Why GPUs Are so Crucial for AI?

Posted on Feb 2, 2024 by

GPUs have been called the rare Earth metals — even the gold — of artificial intelligence, because they’re foundational for today’s generative AI era. So, why do GPUs hold such a high standing in AI development?

The Introduction of GPU

What Is the GPU?

A graphics processing unit (GPU) is a computer chip that renders graphics and images by performing rapid mathematical calculations. GPUs are used for both professional and personal computing. Originally, GPUs were responsible for the rendering of 2D and 3D images, animations, and video, but now they have a wider usage range, especially in AI.

The Application of GPU

An electronic device with an embedded or discrete GPU can smoothly render 3D graphics and video content, making it suitable for AI vision applications. Modern GPUs are also adapted to a wider variety of tasks than they were originally designed for, partially because they are more programmable than they were in the past. Some of the most popular applications of GPUs include the following:

  • Accelerating the rendering of real-time 2D and 3D graphics applications.

  • Video editing and video content creation.

  • Video game graphics.

  • Accelerating ML applications such as image recognition and facial detection and recognition.

  • Training deep learning neural networks.

How Does GPUs Work?

GPUs work by using a method called parallel processing, where multiple processors handle separate parts of a single task. A GPU will also have its RAM to store the data it is processing. This RAM is designed specifically to hold the large amounts of information coming into the GPU for highly intensive graphics use cases.

For graphics applications, the CPU sends instructions to the GPU for drawing the graphics content on the screen. The GPU executes the instructions in parallel and at high speeds to display the content on the device -- a process known as the graphics or rendering pipeline.

GPU vs. CPU: Which Is More Suitable for AI?

A GPU contains hundreds or thousands of cores, allowing for parallel computing and lightning-fast graphics output. The GPUs also include more transistors than CPUs.

Because of its faster clock speed and fewer cores, the CPU is more suited to tackling daily single-threaded tasks than AI workloads. While the GPU handles more difficult mathematical and geometric computations. This means GPU can provide superior performance for AI training and inference while also benefiting from a wide range of accelerated computing workloads.

Why Is GPU Important for AI Today?

GPUs play an important role in AI today, providing top performance for AI training and inference. They also offer significant benefits across a diverse array of applications that demand accelerated computing. There are three key functions of GPUs to achieve these outcomes.

GPU Employs Parallel Processing

The AI model consists mainly of layers upon layers of linear algebra equations. Each equation reflects the possibility that one piece of data is associated with another. For their part, GPUs include thousands of cores, which are tiny calculators that work parallel to slice through the calculations that make up an AI model, providing efficient computing power for AI workloads. Furthermore, GPU cores are constantly upgraded to meet the changing needs of AI models.

Model Complexity and System Expansion

The intricacy of AI models is increasing at an astonishing rate of 10 times each year. The latest cutting-edge large language model (LLM), GPT-4, encompasses over a trillion parameters, serving as a measure of its remarkable mathematical density. GPU systems have adeptly met this evolving challenge by collaborating effectively. They effortlessly scale up to supercomputing levels, utilizing fast NVLink interconnects and the robust Quantum InfiniBand networks.

Broad and Deep GPU Software Stack

Since 2007, the ever-expanding NVIDIA GPU softwares have emerged to allow every aspect of AI, from advanced features to high-level applications. The CUDA programming language and the cuDNN-X deep learning library serve as the foundation for developers to construct software such as NVIDIA NeMo. It enables users to create, configure, and conduct inference on their own generative AI models. Many of these elements are available as open-source software, which is a must-have for software developers. Moreover, major cloud service providers are increasingly offering APIs and services on NVIDIA DGX Cloud.

The Contribution of GPU to AI Development

Stanford's Human-Centered AI group's recent report highlights the extraordinary 7,000-fold increase in GPU performance since 2003, with a remarkable 5,600 times greater price-to-performance ratio. GPUs have emerged as the predominant computing platform for accelerating machine learning workloads, significantly contributing to AI advancements in recent years. Notably, major AI models of the past five years have been trained on GPUs, exemplified by the success of ChatGPT, a large language model serving over 100 million users.

The Bright Future of GPUs in AI

AI's anticipated impact on the global economy is significant, with McKinsey estimating that generative AI could annually contribute between $2.6 trillion to $4.4 trillion across various sectors. Within this transformative landscape, GPUs stand out as crucial enablers, playing a pivotal role in optimizing performance and driving innovation.

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