GPU computing: how much VRAM do I need for mini batch gradient descent? I want to do some GPU computing with an NVIDIA card, and am deciding between having a GTX 960 with a 2GB or 4GB ram. Which one should I take? How much difference would these make in terms of the batch size I can use for mini batch gradient descent? Would this difference be significant?

Thank you for answers.
Generally memory cost increases linearly while mini-batch size increases. If batch size 32 costs you 2GB memory, then batch size 64 will cost you 4GB memory.

In practice, deep learning framework(for instance, keras) will load the entire full-batch into your memory, thus you cannot observe apparent memory increment or decrement during mini-batch tuning.

References

https://github.com/fchollet/keras/issues/146