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.
Origin Story
Back in 1994, Sony used the term GPU as part of the launch of its PS1. Yet, by 1999 NVIDIA popularized the term with the launch of its GeForce 256. Definitely, the launch of the GeForce 256 was extremely effective from a marketing standpoint. Indeed, defined as “the world’s first GPU” it created a category in its own right.
By 2006, NVIDIA launched CUDA, a general-purpose programming model. This accelerated the development of applications for various industries, from aerospace, bio-science research, mechanical and fluid simulations, and energy exploration.
Starting with a focus on PC graphics, NVIDIA has been the leader in the GPU space. Nowadays the company focuses on 3D graphics due to the exponential growth in the gaming market. At the same time, NVIDIA also represents the physical platform of entire industries. From scientific computing, artificial intelligence, AI, data science, autonomous vehicles, AV, robotics, augmented and virtual reality, NVIDIA really gives us an understanding of how tomorrow’s industries will evolve based also on the physical capabilities offered by its chips.
While GPU was initially applied primarily to gaming. Over the years, it became increasingly relevant to run AI/ML algorithms that required massive computing power. Thus, GPU has become the basis for the most promising industries of this decade: AI, autonomous driving, robotics, AR/VR, and more.
While NVIDIA is a GPU design company, it has been following a platform strategy. In short, it has leveraged hardware, and software (with its stack made of algorithms, and libraries) with the ability to serve several industries, and a few other promising industries of the future.
Value Model
As NVIDIA highlights, its vision is to “solve the world’s visual computing challenges.”
As they further specify:
“We make unique contributions to solving some of the world’s most stimulating technology problems – in industries ranging from gaming to scientific exploration.”
Value propositions
NVIDIA serves various enterprise industries, as such it has multi-faceted value propositions depending on the customer profile and industry. As we’ll see, for instance, in some of the industries NVIDIA serves the main customers are large corporations and big tech companies. In other cases, those can be small and medium-sized businesses. While the underlying technology is similar for all the use cases. Each of those customers will require different enhancements, specific to the industry.,
Gaming
Image Source: NVIDIA Quarterly Investor Presentation – 2021.
NVIDIA gaming GPU enhances the gaming experience with smoother, higher-quality graphics. Some of those applications include GeForce Experience (an application that optimizes the PC user’s settings).
Or perhaps NVIDIA RTX which provides “cinematic-quality rendering” for gaming.
Thus the core value proposition for enterprise clients in the gaming industry stands in the advanced graphics its GPUs can provide, combined with the software stack enhancements.
Professional Visualizations
Image Source: NVIDIA Quarterly Investor Presentation – 2021.
NVIDIA Professional Visualization platforms are used in various fields. From design, manufacturing, and digital content creation.
Even here, the value proposition is dependent upon the specific industry/use case.
For instance, for design and manufacturing, NVIDIA offers computer-aided ways to enhance the design and manufacturing workflow of products.
For digital content creators, this includes video editing and post-production, special effects for films, and broadcast-television graphics.
In this specific segment, NVIDIA solutions enhance productivity and improve the workflow for industries like automotive, media and entertainment, architectural engineering, oil and gas, and medical imaging.
Data Center
Image Source: NVIDIA Quarterly Investor Presentation – 2021.
When it comes to data center GPUs here it’s all about efficiency. The customer here is represented by large corporations such as Cloud Platforms (AWS, Alibaba, Azure, Google Cloud, IBM Cloud, and more).
NVIDIA follows a platform strategy, meaning that the hardware and software come together to offer a set of services and tools to enhance the ability of its GPUs. For instance, its software libraries, Software Development Kits, and APIs frameworks make it possible for deep and machine learning models to run smoothly.
And this indeed is critical for data cloud providers, which serve primarily the AI/ML industries. In fact, on top of data cloud providers like AWS (Amazon), Azure (Microsoft), Google Cloud, and IBM Cloud entire industries of small, medium, and large enterprises are built upon (the whole SaaS industry has been built on top of these providers, and also larger players like Netflix, Spotify, YouTube, and the major streaming services draw from cloud computing).
