Click here to learn more about Gilad David Maayan. Deep learning is the basis for many complex computing tasks, including natural language processing (NLP), computer vision, one-to-one personalized marketing, and big data analysis. Deep learning algorithms are based on neural networks, which commonly have millions of parameters that need to be calculated numerous times in order to train the model. Training a neural network is very computationally intensive, and because these computations can very easily be parallelized, they call for a new approach to hardware. Graphical processing units (GPUs), originally designed for the gaming industry, have a large number of processing cores and very large on-board RAM (compared to traditional CPUs).

I was wondering if there is any good comparisons between top GPUs used for gaming like the Nividia 20x series and the workstation GPUs specialized for deep learning, like the Tesla V100, K80, etc. Obviously the workstations will be far faster, but I was looking for a comparison. If I'm already going to purchase a high end GPU for gaming, I'd like to see just how much slower it would be to train a neural net than paying for service on the cloud.