Questions on quantizing both activation and weights for inference layers?

Assume that hardware provides int8, We can quantize both activations and weights. so we put everything in int8. if there are more than one FC/linear layers, Should we re-quantize from one layer to another directly without repeatedly doing quantized/de-quantized activation between layers? How will this compare with doing weights quantized alone from the perspective of precision?