Therefore, the ability of the GPU to run on massive amounts of data is the key value proposition. NVIDIA here emphasizes high performance and efficiency. Its chips coupled with NVIDIA programming models (like CUDA and its acceleration libraries, APIs, and tools) make NVIDIA offering compelling for these enterprise customers.
Automotive
Image Source: NVIDIA Quarterly Investor Presentation – 2021.
Also when it comes to automotive, NVIDIA serves large enterprise customers providing them an end-to-end solution for autonomous driving with its DRIVE brand. This comprises a set of applications for autonomous driving, from self-driving in fully autonomous mode to co-pilot mode. As the company highlights the NVIDIA DRIVE “can perceive and understand in real-time what is happening around the vehicle, precisely locate itself on an HD map, and plan a safe path forward.”
Technological Model
Image Source: NVIDIA Quarterly Investor Presentation – 2021
NVIDIA does not manufacture its chips, but it designs them and it sources manufacturing to selected suppliers, thus reducing the risks associated with manufacturing.
Thus NVIDIA’s core competence and technological asset is in the design of chips together with the sourcing of materials and suppliers for the manufacturing of these chips.
Image Source: NVIDIA Quarterly Investor Presentation – 2021
Each of the industries that NVIDIA serves offers a set of products/chips with its own specifics.
The whole set of NVIDIA hardware products (Image Source nvidia.com/en-us)
The whole set of NVIDIA software products and tools enhancing the hardware, is in line with the NVIDIA “platform strategy” (Image Source nvidia.com/en-us)
Let’s see some of the advanced technological applications coming with the NVIDIA chips, that make these interesting to its customers.
Parallel Processing
In a patent called “Scheduling and managing compute tasks with different execution priority levels” NVIDIA describes “a technique for dynamically scheduling and managing compute tasks with different execution priority levels. The scheduling circuit organizes the compute tasks in groups based on priority levels. The compute tasks may then be selected for execution using various scheduling schemes, such as round-robin, priority, and partitioned priority. Each group is managed as a linked list of pointers to compute tasks, which are encoded as queue metadata (QMD) and stored in memory. The QMDs contain the state needed to perform a computing task. When a task has been selected by the scheduling circuit for execution, the QMD for a group is removed and transferred to a table of active computing tasks. Compute tasks are then selected from the Active Tasks table for execution by a streaming multiprocessor.”
Other core patents are used to enhance the performance of NVIDIA GPUs.
Data & Cloud Computing GPUs
NVIDIA is the major GPU provider for all the leading cloud computing companies.
As Amazon AWS explains “AWS and NVIDIA have collaborated for over 10 years to continually deliver powerful, cost-effective, and flexible GPU-based solutions for customers. These innovations span from the cloud, with NVIDIA GPU-powered Amazon EC2 instances, to the edge, with services such as AWS IoT Greengrass deployed with NVIDIA Jetson Nano modules.”
Also, the NVIDIA Tesla K80, P4, T4, P100, and V100 GPUs are on the Google Cloud Platform.
Source Image: Google Cloud Platform
Autonomous Driving
In a 2019 patent called “Training, testing, and verifying autonomous machines using simulated environments” NVIDIA explains how “physical sensor data may be generated by a vehicle in a real-world environment. The physical sensor data may be used to train deep neural networks … tested in a simulated environment…to control a virtual vehicle in the simulated environment or to otherwise test, verify, or validate the outputs of the deep neural networks.”
This in short offers a framework to develop autonomous driving systems and models.
From the patent “Training, testing, and verifying autonomous machines using simulated environments” it described the process of how data processed via the NVIDIA GPU can turn into deep learning models for autonomous vehicles.
