Thanks to GPUs, instead of waiting for days or weeks for a training algorithm to complete, you may end up waiting for just a few minutes or hours. This saves an enormous amount of time, but it also means that you can experiment with various models much more quickly and frequently retrain your models on new data.

Source – O’Reilly

You can often get a significant performance boost by merely adding GPU cards to a single machine. In fact, in many cases, this will suffice; you won’t need to use multiple machines at all, For example. You can typically train a neural network just as fast using four GPUs on a single machine rather than eight GPUs across multiple machines, due to the extra delay imposed by network communications in a distributed setup. Similarly, using a single powerful GPU is often preferable to using various slower GPUs.

Using a GPU-Equipped Virtual Machine

All major cloud platforms now often GPU VMs, some preconfigured with all the drivers and libraries you need (including TensorFlow). Google cloud platform enforces various GPU quotas, both worldwide and per region: You cannot just create thousands of GPU VMs without prior authorization from Google. By default, the worldwide GPU quota is Zero, so you cannot use any GPU VMs.

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