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Computing complex models for AI and ML projects requires extensive resources and training takes forever. GPUs allow for faster computations and makes training practical. However Setting up Tensorflow on Amazon EC2 P2 GPU Instance
Architecturally, the CPU is composed of just few cores with lots of cache memory that can handle a few software threads at a time. In contrast, a GPU is composed of hundreds of cores that can handle thousands of threads simultaneously. The ability of a GPU with 100+ cores to process thousands of threads can accelerate some software by 100x over a CPU alone. What’s more, the GPU achieves this acceleration while being more power- and cost-efficient than a CPU.

Dedicated GPUs re large , need lots of cooling and power and are just too expensive.

https://github.com/chandanmaruthi/TensorFlowOnGPU/blob/master/screenShotsTfGpu/GamingRig.jpg
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