In yet another patent called “Systems and methods for safe and reliable autonomous vehicles” NVIDIA highlights “autonomous driving is one of the world’s most challenging computational problems. Very large amounts of data from cameras, RADARs, LIDARs, and HD-Maps must be processed to generate commands to control the car safely and comfortably in real-time. This challenging task requires a dedicated supercomputer that is energy-efficient and low-power, complex high-performance software, and breakthroughs in deep learning AI algorithms. To meet this task, the present technology provides advanced systems and methods that facilitate autonomous driving functionality, including a platform for autonomous driving…the technology provides an end-to-end platform with a flexible architecture, including an architecture for autonomous vehicles that leverages computer vision and known ADAS techniques, providing diversity and redundancy, and meeting functional safety standards. The technology provides for a faster, more reliable, safer, energy-efficient and space-efficient System-on-a-Chip, which may be integrated into a flexible, expandable platform that enables a wide-range of autonomous vehicles, including cars, taxis, trucks, and buses, as well as watercraft and aircraft.”
NVIDIA is indeed the leading provider for cutting-edge companies like Tesla exploring the autonomous driving space.
The NVIDIA V100 used by Tesla (Image Source NVIDIA Corporate Website).
The NVIDIA V100 is used by Tesla (Image Source NVIDIA Corporate Website).
Distribution Model and Go-to-market strategy
NVIDIA’s core strategies comprise:
- Enhancing the main NVIDIA “accelerated computing platform” to solve more and more complex problems in significantly less time and more efficiently (with lower power consumption). The improvement of the core product is a key ingredient at an enterprise level, to make sure that large corporations keep renewing their contracts and commitments with NVIDIA.
- Extending the tools available within the NVIDIA platform to integrate them with enterprise partners, thus creating more synergies to lock in these customers in the long-term. As an example, back in 2020 NVIDIA Storefront in the Amazon AWS Marketplace. This comprises 20 NVIDIA NGC software resources (a catalog for GPU-optimized software for deep learning, machine learning) made available on AWS Marketplace. As those tools are optimized to run through NVIDIA GPUs for the cloud, this also prompts the sales for these chips.
- As NVIDIA highlights its AI platform strategy combines GPUs, with “interconnects, systems, its CUDA programming language, algorithms, libraries, and other software.” These tools and “extensions” enable NVIDIA GPUs to be much more performant and personalized according to the required use case.
- Continuous investments in research related to visual computing, enhancing the user experience for consumer entertainment and professional visualization applications.
- Keep improving the autonomous vehicle platform as a core bet on the future of automotive.
- Leverage enterprise custom developments to further refine the NVIDIA core technologies and thus making them even more relevant going forward.
Direct Sales & High-Touch
Given the level of expertise and the massive contract value that enterprise customers bring, this requires a high-touch sales process, where the sales team must have technical expertise in the product and industry it serves.
In fact, the sales team is not only technical (partner networks are assisted by engineering teams from NVIDIA) but it assists enterprise customers in designing, testing, and qualifying systems and technical workflow incorporating NVIDIA’s platforms.
Software Development Community
Therefore, engaging the software development community is also critical. Thus, both NVIDIA engineering and marketing teams engage with software developers to gather requirements, solve technical problems, and give access to new products before they become available to the market, to prompt them to develop AI frameworks, software kits and APIs optimized for the NVIDIA platform.
Education & Training
NVIDIA also runs the Deep Learning Institute provides in-person and online training for developers in industries and organizations to help them build applications leveraging the NVIDIA platform.
The Further Investment in Data Centers’ Growth
In April 2020, NVIDIA completed the acquisition of Mellanox for $7.13 billion. This acquisition strengthened the company’s investment in the data center segment, as Mellanox (a supplier of high-performance products for computing) might be used to further optimize data centers workflows, thus making NVIDIA offering more compelling for the large enterprise corporations leveraging its GPUs for cloud computing.
