

Time: 57.82
Samples/second: 8.86
GPU memory occupied: 14949 MB.
We see that already a relatively small batch size almost fills up our GPU's entire memory. However, a larger batch size 
can often result in faster model convergence or better end performance. So ideally we want to tune the batch size to our
model's needs and not to the GPU limitations. What's interesting is that we use much more memory than the size of the model. 
To understand a bit better why this is the case let's have a look at a model's operations and memory needs.
Anatomy of Model's Operations
Transformers architecture includes 3 main groups of operations grouped below by compute-intensity.