Deep Learning: Why does increase batch_size cause overfitting and how does one reduce it?

I used to train my model on my local machine, where the memory is only sufficient for 10 examples per batch. However, when I migrated my model to AWS and used a bigger GPU (Tesla K80), I could accomodate a batch size of 32. However, the AWS models all performed very, very poorly with a large indication of overfitting. The model I am currently using is the inception-resnet-v2 model, and the problem I'm targeting is a computer vision one. One explanation I can think of is that it is probably the batch-norm process that makes it more used to the batch images.