The Massive Investment in The AI Industry
In 2020, Arm announced the acquisition of Arm (semiconductor and software design company), in a $40 billion transaction. As announced by NVIDIA’s CEO:
“AI is the most powerful technology force of our time and has launched a new wave of computing…In the years ahead, trillions of computers running AI will create a new internet-of-things that is thousands of times larger than today’s internet-of-people. Our combination will create a company fabulously positioned for the age of AI.”
Financial Model
Image Source: NVIDIA Quarterly Investor Presentation – 2021.
NVIDIA’s financial model moves along two core segments (Graphics and Compute/Networking) and four main industries (gaming, data centers, professional visualizations, and automotive).
Post pandemic there was some interesting development for NVIDIA:
- A massive push from the market toward gaming, which revenue also accelerated for NVIDIA. Indeed by January 2021, Gaming revenue increased by 41% from January 2020. This was primarily a reflection of higher sales across desktop and laptop GPUs for gaming, and game-console SOCs.
- A slow-down in Professional Visualization revenue (13% decrease year over year).
- A strong push toward data center optimizations. As Data Center revenue increased by 124% (Internet traffic exploded through the pandemic, and most of the services offered run on top of cloud computing platforms). Here NVIDIA further accelerated this process by purchasing Mellanox (which contributed to 10% of the total company’s revenues by January 2021) and by ramping up its architecture for data cloud optimizations.
- Decreased revenue from the Automotive sub-segment (23% down year over year).
Revenue Model
When it comes to revenue generation we have two core segments (Graphics and Compute/Networking) and four sub-segments (gaming, data centers, professional visualizations, and automotive).
- Graphics – Graphics revenue grew by 29% by January 2021primarily driven by the GeForce GPUs and game console SOCs (system on a chip, usually run in portable gaming consoles such as Nintendo Switch).
- Compute & Networking is primarily driven by the data center sub-segment which substantially grew in 2020.
Cost Structure
When it comes to the costs associated with generating revenues, those primarily consist of semiconductor purchases, wafer fabrication, assembly, testing, packaging, and manufacturing support costs critical to shipping a great product.
Cash Generation
The company is cash positive thanks to its various profitable enterprise segments, while it was cash negative in the fiscal year of 2021, due to the purchases of Mellanox and the commitment to purchase Arm.
NVIDIA and the AI Revolution
NVIDIA is no longer a graphic computing company; it’s an AI inference machine…
In the latest earnings release, Jensen Huang, CEO and co-founder of NVIDIA, highlighted three key paradigm shifts, which we’re looking at right now, and that are fundamentally shaking the whole software industry from within:
- Paradigm Shift 1: The move from general to accelerated computing will dramatically improve energy efficiency and cost by 20x, improving speed by a step change magnitude too.
- Paradigm Shift 2: Generative AI as a new fundamental way of doing software and a new way of computing, thus redefining the whole cloud industry (from retrieval to inference).
- Paradigm Shift 3: A whole new industry from hardware to software; for the first time, a data center is not just about computing data and storing data; there is a new type of data center, which is about AI generation.
In the last couple of years, I’ve been explaining this in what I defined as a new “AI Business Ecosystem.”
Shift 1: The GPU isn’t just a new chip
In the last few years, I’ve been explaining, over here, how the hardware part has become a critical component of the success of prominent tech players like Google, Apple, Meta, and Amazon.
I explained it in detail in the AI Supply Chain piece, which I wrote back in 2020 and updated last year.
Both GPU and TPU are critical components of an AI supercomputer.
The GPU or graphic processing unit is a powerful chip that can perform parallelized computing, primarily used in gaming. It found itself to be the perfect architecture for the current AI paradigm.
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.
GPUs are critical components of AI Supercomputers, like Azure, which are powering up the current AI revolution.
Another version of the GPU is the TPU or tensor processing unit, which is similar to the GPU and is a powerful chip well-suited for training large language models.
The TPU was specifically developed by Google to be optimized around AI training.
