Nvidia dominates the AI chip market, but there’s more competition than ever
Jensen Huang, co-founder and chief executive officer of Nvidia Corp., during the Nvidia GPU Technology Conference (GTC) in San Jose, California, US, on Tuesday, March 19, 2024.
David Paul Morris | Bloomberg | Getty Images
Nvidia’s 27% rally in May pushed its market cap to $2.7 trillion, behind only Microsoft and Apple among the most-valuable public companies in the world. The chipmaker reported a tripling in year-over-year sales for the third straight quarter driven by soaring demand for its artificial intelligence processors.
Mizuho Securities estimates that Nvidia controls between 70% and 95% of the market for AI chips used for training and deploying models like OpenAI’s GPT. Underscoring Nvidia’s pricing power is a 78% gross margin, a stunningly high number for a hardware company that has to manufacture and ship physical products.
Rival chipmakers Intel and Advanced Micro Devices reported gross margins in the latest quarter of 41% and 47%, respectively.
Nvidia’s position in the AI chip market has been described as a moat by some experts. Its flagship AI graphics processing units (GPUs), such as the H100, coupled with the company’s CUDA software led to such a head start on the competition that switching to an alternative can seem almost unthinkable.
Still, Nvidia CEO Jensen Huang, whose net worth has swelled from $3 billion to about $90 billion in the past five years, has said he’s “worried and concerned” about his 31-year-old company losing its edge. He acknowledged at a conference late last year that there are many powerful competitors on the rise.
“I don’t think people are trying to put me out of business,” Huang said in November. “I probably know they’re trying to, so that’s different.”
Nvidia has committed to releasing a new AI chip architecture every year, rather than every other year as was the case historically, and to putting out new software that could more deeply entrench its chips in AI software.
But Nvidia’s GPU isn’t alone in being able to run the complex math that underpins generative AI. If less powerful chips can do the same work, Huang might be justifiably paranoid.
The transition from training AI models to what’s called inference — or deploying the models — could also give companies an opportunity to replace Nvidia’s GPUs, especially if they’re less expensive to buy and run. Nvidia’s flagship chip costs roughly $30,000 or more, giving customers plenty of incentive to seek alternatives.
“Nvidia would love to have 100% of it, but customers would not love for Nvidia to have 100% of it,” said Sid Sheth, co-founder of aspiring rival D-Matrix. “It’s just too big of an opportunity. It would be too unhealthy if any one company took all of it.”
Founded in 2019, D-Matrix plans to release a semiconductor card for servers later this year that aims to reduce the cost and latency of running AI models. The company raised $110 million in September.
In addition to D-Matrix, companies ranging from multinational corporations to nascent startups are fighting for a slice of the AI chip market that could reach $400 billion in annual sales in the next five years, according to market analysts and AMD. Nvidia has generated about $80 billion in revenue over the past four quarters, and Bank of America estimates the company sold $34.5 billion in AI chips last year.
Many companies taking on Nvidia’s GPUs are betting that a different architecture or certain trade-offs could produce a better chip for particular tasks. Device makers are also developing technology that could end up doing a lot of the computing for AI that’s currently taking place in large GPU-based clusters in the cloud.
“Nobody can deny that today Nvidia is the hardware you want to train and run AI models,” Fernando Vidal, co-founder of 3Fourteen Research, told CNBC. “But there’s been incremental progress in leveling the playing field, from hyperscalers working on their own chips, to even little startups, designing their own silicon.”
AMD CEO Lisa Su wants investors to believe there’s plenty of room for many successful companies in the space.
“The key is that there are a lot of options there,” Su told reporters in December, when her company launched its most recent AI chip. “I think we’re going to see a situation where there’s not only one solution, there will be multiple solutions.”
Other big chipmakers
Lisa Su displays an AMD Instinct MI300 chip as she delivers a keynote address at CES 2023 in Las Vegas, Nevada, on Jan. 4, 2023.
David Becker | Getty Images
AMD makes GPUs for gaming and, like Nvidia, is adapting them for AI inside of data centers. Its flagship chip is the Instinct MI300X. Microsoft has already bought AMD processors, offering access to them through its Azure cloud.
At launch, Su highlighted the chip’s excellence at inference, as opposed to competing with Nvidia for training. Last week, Microsoft said it was using AMD Instinct GPUs to serve its Copilot models. Morgan Stanley analysts took the news as a sign that AMD’s AI chip sales could surpass $4 billion this year, the company’s public target.
