NVIDIA, a reputation synonymous with cutting-edge technology and innovation, was founded just a few three many years ago in 1993.
From humble beginnings as a graphics chip designer focused on the gaming industry, NVIDIA has evolved into a worldwide leader in AI and high-performance computing.
NVIDIA was valued at ‘just’ around $100 billion in 2019. It’s now price some $2 trillion, placing it because the third largest company on the planet by market cap, below Microsoft and Apple and ahead of Saudi Aramco, Amazon, Google, and Meta Platforms.
NVIDIA was founded by Jensen Huang, Chris Malachowsky, and Curtis Priem, who shared a vision of revolutionizing computer graphics.
In the early Nineteen Nineties, the trio recognized the untapped potential of specialised graphics processors and got down to create an organization that will transform the burgeoning gaming industry.
One of the corporate’s early triumphs got here from a moment of serendipity.
In 1995, Sega was developing its next-generation gaming console, the Sega Saturn. Sega was on the lookout for a 3D graphics chip to power the console and had initially partnered with NVIDIA competitor 3Dfx Interactive.
However, a likelihood meeting between an NVIDIA engineer and a Sega executive at a conference saw NVIDIA display the corporate’s NV1 chip, which inspired Sega. Sega decided to make use of NVIDIA’s chip within the Saturn as a substitute of 3Dfx’s.
Interestingly, the NV1 chip utilized in the Sega Saturn was not a industrial success for NVIDIA within the PC market. The company’s subsequent product, the RIVA 128 (NV3), was its first successful PC GPU and laid the groundwork for its future dominance within the graphics card market.
Another early breakthrough got here in 1999 with the GeForce 256, marketed because the world’s first GPU.
This laid the inspiration for NVIDIA’s dominance within the gaming industry, and the GeForce line of GPUs quickly became a household name amongst gaming enthusiasts.
As NVIDIA continued to push the boundaries of graphics technology throughout the early 2000s, releasing increasingly powerful GPUs that delivered immersive gaming experiences, the corporate’s R&D established it as a frontrunner in parallel processing.
That would later be instrumental in NVIDIA’s future AI and high-performance computing success.
Beyond gaming: the rise of GPGPU and CUDA
While the gaming industry catalyzed NVIDIA’s early success, the corporate’s leadership recognized GPUs’ potential beyond graphics rendering alone.
In 2006, NVIDIA introduced Compute Unified Device Architecture (CUDA), a programming model that allowed developers to harness the parallel processing power of GPUs for general-purpose computing (GPGPU).
CUDA simplified the strategy of programming GPUs, enabling developers to put in writing code using familiar languages like C and C++. This opened up latest opportunities for NVIDIA in scientific research, oil and gas exploration, financial simulations, and medical imaging, thus opening a myriad of latest partnerships for NVIDIA.
This also showed how NVIDIA would grow to be fundamental in high-tech critical infrastructure, expanding its clientele beyond corporate buyers to governments and public institutions.
Semiconductors: a notoriously tricky market to beat
The semiconductor industry is notoriously complex and highly competitive, with only a handful of firms making their mark.
A key reason for the limited number of huge semiconductor manufacturers is the intense cost and complexity of the manufacturing process.
Semiconductor fabrication requires state-of-the-art facilities, often known as foundries, which might cost billions of dollars to construct and maintain.
These foundries must operate in extremely clean environments to forestall even the tiniest particles from interfering with the manufacturing process.
Additionally, the equipment used for semiconductor manufacture, similar to lithography machines, is very specialized and expensive, with some machines costing upwards of $100 million.
Together, this creates massive entry barriers for brand new industry players, which has helped keep NVIDIA at the highest of the pecking order despite competition from AMD, Intel, and Qualcomm.
The AI revolution
As the demand for AI and machine learning grew within the 2010s, NVIDIA was perfectly positioned to capitalize on this emerging trend.
With parallel processing R&D under its belt, the corporate’s GPUs became the popular hardware for training deep neural networks and powering AI workloads.
Recognizing AI’s immense potential, NVIDIA made strategic investments in the sector, collaborating with leading research institutions and technology firms to advance AI technologies.
The company’s early support for OpenAI showed its ability to tap into cutting-edge industries and take risks to expand its customer base.
NVIDIA also developed specialized compute modules, similar to the DGX series, specifically designed to speed up the training of huge language models (LLMs) and other AI architectures. These powerful systems quickly became the go-to hardware for AI researchers and developers worldwide.
And that’s a pivotally necessary point. When it involves high-end AI hardware, there may be NVIDIA, after which there are the others.
It’s an unusual setup, even in Big Tech. Google, Amazon, Meta, Apple, and Microsoft will not be so different whenever you boil down their core business units.
There are so few players within the semiconductor market, partly since it’s tough and partly because NVIDIA has made it so through strategic investment.
NVIDIA’s cohesive ecosystem also provides certainty to developers, as NVIDIA has grow to be so dependable. This is an organization free from the controversies of Big Tech, the leadership tussles, regulatory motion, and reliance on less tangible digital technologies like social media.
NVIDIA understands this, using software and hardware to tighten dominance over the AI ecosystem and create a collection of software tools and libraries that enhance go-to-market strategies for his or her customers.
