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The GPU (graphics processing unit) is a computer chip that provides thousands of processing cores, specially designed for maximised efficiency in rendering computational video rendering or display.
CPUs mainly handle the processing functions of the computer, slow but able to undertake complex computations. Whereas the GPU specialises in less complex operations at quicker speeds.
However, GPUs and CPUs are able to be used together through hybrid or heterogeneous computing, GPUs can accelerate applications running on the CPU through offloading specific compute-intensive and timely parts of the code.
Applications are able to run quicker due to parallel processing – as a CPU has fewer cores and a GPU has hundreds of cores, they are able to work together to crunch calculations. This incorporation of parallel architecture is why hybrid computing is fast and powerful computationally.
Ø Disruptive growth of workloads
Ø Complexity of computation
Ø Predicting the exact amount of compute capacity needed for each workload
Ø Keeping TCO in check through optimum utilisation
With technology trends shifting towards artificial intelligence, there has been a shift from CPU to GPU computing as AI has been made very useful for a wide range of computational tasks.
Ø AI Machine Learning
Ø AI Deep Learning
Ø Video or Animation Rendering
The significant increase of data for businesses today proves to increase the demand of Deep Learning, AI and ML capabilities to improve their operations. These capabilities look to streamline operations through automating customer services, perform maintenance trends and analyse massive amounts of data.
The NVIDIA RTX A6000 is described to be the most powerful workstation NVIDIA GPU, providing real-time ray tracing, AI-accelerated compute and professional graphics rendering.
The NVIDIA GeForce RTX 3090 is a GPU with massive performance, provided by its enhanced Ray Tracing cores, Tensor cores and key multiprocessors.
The NVIDIA Ampere architecture CUDA cores provide double speed processing of single-precision floating point (FP32) throughput compared to the previous generation. Providing significant performance improvements to graphics workflows such as 3D modelling and compute for workloads such as desktop simulation.
2nd Gen RT Cores
With 2x the throughput of past generations providing significant ray traced rendering performance, the 2nd gen RT Cores provide incredibly quick speed ups for workloads such as rendering complex professional modelling.
3rd Gen Tensor Cores
The new Tensor Float 32 precision delivers up to 5x the training throughput compared to previous generations in accelerating AI and data science models. Purpose-built for deep learning matrix arithmetic at the heart of neural network training and inferencing functions.