The reason why even rent a GPU server for deep learning?

Deep learning can be an ever-accelerating field of machine learning. Major companies like Google, Microsoft, Facebook, among others are now developing their deep learning frameworks with constantly rising complexity and computational size of tasks which are highly optimized for parallel execution on multiple GPU rent a gpu and also multiple GPU servers . So even probably the most advanced CPU servers are no longer capable of making the critical computation, and this is where GPU server and cluster renting will come in.

Modern Neural Network training, finetuning and 3D rendering calculations usually have different possibilities for parallelisation and may require for processing a GPU cluster (horisontal scailing) or most powerfull single GPU server (vertical scailing) and sometime both in complex projects. Rental services allow you to focus on your functional scope more rather than managing datacenter, upgrading infra to latest hardware, monitoring of power infra, telecom lines, server health insurance and so on.

Why are GPUs faster than CPUs anyway?

A typical central processing unit, or a CPU, is a versatile device, capable of handling many different tasks with limited parallelis definitelym using tens of CPU cores. A graphical processing unit, or perhaps a GPU, was created with a specific goal in mind - to render graphics as fast as possible, which means doing a lot of floating point computations with huge parallelism making use of a large number of tiny GPU cores. That is why, because of a deliberately large amount of specialized and sophisticated optimizations, GPUs have a tendency to run faster than traditional CPUs for particular tasks like Matrix multiplication that is clearly a base task for Deep Learning or 3D Rendering.