What is a GPU?

Graphics processing units (GPUs) were initially conceived to accelerate 3D graphic rendering in video games. However, more recently, they have become popular in artificial intelligence and machine learning (ML) contexts. In fact, GPUs are critical components of AI Supercomputers, like Azure, which are powering up the current AI revolution.

Understanding GPUs

GPUs are specialized processing cores that accelerate computational processes. Initially designed to process the images and visual data from video games, they have now been adapted to enhance the computational processes inherent to AI.

GPUs are effective in AI because they use parallel computing to break down a complex problem into smaller, simultaneous calculations. These calculations are distributed among a vast number of processor cores and are well-suited to machine learning and big analytics. Engineers sometimes refer to this type of computing as General Purpose GPU or “GPGPU”.

As the use of GPUs continues to evolve, research firm JPR predicts the GPU market will grow to reach a total of 3,318 million units by 2025. This represents a compound annual growth rate (CAGR) of 3.8%. 

The benefits of GPUs for deep learning

Why would an engineer not choose a fast, powerful CPU (central processing unit) over a GPU to support AI and ML operations? The answer can be found when one considers how each works:

  • Since CPUs handle most of the tasks for a computer, they need to be fast and versatile. They must also be able to switch between multiple tasks rapidly to support the computer’s general operations.
  • GPUs, on the other hand, were created to render images and graphics from scratch. This task does not require much context switching and, as we mentioned earlier, relies on breaking complex tasks into smaller subtasks. 

While the power of a CPU doubles theoretically every two years, GPUs instead focus their resources on a specific problem. This parallel computing strategy is known as the Single Instruction, Multiple Data (SIMD) architecture and enables engineers to efficiently distribute tasks and workloads across GPU cores.

In essence, GPUs are a more suitable choice since ML requires the continuous input of vast amounts of data to train models. The more data that is incorporated, the better such models can learn. This is particularly relevant in deep learning and neural networks where parallel computing is used to support complex, multi-step processes.

GPU examples for deep learning

Well-known GPU manufacturers such as AMD and Intel are present in the industry, but Nvidia is by far the dominant player.

Nvidia is a popular choice because its libraries (known as the CUDA toolkit) enable users to easily set up deep learning processes and access a dedicated ML community. The company also provides libraries for popular frameworks such as TensorFlow and PyTorch. 

Some popular Nvidia GPUs for AI and ML include:

  1. Nvidia Titan RTX – powered by Nvidia’s Turing architecture, the Titan RTX is one of the best GPUs for “entry-level” neural network applications.
  2. Nvidia A100 – powered by Nvidia’s Ampere architecture, the A100 Tensor Core GPU offers unmatched acceleration at scale to power data centers in high-performance computing (HPC), AI, and data analytics. The latest version of the A100 offers 80GB of memory and the world’s fastest memory bandwidth of over 2 terabytes per second.
  3. DGX A100 – an enterprise-grade solution designed specifically for ML and deep learning operations. The DGX A100 offers two 64-core AMD CPUs in addition to 8 A100 GPUs for ML training, inference, and analytics. Multiple units can be combined to create a supercluster.

Key takeaways:

  • Graphics processing units (GPUs) were initially conceived to accelerate 3D graphic rendering in video games. However, in more recent times, they have become popular in artificial intelligence contexts. 
  • GPUs are effective in AI because they use parallel computing to break down a complex problem into smaller, simultaneous calculations.
  • Nvidia is not only the only GPU manufacturer, but it is the dominant player. These GPUs are a popular choice because the company’s CUDA toolkit enables users to easily set up deep learning processes and access a dedicated ML community. 

Key Highlights

GPU Advancements in AI

  • Introduction: GPUs initially designed for video games, now widely used in AI and ML.
  • Parallel Computing: GPUs leverage parallel computing for faster processing.
  • Optimized for ML: Ideal for machine learning tasks due to their parallel architecture.

Nvidia’s Dominance in AI GPUs

  • Nvidia’s Leadership: Nvidia is the primary player in the AI GPU market.
  • CUDA Toolkit: Nvidia’s CUDA toolkit simplifies deep learning setup.
  • Framework Support: Provides libraries for popular frameworks like TensorFlow and PyTorch.

Advantages of GPUs in Deep Learning

  • Efficient Processing: GPUs focus on specific tasks, enabling efficient data processing.
  • SIMD Architecture: Utilizes Single Instruction, Multiple Data for parallel computations.
  • Continuous Input: Well-suited for ML’s need for continuous data input.

Popular Nvidia GPUs for AI/ML

  • Nvidia Titan RTX: Excellent choice for entry-level neural networks.
  • Nvidia A100: Offers unmatched acceleration for data centers and AI.
  • DGX A100: Designed for ML and deep learning operations; scalable supercluster solution.

Growth of GPU Market in AI

  • Market Prediction: GPU market to reach 3,318 million units by 2025.
  • CAGR: Compound annual growth rate projected at 3.8%.

GPU Impact on AI Advancements

  • AI Revolution: GPUs powering the current AI revolution.
  • Real-time Performance: Enables real-time AI processing.
  • Wide Applications: Used across industries for various AI applications.

Connected To NVIDIA

NVIDIA Business Model

NVIDIA is a GPU design company, which develops and sells enterprise chips for industries spacing from gaming, data centers, professional visualizations, and autonomous driving. NVIDIA serves major large corporations as enterprise customers, and it uses a platform strategy where it combines its hardware with software tools to enhance its GPUs’ capabilities.