A tensor processing unit (TPU) is a specialized integrated circuit developed by Google for neural network machine learning.
The TPU is a critical component of Google’s AI Supercomputer, which enables the company to develop large language models that are spurring up the current AI revolution.
When you stack up (a few years ago a few hundred) these GPUs, that’s how you get an AI supercomputer.
Of course, there is way more to it, as there are various hardware architectures to follow to build a powerful AI Supercomputer.
In addition to that, right now, an AI Supercomputer, to be competitive, needs to employ thousands of GPUs or TPUs.
But the key point is that the GPU isn’t just a new chip; it’s a software platform…
In the latest earnings, Jensen Huang explained why the GPU isn’t just a chip but way more than that. It becomes a software platform:
NVIDIA GPUs is like a chip. But the NVIDIA Hopper GPU has 35,000 parts. It weighs 70 pounds. These things are really complicated things we’ve built. People call it an AI supercomputer for good reason. If you ever look in the back of the data center, the systems, the cabling system is mind boggling. It is the most dense complex cabling system for networking the world’s ever seen.
The more Generative AI integrates into anything, the more the hardware part (for major tech players) becomes the critical moat.
In addition to that, the underlying cloud infrastructure, which serves the Generative AI paradigm, also shifts toward inferencing!
Indeed, as you can see from the above, NVIDIA’s revenue from computing more than tripled in a single year!
Can you guess why? It’s the new inference paradigm!
As Jensen Huang highlighted in the latest earnings release:
One, the amount of inference that we do is just off the charts now. Almost every single time you interact with ChatGPT, that we’re inferencing. Every time you use Midjourney, we’re inferencing. Every time you see amazing — these Sora videos that are being generated or Runway, the videos that they’re editing, Firefly, NVIDIA is doing inferencing. The inference part of our business has grown tremendously. We estimate about 40%. The amount of training is continuing, because these models are getting larger and larger, the amount of inference is increasing.
Paradigm Shift 2: AI Supercomputers turn the cloud into an AI Generation Factory
A vital element of the current AI landscape is the ability of large language models to be pre-trained in an unsupervised manner.
AI models can learn from large amounts of unlabelled/unstructured data by turning them into tokens.
Before, you needed hundreds of humans to manually and carefully curate that data to make it useful, in the first place, to develop AI models.
Fundamentally, the whole process translates raw materials (data) through accelerated computing to turn them into tokens, which is the language of Generative AI. These tokens are generated in a specialized data center, a supercomputing data center, or an AI generation factory.
That’s why everyone who wants to seriously compete in the new AI industry must look into producing its own chips. And to give you some context, to even be able to pre-train a large language model at the levels of the most advanced ones on the market today (like GPT-4 or Gemini 1.5), you need a few billion dollars to start!
Microsoft is reportedly developing its own AI chips for training large language models, a project that has been kept secret since 2019.
The company aims to reduce reliance on Nvidia, the current key supplier of AI server chips, and cut costs associated with deploying AI software.
For some context, OpenAI is estimated to need over 30,000 of Nvidia’s A100 GPUs for commercializing ChatGPT, and Nvidia’s H100 GPUs are in high demand, selling for over $40,000 on eBay.
This means a cost of almost a billion to start!
This is where we are in terms of resources needed to be competitive on the foundational layer…
And things are getting even more competitive, where even to pre-train a model, at the level of GPT-4, a company might need a few billion dollars in GPUs!
That is why Microsoft’s project, codenamed Athena, involves building in-house AI chips, which may be made available within Microsoft and OpenAI as early as next year.
Microsoft’s AI chips are not direct replacements for Nvidia’s, but they could significantly reduce costs for Microsoft’s AI-powered features in Bing, Office apps, GitHub, and more.
Microsoft has also been exploring the design of ARM-based chips for servers and potential Surface devices.
Other tech giants, including Amazon, Google, and Meta, have developed their own in-house AI chips, but many companies still rely on Nvidia chips for large language models.
