AI Existence Depends On NVIDIA
The Backbone of Every Tech | Beyond GPU | AI Universe
Do you know that the world’s largest tech companies, like Microsoft, Google, Apple, and Meta, are afraid of the Nvidia company’s success? Wait, Nvidia is simply a graphics card manufacturer; why should anyone be afraid of it? What is the reason for the fear? Exactly, so why I am saying this? If you want to know the answer like me then let’s find it out in my todays article.
2006 AI
In 2006, Nvidia released CUDA technology, which improved the processing capability of its graphics cards by 3.6 times. Nvidia realized the power of AI when developing CUDA technology. Many things are now possible thanks to CUDA, including high-quality architects, visualization, 3D graphics, 3D art, and editing.
After the development of CUDA technology, Nvidia realized the importance of AI. As a result, Nvidia continued to develop processors with complicated AI programs within all of its semiconductor chips.
AlexNet
I think you are confused about this term AlexNet, don’t worry you are not the only one but I was also got quite surprised when I learned about it. So what is this AlexNet?
In simple words, Alexnet is an accurate neural network. Alexnet won the Large Scale Image Recognition Contest in 2012. The interesting thing here was that the codes of Alexnet were written with the help of CUDA technology. The popularity of Alexnet has sparked a rise in interest in AI.
My friends, if you do not then make sure not to forget it from this day onwards, because this is the starting point of the AI boom.
Deep Learning
From these events, Nvidia understood that this type of parallel processing can be used for deep learning in the future. With this deep learning, any robotic device or computer can instantly become capable of learning about the actions taking place around it. Seriously guys can you believe it, Nvidia was planning to develop something which can perform all the operations without a coding order from us. Basically, an AI which has its own brain.
In today’s time, commands have to be given to the robot or machine from the computer; however, there will come a day when the machine will do all of the work without the command.
As I and everyone know AI processors, machine learning, deep learning, and neural networks all require hardware which can process the task and Nvidia is the one who is developing this kind of hardware technology.
Tech giants like Microsoft, Google, OpenAI, and Meta want to stay ahead in the AI race, and they are competing with their AI tools. To win this AI race, the most advanced tools must be developed than the opponents; only then will someone be able to win. However, this competition is impossible without using Nvidia’s technology. Only Nvidia’s hardware will allow humanity to advance to the next level through the development of new technology.
Core Point
When a developer tries to develop a new technology or tool, he requires hardware that can handle and execute heavy tasks throughout development. When developers develop games, tools, or any other type of product, users want high-performance hardware to utilize it. So if the users want to play those high-performance games, and use tools, and software then at that time, they also need high-end hardware which can be capable of running those tasks.
Due to the development of high-end things the demand for high-quality games, movies, pictures etc will also increase. When we think about all of these things which could not be possible without the high-tech GPU then we understand the true value of the GPU.
In today’s time, Nvidia has become the king of this entire market. Yeah, I know there are also other companies who make GPUs like ASUS, Gigabyte, and many more but Nvidia’s technology is much more advanced than any of them. So on this basis, it’s not completely wrong to tell that Nvidia has become a King and dominates the entire GPU market.
Imagine that during the AI revolution, every company will need Nvidia GPU for development or deployment.
2010
Bryan Catanrazo works as Nvidia’s Vice President of Deep Learning Research. He was asked many times in the interview (indirectly), “Why Nvidia is investing money in AI?” these questions were asked before the AI boom. At the time, Nvidia was being criticized by everyone for spending money foolishly. People raised questions on social media like, “What will a graphics card manufacturer do in AI?”
People believe that Nvidia started working on AI research around 2014–2015, however, the company really began researching it in 2006. Nvidia started experimenting with AI and building technology in 2006.
When the AI boom arrived in 2016, people became aware of what Nvidia had been working on AI since 2006. For Nvidia, 2016 turned out to be an important turning point. The positive impact of spending a lot of money on AI was seen in 2016.
2016
The crypto bullrun began in 2016 when people were trying to mine Bitcoin and Ethereum. You will also be aware that mining Bitcoin and Ethereum requires a significant amount of GPU computing power. People were purchasing graphic cards with the highest hash power (the more the Hashpower, the faster the coin mine). During this period, Nvidia’s graphic cards were sold in full swing and their cards were also giving good Hashpower.
Many mining companies arrived in 2016–2017 to start large mining farms. The graphics card is an important component in the mining rig setup. Because of the widespread usage of Nvidia graphics cards in mining rigs, demand began to skyrocket. Not only did Nvidia’s graphic cards sell more, but demand for other companies’ graphic cards also went up.
Demand
Normally, gamers buy one or two graphics cards at a time, but cryptocurrency miners buy 10, 20, or 40, of such cards all at once. Graphic cards began to be carried around by more people, and the market’s demand for them rose quickly, but there were not enough graphic cards in the market, which led to a rise in price.
Profit & Stock
The price of Nvidia’s graphics cards rose, as did the company’s profits. Nvidia’s earnings climbed from 600 million in 2016 to 1.5 billion in 2017. The profitability of the business has influenced Nvidia’s stock price, which has risen from $53 in 2016 to $160 in 2017.
