sad_robot:

110M x 4 (float size) = 440M = 0.440 GB


I would be very surprised if just the float size is taken up by your model. Especially when it is training, it has to keep track of gradients. And there are probably lots of other things which need to be in memory for the model to work.
See if you get the out of memory error if you train the network on a batch with just one input. If that works, try increasing the number of inputs in a batch till you figure out the breaking point.
Another thing: perhaps the GPU memory is being used by other processes, so that not all of the 11GB is available? The output of nvidia-smi should tell you if this is the case or not.