Can you guess what would be the ultimate dream of a company in the 21st century? To join the trillion-dollar club! Even if you aren’t a market maven, you would have heard this news for sure. “Nvidia’s entry into the elite club of five US firms- Alphabet Inc., amazon.com Inc., Aramco, Apple Inc., and Microsoft Inc.” Here, elite firms refer to the trillion-dollar valued companies. This was a notable event in the Nvidia success journey. Well, let’s delve into it.
Synopsis of Nvidia Success Journey
Do you remember how ChatGPT became the talk of the town recently? Artificial Intelligence, Auto-GPT, Open AI, etc. are some of the names that you know. But we can bet that you didn’t know that Nvidia was the core of AI. Wasn’t Nvidia a gaming company? Yes! It’s true. Then how did it associate with AI? Because the supercomputer built by OpenAI included systems with-
- 285,000 CPU cores
- 10,000 GPUs
- 400 Gigabits per second of network connectivity
- GPU servers
And guess who built all of the above stuff? Nvidia! The generative AI runs on powerful chips called GPUs (Graphics Processing Units). Over 80% of GPUs are produced by Nvidia.
Nvidia Corporation
It is a multinational technology company based in California. It mainly creates the following-
- GPUs (Graphics Processing Units)
- APIs (Application Programming Interfaces) for data science and high-performance computing
- System on a chip unit (SoC) for mobile computing and automotive markets
- Tegra mobile processors for smartphones, tablets, vehicle navigation, and entertainment systems
Nvidia is the major manufacturer of GPUs that are an essential part of generative AI systems. Thus, it has a supreme advantage over other chip makers like Intel which is primarily focused on manufacturing the CPUs. Although Central Processing Units are designed for routine computing operations. Like running applications and other operating systems. But GPUs are specialist processors that are highly optimized for parallel computing. This makes them perfect for graphics rendering, scientific simulations, and speedy AI computations.
Success Journey of Nvidia
Although it was founded 30 years ago, its AI journey began 17 years ago in 2006. All thanks to Nvidia co-founder Jensen Huang’s far-sighted visionary goals. He vested in added functionality for Nvidia GPU chips long before the AI revolution. Fortunately, this investment paid off with flying colors. Let’s look at the latest news.
On 30th May 2023, the market valuation of Nvidia crossed $1 trillion, making it the 6th trillion-dollar valued company. Also, its market capitalization touched $990.7 billion. The investor piled into the chipmaker firm. Eventually, its shares were up 5.7% two days ago. Obviously, this was not an overnight success! Are you ready to dive into the Nvidia Success Journey?
Let’s begin!
1993: A Stepping Stone
1990 was an era when numerous engineers, scientists, and entrepreneurs realized the upcoming revolution of the internet and artificial intelligence. Jensen Huang, a Taiwanese-American electrical engineer was one of them. Along with Curtis Priem and Chris Malachowsky, Huan founded the company to excel in the next wave of computing. They named it “Nvidia.” This same thing was done by Microsoft and Google too!
So how was Nvidia different from them? You may ask.
Jensen Huang was triggered by the idea of graphics-based computing because it was far much superior to general-purpose computing. The rise of video games was also a factor that acted as a catalyst to invest a heavy amount in the R&D of Nvidia. Especially for solving tough computational problems. Thankfully, it received substantial funding from Sequoia Capital too. In January 1999, Nvidia went public and got listed at $12 per share! Meanwhile, it acquired numerous start-ups and companies like Mellanox Technologies, 3dfx Interactive, MediaQ, etc.
