels help in retaining finer level properties of the image.