The Omniverse and the Metaverse
Despite its manufacturing focus, NVIDIA has made a concerted effort to spearhead visionary software.
Building on its graphics, AI, and simulation expertise, NVIDIA introduced Omniverse, a platform for creating and simulating realistic 3D environments.
Omniverse leveraged NVIDIA’s cutting-edge technologies to enable collaborative design, engineering, and content creation, opening up latest possibilities for the automotive, manufacturing, architecture, and entertainment industries.
Companies could streamline design processes, optimize production lines, and test products in virtual settings before physical implementation by creating digital replicas of real-world objects and environments.
NVIDIA’s Omniverse platform quickly gained traction as a key player in developing the metaverse, a collective virtual shared space that blends physical and digital realities.
It was a sensible move, as why Meta plowed into their very own version of the Metaverse, NVIDIA saw that this might best suit enterprise clients reasonably than the buyer market.
NVIDIA’s role in generative AI
The rise of generative AI further solidified NVIDIA’s position as an AI powerhouse. This is the stage upon which NVIDIA truly established itself as some of the influential firms on the planet.
Generative AI involves training models on vast data to create latest content based on learned patterns and styles, similar to text, images, and music.
Recognizing its immense potential, NVIDIA introduced AI Foundations, a cloud-based platform that democratized access to state-of-the-art generative AI models.
AI Foundations allows businesses and developers to harness the ability of generative AI without the necessity for extensive in-house resources or expertise.
NVIDIA’s AI Foundations initially included pre-trained models, similar to NeMo for natural language processing and Picasso for image and video generation.
Again, this shows NVIDIA’s commitment to constructing an ecosystem reasonably than individual products. This is where they differentiate from other manufacturers, particularly competitors in semiconductor manufacturing.
NVIDIA is a one-stop-shop for cutting-edge AI development, offering hardware, software, and robust collaborations with cloud resources via Google, Microsoft, Amazon, and others.
NVIDIA’s GPU assault
In the midst of the generative AI boom, NVIDIA has vastly expanded its AI chip portfolio, introducing several groundbreaking processors designed to push the bounds of AI and computing technologies across various sectors.
Let’s take a more in-depth take a look at these chips and their contributions:
- A100 and H100: The H100 quickly became NVIDIA’s flagship for AI applications, clocking speeds 6x faster than its predecessor, the A100.
- HGX H200 GPU: Based on the Hopper architecture, the H200 introduces HBM3e memory, providing nearly double the capability and a pair of.4 times more bandwidth than its predecessor, the A100. It’s designed to double the inference speed on Llama 2, a 70 billion-parameter LLM, in comparison with the H100. The H200 is compatible with various data center configurations and is scheduled for release in early-to-mid 2024.
- GH200 Grace Hopper Superchip: The GH200 combines the HGX H200 GPU with an Arm-based NVIDIA Grace CPU. It’s aimed toward supercomputing applications to tackle complex AI and HPC applications. The GH200 is predicted to be utilized in over 40 AI supercomputers worldwide, including significant projects just like the JUPITER system in Germany, which is projected to be the world’s strongest AI system upon its 2024 installation.
- Blackwell GPU: Unveiled at GTC 2024, the Blackwell GPU is NVIDIA’s next-generation processor, succeeding the H100 and H200 GPUs. Touted because the world’s strongest chip by NVIDIA, Blackwell is designed specifically for the demands of generative AI. It offers a 30x performance increase over the H100 for LLM workloads with 25x higher energy efficiency.
Blackwell will likely be massive, with NVIDIA’s press release showcasing interest from a roster of Big Tech’s biggest names, similar to Microsoft’s Satya Nadella, Google and DeepMind’s Sundar Pichai and Demis Hassabis, OpenAI’s Sam Altman, and diverse others.
NVIDIA outsmarts the US government
NVIDIA’s success extends to its agile corporate strategy, governance, and response to market pressures. That includes swerving the US government’s efforts to curb high-end hardware exports to China, one among its biggest customers.
In August 2022, the US Commerce Department imposed licensing requirements on importing certain high-end GPUs, including NVIDIA’s A100 and H100 chips, to China and Russia. This caused its stock to temporarily dip by almost 8%.
The restrictions were designed to forestall these chips from getting used in military applications, similar to supercomputers and AI systems.
In October 2022, the US further tightened its export controls, introducing a sweeping algorithm that aimed to chop China off from certain semiconductor chips made anywhere on the planet with US equipment. These rules also restricted the export of US-made tools and components essential for chip manufacturing.
With each iteration of those rules, NVIDIA has found ways to navigate them by altering its chips to specifically evade export bans.
For example, in November, NVIDIA released three latest products – HGX H20, L20 PCle, and L2 PCle – based on NVIDIA’s powerful H100 chip but designed to comply with export restrictions.
These chips are less powerful than the previously restricted A100 and H800 models but still offer effective performance capabilities for AI tasks.
As noted by SemiAnalysis, “Nvidia is perfectly straddling the road on peak performance and performance density with these latest chips to get them through the brand new US regulations.”