In AI is eating software, I explained in detail how the quote from NVIDIA’s CEO, Jensen Huang, “Software is eating the world, but AI is going to eat software,” is playing out.
I also explained why this trend was a continuation and the last leg of a movement that Marc Andreessen emphasized in 2011 about Software eating the world.
That’s how you want to frame the current AI revolution!
In short, we’ll see in the coming decade what’s the maximum potential we can get by transforming anything into a software paradigm, where anything moves from dumb to smart, from static to dynamic, and from generalized to hyper-personalized!
And yet, there is a paradox to this revolution.
Paradigm Shift 3: The Emergence of A Whole New Industry (Generative AI Native)
As Jensen Huang emphasized, NVIDIA enabled, a whole new computing paradigm, generative AI, where software can learn, understand and generate any information from human language to the structure of biology and the 3D world.
He also highlighted how with accelerated computing:
You can dramatically improve your energy efficiency. You can dramatically improve your cost in data processing by 20 to 1. Huge numbers. And of course, the speed. That speed is so incredible that we enabled a second industry-wide transition called generative AI.
How does this translate at a consumer level?
You see consumer Internet services that are now augmenting all of their services of the past with generative AI. So they can have even more hyper-personalized content to be created.
Thus:
Generative AI really becoming a whole new application space, a whole new way of doing computing, a whole new industry is being formed and that’s driving our growth.
How will this happen?
Every company in every industry is fundamentally built on their proprietary business intelligence, and in the future, their proprietary generative AI.
Recap of the AI Revolution through NVIDIA’s Business Model Change!
- NVIDIA’s Paradigm Shifts: CEO Jensen Huang outlined three significant paradigm shifts in the software industry during NVIDIA’s latest earnings release:
- Shift 1: General to Accelerated Computing: Expected to enhance energy efficiency and cost by 20x over a decade, with significant speed improvements.
- Shift 2: Generative AI Revolution: Transforming software and computing, particularly in cloud infrastructure, from retrieval to inference.
- Shift 3: Emergence of a New Industry: Generative AI, enabled by accelerated computing, creating a new computing paradigm for various applications.
- GPU Evolution: GPUs, initially designed for 3D graphic rendering in gaming, have become pivotal in AI and machine learning. TPUs, like Google’s, specialize in neural network machine learning. Both are crucial for AI supercomputers.
- AI Supercomputers and Inference: Inference, the process of deriving insights from data, has seen exponential growth, especially with applications like ChatGPT and video generation. NVIDIA’s revenue from computing has tripled, largely due to inference.
- Cloud as AI Generation Factory: Large language models can now be pre-trained unsupervised, turning raw data into tokens. Companies like Microsoft are investing in developing their own AI chips to reduce reliance on Nvidia and cut costs.
- Future of AI Revolution: The AI revolution will see a transformation from static to dynamic and generalized to hyper-personalized software paradigms. Generative AI will become a new application space, driving industry growth and reshuffling the competitive landscape.
- Market Expansion Theory: Generative AI’s mainstream adoption will initially benefit incumbents but will eventually lead to the dominance of native generative AI companies, reshaping the competitive landscape over the next decade or two.
Business Model Highlights
- NVIDIA is a GPU design company following a platform strategy, where together with its chips the company provides the software toolkit to accelerate the performance of AI/ML applications built on top of its chips.
- The company serves various large enterprises (such as Gaming, Data Centers, Professional Visualizations, and Automotive). The strongest segment post-pandemic were gaming and data centers.
- The company’s technological model is built upon the further development of GPUs for developing AI/ML models for data cloud computing applications, as a toolbox built on top of NVIDIA’s products. While its bets are placed toward industries such as autonomous vehicles.
- The company accelerated its investments and product development toward AI and cloud computing with the acquisition of Mellanox and the initiated acquisition of Arm.
- NVIDIA’s main focus is on design, development, testing, and manufacturing support for its GPUs.
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