Here is a high level of the differences:
# fp32
# torchvision.models.inception_v3(pretrained=False, aux_logits=False)
# state_dict keys of Conv2d_1a_3x3
# conv
Conv2d_1a_3x3.conv.weight           
# bn
Conv2d_1a_3x3.bn.weight             
Conv2d_1a_3x3.bn.bias               
Conv2d_1a_3x3.bn.running_mean       
Conv2d_1a_3x3.bn.running_var        
Conv2d_1a_3x3.bn.num_batches_tracked

# ready for quantization but not quantized
# mq = torchvision.models.quantization.inception_v3(pretrained=False, aux_logits=False, quantize=False)
# state_dict keys of Conv2d_1a_3x3
# conv
Conv2d_1a_3x3.conv.weight   
# bn        
Conv2d_1a_3x3.bn.weight             
Conv2d_1a_3x3.bn.bias               
Conv2d_1a_3x3.bn.running_mean       
Conv2d_1a_3x3.bn.running_var        
Conv2d_1a_3x3.bn.num_batches_tracked

# quantized and fused
# mq = torchvision.models.quantization.inception_v3(pretrained=False, aux_logits=False, quantize=True)
# state_dict keys of Conv2d_1a_3x3
# conv, including quantization-specific scale+zp
Conv2d_1a_3x3.conv.weight    
Conv2d_1a_3x3.conv.bias      
Conv2d_1a_3x3.conv.scale     
Conv2d_1a_3x3.conv.zero_point
# no bn, it was fused into the conv

You could use the ready for quantization but not quantized model if you are running quantization yourself, the state_dict will be closer to fp32 version since it is before fusion.  Then you’d have to go block by block and see if there are additional differences.