But here, I’d like to point out one thing that the demand for graphics cards rises during a bull market. At present, due to the bear market, the demand for mining is not increasing. Many of you must be thinking that will the era of the mining return? Yes, why not! as long as the crypto market exists it will come.
According to the prediction of many experts, when the bull run will begin in 2024, the demand for graphic cards may raise again, but this is just a probability.
Nvidia’s Revenue
Wait a minute, so how is Nvidia currently making profits? Let’s assume that the demand for crypto mining does not come in the future, then will Nvidia’s profits fall?
Friends, if you are also thinking like me then let me tell you this is not going to happen.
Nvidia is earning revenue from many such places such as healthcare, chip, and data center. Would you believe that they are making a profit from healthcare as well?
One thing is certain: whether it is crypto or healthcare, or a powerful PC of a space organization like NASA, a high-performance device is required.
Do you know that Nvidia holds the Guinness World Record for DNA Sequencing?
The process of DNA sequencing used to take many weeks, but it is now completed in a matter of hours. Nvidia has set a Guinness World Record for the quickest DNA sequencing technology, with a record time of 5 hours and 2 minutes.
Because the Time to Diagnosis (TTD) has been substantially lowered by the Genomic Sequencing Technique, the patient’s regular treatment time has been greatly reduced.
Mini Robots
I was quite shocked when I heard about these facts. I do not know whether you already knew or not but I am sure that you will also get surprised after reading this.
Nvidia is making tiny computer chips that are being used in robots. Robots are being used in the logistics department in Amazon’s warehouse. Mini robots keep roaming on the floor, they are made from Tegra line chips. This Tegra line chip was made by Nvidia in 2010. Nvidia even tried to enter into the smartphone market with this Tegra chip as I said in my previous article but they failed.
Tesla
This failure chip is being used in small robots after being developed. Surprisingly, this chip was also utilized in Tesla’s Model 3 and Model 4 vehicles. Tesla is currently manufacturing chips on its own. This chip is mostly utilized in self-driving cars.
Just think about it, the company that wants to make a self-driving car is going to use Nvidia’s chips. That’s why Telsa is now making the chips by themselves.
ChatBot
The demand for AI chatbots has increased in the market due to ChatGPT. Google and Microsoft are competing with each other to make the best ChatBot. Have you ever thought about where these Chatbots are running and from where they are getting accurate answers to our questions?
Data centers are built up for chatbots, and all of these data centers use Nvidia’s A100 CPUs. A100 chips work like an engine, it is made to train large models like ChatGPT. Nvidia does the work of making both the A100 chips and the hardware for the data center.
Nvidia manufactures GPU server boards by combining eight A100 processors. This server board is known as the DGX100, and it costs $20,000 USD. Because this board has 8 GPUs, we get an exact result in just one click. A data center is built by connecting 500 to 1000 DGX100 boards.
The ChatGPT service is powered by 10,000 Nvidia H100 servers and 10,000 Nvidia A100 GPUs. The usage of these servers will rise in the future as the demand for Bots, Games, Mini Robots, and Data increases progressively.
Just think that if Nvidia will not manufacture or develop technology, then no company will be able to develop chatbots or AI tools. For this reason, Nvidia’s data center business is far ahead of AMD’s. Nvidia’s data center business is 250% larger than AMD’s.
Nvidia founder Jensen Huang revealed that the company is building its own data center, which will be 0.1 (one-tenth) the cost and 10 times faster.
Nvidia is installing Data Centers, Edge Computing, and Public Cloud to make a solid foothold in Artificial Intelligence.
Despite strong competition from AMD and Intel, Nvidia dominated the gaming industry to the point that no one could replace them. It is investing money in research and development of new ideas after making a lot of money by conquering the graphic card market.
Believe it or not, after the gaming market, Nvidia now controls the AI market.
Profits
Nvidia is putting money into developing technologies rather than manufacturing. The expense of developing technology is one-time after you can make as many things as you like.
Whichever firm develops a product using Nvidia’s technology must use the Nvidia GEFROCE RTX name and pay a commission licensing fee.
Top Lesson
Lesson №1
Focus on reducing costs as much as possible. Despite the fact that Nvidia has yet to set up its own manufacturing facility. Manufacturing incurs ongoing expenditures as a result of operational leverage.
Avoid areas where risk is high since if anything goes wrong, it may do significant damage to your company.
Lesson №2
Always planning should be done for both short term and long term. Nvidia started working seriously on AI since 2010, but in the short term, they were working on other things as well. Seeing the future of AI, Nvidia has been able to reach the position of $1 trillion market cap today.
Lesson №3
In the ecosystem of every business, one of the players is dependent on the other player for something. We have to do something so that we do not have to be dependent on anyone.
Nvidia is neither making games nor playing games, it is making powerful graphics cards to improve the performance of games. Nvidia is not competing with any gaming company. No matter which gaming firm comes into the market, Nvidia will still make money.
You should now understand why Nvidia is the master of AI and how they make money.
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Disclaimer
I am not a Financial advisor. I am not affiliated with any of the websites or coins and also this is not financial and Investment advice. This article is meant only for educational purposes. I am just sharing my thoughts and analysis based on my many years of experience.