Timeline: 1993-2006
| Calendar Year | Nvidia’s History | Description |
| 1993 | The foundation of the company Nvidia | Nvidia was founded by Jensen Huang, Chris Malachowsky, and Curtis Priem |
| 1994 | First strategic partnership with SGS-Thomson Microelectronics. | To manufacture a single-chip graphical user-interface accelerator. |
| 1995 | Nvidia launched its first product- “NV1” | The PCI card featured a 2D/3D graphics core based on quadratic texture mapping |
| 1996 | Unveiled first Microsoft DirectX Drivers | With support for Direct3D to render 3D graphics where performance is critical |
| 1997 | Launched RIVA 128, the world’s first 128-bit 3D processor | More than 1 million units were sold in the first four months. |
| 1998 | Partnership signed with TSMC | TSMC is a Taiwan Semiconductor Manufacturing Company to assist in the production of Nvidia products. RIVS TNT- a multi-texturing 3D processor. |
| 1999 | Nvidia invents the GPU (Graphic Processing Unit)- GeForce 256 (World’s first GPU processor) | GeForce 256 was capable of processing 10 million polygons per second |
| 2000 | Nvidia acquires Graphics pioneer 3DFX; GeForce2 Go (the world’s first notebook GPU) was launched | Nvidia provides graphics processors for its 1st Xbox gaming console. |
| 2001 | Nforce platform launched | Nvidia entered the integrated graphics market with Nforce |
| 2002-03 | Sold 100 million processors and acquired Media Q (wireless graphics and multimedia technology) | Nvidia became the fastest-growing company in the US. |
| 2004 | Nvidia launched SLI technology, helped NASA to transmit the data through a rover on Mars, and teamed up with Blizzard Entertainment to release “World of WarCraft” in 3D graphics. | SLI technology allowed the linking of multiple GPUs to increase the graphics power of a single machine |
| 2005 | Developed processor for SONY PlayStation 3 | A gaming console and acquired ULi Electronics (Taiwan-based company) |
2006: Beginning of AI Journey
So, this is the year when Nvidia began its AI journey. By that time, not only it shipped 500 million graphics processors but also unveiled CUDA. It means Compute Unified Device Architecture. The team began adding CUDA to every GPU. This was a revolutionary architecture for GPU computing in general purpose. CUDA enabled scientists and researchers to harness the parallel processing capabilities of GPUs. It helped them to tackle their complex computing challenges!
CUDA aided in the dramatic evolution of Nvidia. How? Because no other chipmaker was able to integrate hardware and software seamlessly as Nvidia did. It was more successful than its peers and competitors to turn computer code into realistic images. This feature was no less than bliss for computer gamers.
Current Scenario: AI Wave and Nvidia’s Business Growth
Nvidia’s move to push data center servers and artificial intelligence processing proved to be quite fruitful. Within a decade, its business revenue increased from $300 million to $15 billion. It won numerous successive orders from tech giants. Because Nvidia’s graphic chips were able to handle AI workloads much better than other contemporary processors. Also, its chips were more expensive than other companies’ CPUs on a per-unit basis. This led to better margins.
Almost every sector is doomed in the pandemic. Then how did Nvidia survive? You may ask. First of all, you must know that the IT sector bloomed in a pandemic where other sectors were facing the wrath of lockdowns. Work-from-home and online services became the new normal. Being a part of the IT sector, Nvidia blossomed too! Its business rapidly expanded during that time due to the surge of cloud adoption.
Prime Revenue Sources
Can you guess Nvidia’s major source of revenue? Over 70% of revenue directly comes from large language models (LLMs) and generative AI tools. In FY22, it earned a revenue of $8,073 million.
Why do Businesses in Today’s World want to have their own AI?
By utilizing Artificial Intelligence, you can easily understand and communicate with customers, streamline processes, and increase operational efficiency. This in turn increases revenue and helps your business grow. Sounds complicated? Don’t worry. Let me explain it in simple terms. Imagine you are a streaming platform like Netflix. How would you figure out which TV show is likely to get more clicks for people or which show is binge-worthy? This is the point where you would need an AI model to predict it.
Or if you are an automobile company keen to develop intelligent driving systems to help drivers or smart vehicles. Here too, you will need AI.
So, when the need for AI arises, the demand for Nvidia’s GPUs increases automatically. This factor helped it to achieve grand success and enter the trillion-elite club!
What does the business analysis say?
According to analysis, Nvidia is far ahead of others in the race for AI chips. The prime reason for this is, its software effectively utilizes GPU hardware’s features for AI applications. That’s great! But why aren’t other companies able to produce superb AI chips like Nvidia? Because it is not that easy to replicate a decade’s hard work in a few months. As you have read above, Nvidia began its AI journey back in 2006. To produce the chips of the same quality, you would need to engineer all the software, algorithms, and optimization of frameworks.
Being a dominant player in the GPU and chip market, its stock’s value tripled in the last eight months. Tegra mobile processors for smartphones, tablets, vehicle navigation, and entertainment systems are aiding Nvidia to dominate the mobile computing market.
Final Thoughts
It is time to wrap up! On May 30th, 2023, the trillion-dollar elite club of US companies welcomed a new member- Nvidia. Its share price shot up over 5% and its valuation rose to $1 trillion. The surging demand for GPUs along with the advancement of AI are the principal reasons for this. Last year, 10,000 GPUs of Nvidia were used in ChatGPT and the supercomputers of Microsoft. It is an essential component of OpenAI’s ChatGPT and Google’s Bard. Thus, the AI boom played a crucial role in the $1 trillion valuation of Nvidia. This was all about the Nvidia Success Journey!
