The race for artificial intelligence (AI) caused demand for AI chips to increase sharply, Nvidia is expected to reach revenue of $ 11 billion in the second quarter of 2023, surpassing the previous estimate of $ 7.15 billion by analysts. accumulate.
The AI ”craze” helped Nvidia stock jump 25% in trading on May 25, bringing the company’s market capitalization to about $ 950 billion. Before that, Nvidia’s market capitalization on May 24 was only 755 billion USD. According to CNBC, if it achieves expectations, the chip industry “giant” will become the fifth US company worth $1 trillion.
Nvidia is changing the way it builds computers to further profit growth. Mr. Huang Jensen said the parts used to build the data center (data center) could make up a market worth $1,000.
The most important part in computers and servers is the central processing unit (CPU). by Nvidia’s rivals Intel and AMD That market is dominated . But with the advent of AI applications that require powerful computing power, Nvidia now dominates the GPU market
Mr. Huang said that the data center used to mainly use the CPU to access files, in the future will be general data. Instead of retrieving data, you’ll be retrieving part of it, but having to generate most of it using AI. So instead of using millions of CPUs, you will need a lot less CPUs, but they will be connected to millions of GPUs, Nvidia CEO added.
That’s one reason why Nvidia’s data center business grew 14% in the first quarter of 2023. Meanwhile, Intel’s data center and AI business fell 39% in revenue to $3.7 billion, while AMD’s growth was flat.
Also, Nvidia’s GPUs tend to be more expensive than many central processors. Intel’s latest generation of Xeon CPUs can cost up to $17,000 at list prices. An Nvidia H100 chip can be resold for $40,000 on other platforms like eBay.
The competition Nvidia faces will intensify as the AI market heats up. Two big competitors AMD and Intel both have their own GPU lines. Besides, technology “giants” like Google and Amazon are also designing AI chips. But Nvidia’s high-end GPU is still the chip of choice for AI training. Analysts say Nvidia still leads in AI chips thanks to its proprietary software that makes it easier to apply AI.
Mr. Huang shared that the company’s software will not be easily copied because you have to design all the software, all the libraries, all the algorithms, integrate them and optimize the frameworks and optimizations. optimize it for the architecture, not just a single chip, but the architecture of the entire data center
