One of the current leaders in GPU (Graphics Processing Unit) technologies are Nvidia graphics cards. The Nvidia corporation specializes in high-performance graphics processing hardware and software ranging from graphics cards to graphics drivers and graphics optimization utilities. Alongside AMD, Nvidia is one of the main providers of GPU hardware for both individual computer hobbyists as well as data centers and large-scale server operations. In this article, we will explore Nvidia graphics cards and discuss how they have reshaped the computing landscape in recent years.
What are Nvidia Graphics Cards?
A graphics card is a hardware device that connects to the computer motherboard to provide the computer with graphics processing resources. In common parlance, the terms GPU and graphics card are used interchangeably. Nvidia graphics cards are high-performance devices that enable powerful graphics rendering and processing for use in video editing, video gaming, and other complex computing operations.
One of the advantages of using graphics cards for complex computing tasks is that multiple GPUs can be installed in a single computer, lending a large amount of parallelized, highly efficient processing resources to the computer system. This is particularly beneficial for applications in the fields of cloud computing and artificial intelligence, making Nvidia graphics cards a valuable resource in modern computing applications.
Examples of Nvidia Graphics Cards
Throughout its decades of operation, Nvidia has produced many generations of GPU hardware, primarily in the form of graphics cards. With each generation the hardware is improved with more processing resources and new features to enhance graphics processing capabilities. Please see below for some examples of modern Nvidia GPU hardware.
GeForce – GeForce is a line of Nvidia GPUs designed for high-end PC gaming and other applications. Later iterations of the hardware provided less expensive alternatives as well as components for handheld and mobile devices. As of 2021, there have been seventeen generations of GeForce hardware, with the latest being the RTX 30 series of graphics cards.
A100 Tensor Core – The A100 Tensor Core GPU is a relatively new hardware offering from Nvidia that delivers extremely high performance processing capabilities for use in data centers and artificial intelligence. The A100 GPU provides up to 20 times higher performance over previous generations and allows for partitioning of resources into seven separate instances. This is useful for adjusting to meet changing resource demands. The A100 80GB model also offers a memory bandwidth of over 2 terabytes per second, making it perfectly suited for processing large datasets and computer models
The Impact of Nvidia Graphics Cards
The primary reason Nvidia has become a leader in graphics processing technology is that Nvidia uses an application programming interface (API) known as Compute Unified Device Architecture (CUDA). This API allows programmers to utilize the high number of parallelized processing cores present in the GPU hardware for use in machine learning algorithms. As a result of these advancements, Nvidia produced the first of the DGX line of GPU-enabled supercomputers in 2016. These machines were designed to allow users to more easily use deep learning by combining GPU hardware with integrated deep learning software. Since then, Nvidia has remained a powerful force in the fields of graphics processing and parallel computing.