According to the South China Post, an estimated 20 to 25% of NVIDIA’s data center revenue is generated from Chinese buyers, even despite ever-stricter export bans.
Robotics with Project GR00T and Jetson Thor
NVIDIA supports cutting-edge and emerging technologies through its enterprise robotics development platforms.
At the GTC 2024 conference, the corporate announced Project GR00T and Jetson Thor. GR00T intends to revolutionize humanoid robotics by providing a general-purpose foundation model that allows robots to learn from human actions and rapidly learn coordination, dexterity, and other skills.
Today is the start of our moonshot to unravel embodied AGI within the physical world. I’m so excited to announce Project GR00T, our latest initiative to create a general-purpose foundation model for humanoid robot learning.
The GR00T model will enable a robot to know multimodal… pic.twitter.com/EqN19Z3cXH
— Jim Fan (@DrJimFan) March 18, 2024
Jetson Thor, introduced alongside Project GR00T, is a brand new computing platform designed for these humanoid robots. It’s equipped with a next-generation GPU based on NVIDIA’s Blackwell architecture.
NVIDIA can be actively developing its Isaac Robotics Platform to support the event of sophisticated robots with natural asynchronous movement and dexterity.
NVIDIA’s financial performance and market dominance
NVIDIA’s success in gaming, AI, and high-performance computing translated into remarkable financial performance. In 2023, the corporate reported revenue of $26.9 billion, a staggering 61% increase from the previous yr.
The data center segment, which incorporates AI and high-performance computing, accounted for $11.2 billion, or 42% of the entire revenue, highlighting the growing importance of those areas for NVIDIA’s business.
Impressively, NVIDIA’s gaming segment continued to thrive, contributing $9.3 billion, or 35% of the entire revenue, demonstrating its ability to keep up its leadership within the gaming industry while concurrently expanding into latest markets.
NVIDIA’s financial success reached latest heights in the primary quarter of fiscal yr 2024, with revenue soaring to $13.5 billion, a formidable 88% increase from the previous quarter. The data center segment was the first driver, with record sales surpassing $10 billion.
This exceptional performance propelled NVIDIA’s market valuation past the $1 trillion mark in mid-2023, which was already extremely impressive – but inside just months, the corporate managed to double its market cap to the $2 trillion mark, where it sits today.
Will NVIDIA’s rise proceed?
The tech industry, on the entire, is experiencing a remarkable resurgence, with Alphabet, Meta, and Microsoft reporting impressive leads to 2023.
Alphabet, Amazon, NVIDIA, Apple, Meta, and Microsoft dominate the S&P 500 index, accounting for 9% of its sales, 16% of its net profits, and a few 25% of its market cap.
However, what truly defies market dynamics is that Big Tech grows and grows, with annual average growth generally falling between 13% and 16% for a decade or longer.
For instance, Alphabet’s annual average sales growth is a remarkable 28%, meaning they need to add $86bn in 2024 to sustain that, then $111bn in 2025, and so forth. It’s an exceptionally tough cycle to keep up.
NVIDIA’s revenue last yr was about $60 billion, a 126% increase from the prior yr. Its high valuation and stock price are based on that revenue and its predicted continued growth.
For comparison, Amazon has a lower market value than NVIDIA, yet made almost $575 billion in sales last yr.
This disparity shows the steep path NVIDIA must navigate to book large enough profits to justify its $2 trillion valuation, especially as competition within the AI chip market intensifies.
But despite that, analysts have increased their price targets for NVIDIA, with UBS analyst Timothy Arcuri recently raising it to 1,100 from 800, citing the potential for NVIDIA to capture demand from global enterprises and governments with Blackwell.
However, some consider that NVIDIA’s stock chart shows signs of weakening. Indeed, it’s extremely high for an organization that has yet to ship the overwhelming majority of its A100 and H100 orders.
Looking ahead, the longer term of massive tech and NVIDIA’s growth stays uncertain. While the expansion potential is immense, firms must also contend with the potential for a cooling AI love affair, technological limitations, and regulatory hurdles.
Traffic to ChatGPT, for instance, has dropped off since May 2023, and a few investors are slowing down their investments in AI-related firms. There is a few concern that generative AI has come on too fast, quickly obtaining a peak that it’d struggle to surpass within the near future.
Moreover, brute force computing is resource-heavy, each for NVIDIA and its customers. When summed across global AI workloads, chips need constant power that rivals the capability of small nations.
And it’s not only power, but water too, which is pumped through data centers to the tune of billions of gallons a day. Natural resources required to construct high-end AI hardware, similar to rare earth metals, are also not limitless.
NVIDIA could be very much conscious of the industry’s energy challenges, hence why their latest chips are considerably more energy efficient.
At GTC 2024, Huang said, “Accelerated computing has reached the tipping point. General-purpose computing has run out of steam. We need one other way of doing computing in order that we will proceed to scale, in order that we will proceed to drive down the price of computing, in order that we will proceed to devour increasingly computing while being sustainable.”
At least Huang is realistic about these issues.
You can make certain that NVIDIA will channel more funds into unlocking energy-efficient AI growth that rids the industry from the shackles of brute-force accelerated computing.
