Nvidia, which sells graphics cards, is gone! Nvidia has monopolized the AI ​​industry

From games to artificial intelligence, from cloud computing to ChatGPT and AI data large models that became popular at the end of last year. Whenever the wind shifts, the pigs in the sky take turns flying, but Huang Renxun can always leave his own figure among them. Jen-Hsun Huang is the CEO of the famous American semiconductor company NVIDA. Huang Jen-Hsun believes that we are in the iPhone era of AI, which means that AI will change the world just like the iPhone changed the mobile phone industry.

NVIDIA GPU professional computing cards are expected to replace CPUs and become the main computing power support for future computers and supercomputers. NVIDIA cooperates with many world-renowned companies, including cloud service providers such as Alibaba Cloud and Azure, to provide a variety of software and hardware solutions. Only by using NVIDIA products can enterprises deploy AI models better and more easily.

Without computing power, no matter how good the model is, it cannot run. If you want to play with AI, you need at least a thousand Nvidia A100 or H100 computing cards. The market price of an A100 is 150,000 yuan, but the cost is less than 9,000 yuan. This shows how profitable Nvidia’s computing cards are, and their ability to attract money is terrifying. On May 30, Nvidia's market value reached US$1.02 trillion, while Intel's market value was only US$123.5 billion, with eight Intels worth only one Nvidia.

The first quarter financial report for fiscal year 2024 was announced by NVIDIA on May 23 (the fiscal year is different from the natural year). The company's revenue reached US$7.192 billion, exceeding analysts' average forecast of US$6.52 billion, and net profit was US$2.713 billion. On the day the financial report was released, NVIDIA's stock price soared 24.63% like a rocket, an increase equivalent to the market value of AMD. Today's achievements are all due to NVIDIA's long-term technology research and development and product layout in high-performance computing and data centers.

According to financial report data, Nvidia’s data center business revenue in the first quarter was US$4.28 billion, accounting for more than half of the total revenue, a year-on-year increase of 14% and a month-on-month increase of 18%. It is worth noting that although the revenue of the automotive business was only US$296 million, its year-on-year growth rate was as high as 114%, which is a very rapid growth rate. In September last year, Nvidia released the latest generation of autonomous driving chip Thor. To the author's surprise, the computing power reached 2000TOPS. Manufacturers can use all computing power for autonomous driving functions, or allocate part of the computing power to in-vehicle AI and entertainment functions, while the other part is used for assisted driving.

This sentence can be rewritten as: If you want to know whether the automotive business has the ability to become a pillar, the key is to understand the performance of Thor's self-driving chips and never underestimate its potential. NVIDIA hopes that all the computing power required for a series of functions such as assisted driving, automatic parking, and smart cockpits of future cars will be supplied by one chip. Providing car manufacturers with universal software and hardware solutions for all scenarios and building an autonomous driving platform can be more profitable than selling graphics cards.

If the current competition in the AI ​​field is like a gold rush, then NVIDIA is a businessman specializing in "shovels". The difference is that all companies can only buy NVIDIA's "shovel" because NVIDIA monopolizes the entire industry. The hardware, software deployment solutions and development platforms provided by NVIDIA are enough to influence the development of the AI ​​industry. To a certain extent, Nvidia has built an ecological barrier to prevent AMD from penetrating. With the emergence of huge artificial intelligence market demand, graphics card manufacturers have emerged one after another, including Chinese brands, but if they want to catch up with Nvidia, they still need to make exponential efforts.

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