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Hardware availability

Deep learning requires complex mathematical operations to be performed on millions, sometimes billions, of parameters. Existing CPUs take a long time to perform these kinds of operations, although this has improved over the last several years. A new kind of hardware called a graphics processing unit (GPU) has completed these huge mathematical operations, such as matrix multiplications, orders of magnitude faster.

GPUs were initially built for the gaming industry by companies such as Nvidia and AMD. It turned out that this hardware is extremely efficient, not only for rendering high quality video games, but also to speed up the DL algorithms. One recent GPU from Nvidia, the 1080ti, takes a few days to build an image-classification system on top of an ImageNet dataset, which previously could have taken around a month.

If you are planning to buy hardware for running deep learning, I would recommend choosing a GPU from Nvidia based on your budget. Choose one with a good amount of memory. Remember, your computer memory and GPU memory are two different things. The 1080ti comes with 11 GB of memory and it costs around $700.

You can also use various cloud providers such as AWS, Google Cloud, or Floyd (this company offers GPU machines optimized for DL). Using a cloud provider is economical if you are just starting with DL or if you are setting up machines for organization usage where you may have more financial freedom.

Performance could vary if these systems are optimized.

The following image shows some of the benchmarks that compare performance between CPUs and GPUs :

Performance benchmark of neural architectures on CPUs and GPUs (Image source: http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture8.pdf)