The card you see right here is what millions of gamers worldwide rely on worldwide to get better gaming performance, introducing, Nvidia Geforce. Nvidia has been dominating the market for nearly 30 years.
Being THE choice for hardcore and casual gamers alike, ever since the release of the very first GPU GeForce 256, Nvidia paved the way to the very top. Compared to their latest offering, the 24 GB RTX 4090 has reached a milestone in gaming performance, which gets overshadowed by the 40 GB Nvidia A100 PCIe! But now, they’re actively working on broadening their horizons and venturing into uncharted territories.
As you already know, ChatGPT is now a very interesting topic and most probably the most revolutionary thing since the emergence of iPhones. As expected, the AI Startup after this has gone nowhere but up, and as the main powerhouse behind large language models such as ChatGPT, Nvidia is effortlessly reaping their rewards for their strategic investment, even during inclement situations.
So, how exactly is Nvidia maintaining to prosper considering the current situation, and more importantly, how were they able to conquer the AI chip Industry? Let’s find out.
TSMC and Nvidias Vision
One of the core vulnerabilities of this California-based chip designer is, that they rely solely on Taiwan Semiconductor Manufacturing Company, mostly known as TSMC in order to make pretty much ALL of their chips. Take the US-China relationship and its impact on TSMC in general, and you’ll notice how they are hanging by a thread, or at least appear to be. However, this wouldn’t be the first time Nvidia was put through a stress test. Jensen Huang, the CEO and Founder of Nvidia takes a form of thrilling excitement, as he keeps gambling with impossible odds with the company while being humble and saying “Every Company makes mistakes, and I make a lot of them.” Especially at the beginning, Nvidia was a shell of a company that had competitors who made them feel out of their league. The thing is, Nvidia had vision. They knew what they were about to achieve powers more than just gaming, and it was evident in the coming years.
Currently holding its rank as one of the top ten most valuable companies on a global scale, Nvidia is one of those rare companies that is still run by their founder after 30 long years. In the early 90s, Huang was accompanied by fellow engineers Chris Malachowsky and Curtis Priem, who all shared the same dream of enabling PCs with three-dimensional graphics which was popularized through films like Jurassic Park. Turning the clock back 30 years and today would’ve been impossible to imagine, but Nvidia guaranteed and owned the future of computing and charted the path of how software should be run.
Back then, it was generally assumed that the CPU was the way to go for the future of computing, and so it was for a long time. However, there did exist a number of resource-heavy applications that just could not work properly without some form of acceleration, and from that, straight out of a condo in Fremont California, in 1993, Nvidia was born.
The word Nvidia has two distinct origins, N V was an acronym for Nvidia which stood for “Next Version”, alongside the Latin Word “Invidia” which stood for Envy. Envy, cause they had a vision of revamping the computer system to such great heights that their competition would be “Green” with Envy. Clever, don’t you think? Currently, Nvidia’s primary business remains GPUs with competitors like Intel and AMD. There were a lot more GPU makers back in the day, but Nvidia and AMD remained the sole survivors due to Nvidia’s contribution to the Software Community. At this point, Nvidia is confident that they are not merely in the chip business, but this is a business of figuring out how things end to end.
Nvidia’s Uprising and entry into AI
Nvidia designed its first high-performance chip in 1997. Designed, not manufactured mind you because Huang preferred Nvidia to become a chip company that kept capital expenditure at the bare minimum by outsourcing the overwhelming expenses it’d take TSMC to come up with the chips. In 1999, when Nvidia laid off almost all of their workers and was ready to declare bankruptcy, they released their trump card, the GeForce 256, the first programmable graphics card that allowed a PC to render custom shading and lighting effects. Success kept rolling in as by the year 2000, Nvidia was the exclusive graphics provider for the very first Xbox from Microsoft. Fortunately for Nvidia, Xbox was revealed at the same time they invented “programmable shader”, which is the standard as to how computer graphics are made today!
In 2006, Nvidia released a software toolkit called CUDA, which was Nvidia’s dawn into the AI structure, basically acting as a programming model that overhauls the GPU, basically allowing it to focus on a given task from multiple directions and proceed to solve said task all at once, greatly multiplying the speed and power.
However, in 2010, Nvidia tried to dabble in the smartphone business with their Tegra lineup of processors and quickly realized how this wasn’t a place for them to thrive. Fast forward to 2020, Nvidia was able to bag a 7 million dollar deal to acquire Mellanox but had to abandon a 40 billion dollar bid to acquire ARM, which resulted in significant and impactful regulatory changes. Today, Nvidia has over 26,000 employees, a newly built polygon-themed headquarters situated in Santa Clara, California, and is in possession of over BILLIONS of chips that are used for far more than just mere graphics processing. Things…like data centers, cloud computing, and of course, artificial intelligence.
Every chip Nvidia made from that point forward focused on Artificial Intelligence. Back then, the Deep Learning team from Nvidia only had Bryan Catanzaro as the first and only employee, which now consists of 50 people and counting. For over ten years, Wall Street has been asking Nvidia why it is investing in something no one’s using. After the emergence of CUDA, they got their answer.
So, how good is Nvidia’s AI for real-world applications? Imagine healthcare where faster drug discovery and DNA sequencing are as easy as a few taps on the PC. There are case files that highlight a 13-year-old boy who was diagnosed and administered through the process to have a heart transplant and he’s thriving today, alongside a three-month-old baby with epileptic seizures who was prescribed Anti-Seizure medication. Imagine Artwork such as creations by Refik Anadol that cover entire buildings! And crypto? Well, because of the whole crypto-currency situation, Nvidia caused a severe backlash among gamers by pricing their new 40-Series GPUs a lot higher in comparison. However, Nvidia was still able to beat the expectations, Kudos to the AI as tech giants like Google and Microsoft fill their data centers with thousands of Nvidia A1000s.
ChatGPT utilizes Nvidia’s iconic DGXA100 Server Board that comes with a price tag of nearly $200,000 dollars! For the price, you’re getting 8 Ampere GPUS that work together that enable ChatGPTs insanely fast and eerily human-like replies. Hopper, the next stop from Ampere, has already started to ship. While some are using it for generative AI for real-time translation and instant text-to-image rendering, there’s also the risk of generating dangerous deep fake audio, videos, and texts.
While AI and ChatGPT was generating a noticeable amount of buzz for Nvidia, this wasn’t Huang’s ONLY focus, as Amazon and others use Nvidia to power their robots in their warehouse and run simulations to replicate to flow of millions of packages each day. Fun fact, these robots are being powered by Nvidia’s previously flopped Tegra chips from their mobile phone era. Now these chips, apart from powering robots in Amazon, are also used in Tesla Model 3s from 2016 to 2019.
The RTX Lineup is Nvidia’s biggest move in graphics with their new Ray-Tracing Technology, which basically simulates the pathways of light with generative AI. With Ray-Tracing support for over 300 games and counting like Cyberpunk 2077, Fortnite, and Minecraft, Ray Tracing is also what makes simulations so much better where they can model how objects would behave in real-world situations. And with DLSS 3.5 On the Horizon, Nvidia appears to be on a solid path to success. All of this is a part of Nvidia called “Omniverse” which is shaping up to be Nvidia’s next big project.
With over 700 customers who are trying out Omniverse right now ranging from the automobile industry to logistics warehouses to wind turbine plants, Nvidia is pretty hyped up regarding their overall progress. All of these represent Nvidia’s core objective of merging computer graphics, AI, Robotics, and Physical Simulation, in one package.