That's quite a statement. It was made in reference to NVDA's chips and CUDA software combo pack.
Whether it's true or not, the behemoths, MSFT, GOOG, META, etc. want to reduce their reliance on Nvidia's chips.
An extremely deep dive from Eric Savitz at Barron's, April 19:
Artificial intelligence has delivered seemingly daily wonders for the past 18 months. For investors, the biggest surprise has been the rise of Nvidia , which has come from humble roots to thoroughly dominate the market for AI-related chips.
Once known mostly for building PC add-on graphics cards for gamers, Nvidia has transformed its graphics processing units, or GPUs, into the beating heart of the AI revolution, powering the creation of large language models and running the inference software that leverages them in data centers around the world. Nvidia has been nearly alone on the field, with more than 90% market share.
But fresh competition is coming—from companies big and small—and the battle will be fierce. The stakes couldn’t be bigger: Lisa Su, the CEO of Advanced Micro Devices , has sized the AI chip market at $400 billion by 2027. Intel CEO Pat Gelsinger has projected a $1 trillion opportunity by 2030. That’s almost twice the size of the entire chip industry in 2023.
Nvidia’s Jensen Huang has built a company that is universally respected and admired, but chip buyers aren’t keen on relying on a single source. Hardware companies such as Dell Technologies , Hewlett Packard Enterprise , Lenovo , and Super Micro Computer can’t get enough Nvidia chips to meet customer demand—and they’d like alternatives. Cloud providers like Amazon.com and Alphabet ’s Google want more options so badly that they are designing their own chips. And companies that rely on AI-based systems want more computing resources at more manageable costs than they can get now.
Nvidia’s success is now an opportunity for everyone else.
It’s hard to find a product of any variety that has had more impact on the financial markets so quickly than the Nvidia H100 GPU, which launched in March 2022.
Nvidia’s share price has more than tripled since the H100’s debut, boosting the company’s market value to $2.1 trillion. Among U.S.-listed companies, only Microsoft and Apple have higher market caps. And no other chip company is anywhere close.
This is no GameStop or Trump Media & Technology . In fact, Nvidia is the anti-meme stock: The company’s revenue growth has actually outpaced the stock gains. For its fiscal fourth quarter ended on Jan. 28, Nvidia posted revenue of $22.1 billion, up 265% from a year earlier. The company’s data center revenue was up 409%.
A few weeks ago, Nvidia launched its latest marvel, the Blackwell B200 GPU, which CEO Huang says dramatically outperforms the H100. With Blackwell, Nvidia raises the bar for its rivals. Nvidia for the foreseeable future will sell as many Blackwells as it can make—or, to be more precise, that partner Taiwan Semiconductor Manufacturing can make for it.
Huang has said Blackwell GPUs will cost $30,000 to $40,000 apiece. The current H100s sell in the same range. But the chip prices aren’t the whole story. AI customers want to run workloads in the shortest time, at the lowest cost, with the highest accuracy and reliability, drawing as little power as possible. There are a number of companies that think they can do that as well—or better—than Nvidia.
Nvidia’s rivals fall into three groups: big chip makers, cloud computing vendors, and venture-backed start-ups. With a $1 trillion market at stake, this won’t be winner-take-all. It isn’t game over. It’s game on.
Nvidia’s most obvious challengers are Advanced Micro Devices and Intel.
AMD shares have rallied 71% over the past 12 months, aided by the market’s perception that its new MI300 GPUs will chip away at Nvidia’s stranglehold on the market. That hope is inspired by AMD’s success at stealing market share from Intel in PC and servers.
“AMD is really the only other company on the field,” contends Andrew Dieckmann, general manager of AMD’s data center GPU business. “We’re the only other solution being adopted at scale within the industry.” He says that AMD chips outperform Nvidia’s H100 for many inference workloads, while offering parity for model training. But AMD’s other asset is that it isn’t Nvidia.
“For the very large users, they are not going to bet their entire franchise on one supplier,” Dieckmann says. “There is an extreme desire for market alternatives.”....
....MUCH, MUCH MORE