The top individual shareholder of NVIDIA is Jen-Hsun Huang, founder, and CEO of the company, with 87,521,722 shares giving him 3.50% ownership. Followed by Mark A. Stevens, venture capitalist and a partner at S-Cubed Capital, who was part of the NVIDIA board in 2008 and previously served as a director from 1993 to 2006, with 6,258,803 shares. Institutional investors comprise The Vanguard Group, Inc, with 196,015,550, owning 7.83%. BlackRock, Inc., with 177,858,484, owns 7.10%. And FMR LLC (Fidelity Institutional Asset Management) with 158,039,922, owning 6.31%.

NVIDIA Revenue

NVIDIA generated almost $27 billion in revenue in 2023, compared to the same revenue value in 2022 and over $16.6 billion in 2021.

NVIDIA Revenue Breakdown

NVIDIA generated almost $27 billion in revenue in 2023, of which $15 billion came from computing and networking and $11 billion from graphics. Opposite to 2022, where of $27 billion in revenue, over $15.8 billion came from Graphics and $11 billion from computing and networking. With the explosion of AI, the computing segment has become the main driver of NVIDIA’s growth.

NVIDIA Revenue By Segment

NVIDIA generated almost $27 billion in revenue in 2023, of which over $15 billion came from competing & networking and $11.9 billion from graphics. NVIDIA, through its GPU, is powering up the AI supercomputing revolution, which is part of the current AI paradigm.

NVIDIA Profits

NVIDIA generated $4.37 billion in net profits in 2023, compared to over $9.7 billion in profits in 2022, and $4.3 billion in 2021.

NVIDIA Employees

In 2023, of 26,196 employees, 19,532 employees were engaged in R&D (74.5% of the total workforce). In 2022, 16,242 NVIDIA employees (72% of the workforce) were involved in R&D.

NVIDIA Revenue Per Employee

In 2023, NVIDIA generated $1,029,699 per employee, compared to almost $1.2 million in revenue per employee in 2022.

Connected Business Model Analyses


Generalized AI consists of devices or systems that can handle all sorts of tasks on their own. The extension of generalized AI eventually led to the development of Machine learning. As an extension to AI, Machine Learning (ML) analyzes a series of computer algorithms to create a program that automates actions. Without explicitly programming actions, systems can learn and improve the overall experience. It explores large sets of data to find common patterns and formulate analytical models through learning.

Deep Learning vs. Machine Learning

Machine learning is a subset of artificial intelligence where algorithms parse data, learn from experience, and make better decisions in the future. Deep learning is a subset of machine learning where numerous algorithms are structured into layers to create artificial neural networks (ANNs). These networks can solve complex problems and allow the machine to train itself to perform a task.


DevOps refers to a series of practices performed to perform automated software development processes. It is a conjugation of the term “development” and “operations” to emphasize how functions integrate across IT teams. DevOps strategies promote seamless building, testing, and deployment of products. It aims to bridge a gap between development and operations teams to streamline the development altogether.


AIOps is the application of artificial intelligence to IT operations. It has become particularly useful for modern IT management in hybridized, distributed, and dynamic environments. AIOps has become a key operational component of modern digital-based organizations, built around software and algorithms.

Machine Learning Ops

Machine Learning Ops (MLOps) describes a suite of best practices that successfully help a business run artificial intelligence. It consists of the skills, workflows, and processes to create, run, and maintain machine learning models to help various operational processes within organizations.

OpenAI Organizational Structure

OpenAI is an artificial intelligence research laboratory that transitioned into a for-profit organization in 2019. The corporate structure is organized around two entities: OpenAI, Inc., which is a single-member Delaware LLC controlled by OpenAI non-profit, And OpenAI LP, which is a capped, for-profit organization. The OpenAI LP is governed by the board of OpenAI, Inc (the foundation), which acts as a General Partner. At the same time, Limited Partners comprise employees of the LP, some of the board members, and other investors like Reid Hoffman’s charitable foundation, Khosla Ventures, and Microsoft, the leading investor in the LP.

OpenAI Business Model

OpenAI has built the foundational layer of the AI industry. With large generative models like GPT-3 and DALL-E, OpenAI offers API access to businesses that want to develop applications on top of its foundational models while being able to plug these models into their products and customize these models with proprietary data and additional AI features. On the other hand, OpenAI also released ChatGPT, developing around a freemium model. Microsoft also commercializes opener products through its commercial partnership.


OpenAI and Microsoft partnered up from a commercial standpoint. The history of the partnership started in 2016 and consolidated in 2019, with Microsoft investing a billion dollars into the partnership. It’s now taking a leap forward, with Microsoft in talks to put $10 billion into this partnership. Microsoft, through OpenAI, is developing its Azure AI Supercomputer while enhancing its Azure Enterprise Platform and integrating OpenAI’s models into its business and consumer products (GitHub, Office, Bing).

Stability AI Business Model

Stability AI is the entity behind Stable Diffusion. Stability makes money from our AI products and from providing AI consulting services to businesses. Stability AI monetizes Stable Diffusion via DreamStudio’s APIs. While it also releases it open-source for anyone to download and use. Stability AI also makes money via enterprise services, where its core development team offers the chance to enterprise customers to service, scale, and customize Stable Diffusion or other large generative models to their needs.

Stability AI Ecosystem


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