Intel, which was surpassed by Nvidia last year in terms of revenue, is also trying to establish a presence in AI. The company recently announced the third version of its AI accelerator, Gaudi 3. This time Intel compared it directly to the competition, describing it as a more cost-effective alternative and better than Nvidia’s H100 in terms of running inference, while faster at training models.
Bank of America analysts estimated recently that Intel will have less than 1% of the AI chip market this year. Intel says it has a $2 billion order of backlogs for the chip.
The main roadblock to broader adoption may be software. AMD and Intel are both participating in a big industry group called the UXL foundation, which includes Google, that’s working to create free alternatives to Nvidia’s CUDA for controlling hardware for AI applications.
Nvidia’s top customers
One potential challenge for Nvidia is that it’s competing against some of its biggest customers. Cloud providers including Google, Microsoft and Amazon are all building processors for internal use. The Big Tech three, plus Oracle, make up over 40% of Nvidia’s revenue.
Amazon introduced its own AI-oriented chips in 2018, under the Inferentia brand name. Inferentia is now on its second version. In 2021, Amazon Web Services debuted Tranium targeted to training. Customers can’t buy the chips but they can rent systems through AWS, which markets the chips as more cost efficient than Nvidia’s.
Google is perhaps the cloud provider most committed to its own silicon. The company has been using what it calls Tensor Processing Units (TPUs) since 2015 to train and deploy AI models. In May, Google announced the sixth version of its chip, Trillium, which the company said was used to develop its models, including Gemini and Imagen.
Google also uses Nvidia chips and offers them through its cloud.
Microsoft isn’t as far along. The company said last year that it was building its own AI accelerator and processor, called Maia and Cobalt.
Meta isn’t a cloud provider, but the company needs massive amounts of computing power to run its software and website and to serve ads. While the Facebook parent company is buying billions of dollars worth of Nvidia processors, it said in April that some of its homegrown chips were already in data centers and enabled “greater efficiency” compared to GPUs.
JPMorgan analysts estimated in May that the market for building custom chips for big cloud providers could be worth as much as $30 billion, with potential growth of 20% per year.
Startups
Cerebras’ WSE-3 chip is one example of new silicon from upstarts designed to run and train artificial intelligence.
Cerebras Systems
Venture capitalists see opportunities for emerging companies to jump into the game. They invested $6 billion in AI semiconductor companies in 2023, up slightly from $5.7 billion a year earlier, according to data from PitchBook.
It’s a tough area for startups as semiconductors are expensive to design, develop and manufacture. But there are opportunities for differentiation.
For Cerebras Systems, an AI chipmaker in Silicon Valley, the focus is on basic operations and bottlenecks for AI, versus the more general purpose nature of a GPU. The company was founded in 2015 and was valued at $4 billion during its most recent fundraising, according to Bloomberg.
The Cerebras chip, WSE-2, puts GPU capabilities as well as central processing and additional memory into a single device, which is better for training large models, said CEO Andrew Feldman.
“We use a giant chip, they use a lot of little chips,” Feldman said. “They’ve got challenges of moving data around, we don’t.”
Feldman said his company, which counts Mayo Clinic, GlaxoSmithKline, and the U.S. Military as clients, is winning business for its supercomputing systems even going up against Nvidia.
“There’s ample competition and I think that’s healthy for the ecosystem,” Feldman said.
Sheth from D-Matrix said his company plans to release a card with its chiplet later this year that will allow for more computation in memory, as opposed to on a chip like a GPU. D-Matrix’s product can be slotted into an AI server along existing GPUs, but it takes work off of Nvidia chips, and helps to lower the cost of generative AI.
Customers “are very receptive and very incentivized to enable a new solution to come to market,” Sheth said.
Apple and Qualcomm
Apple iPhone 15 series devices are displayed for sale at The Grove Apple retail store on release day in Los Angeles, California, on September 22, 2023.
Patrick T. Fallon | Afp | Getty Images
The biggest threat to Nvidia’s data center business may be a change in where processing happens.
Developers are increasingly betting that AI work will move from server farms to the laptops, PCs and phones we own.
Big models like the ones developed by OpenAI require massive clusters of powerful GPUs for inference, but companies like Apple and Microsoft are developing “small models” that require less power and data and can run on a battery-powered device. They may not be as skilled as the latest version of ChatGPT, but there are other applications they perform, such as summarizing text or visual search.
Apple and Qualcomm are updating their chips to run AI more efficiently, adding specialized sections for AI models called neural processors, which can have privacy and speed advantages.
Qualcomm recently announced a PC chip that will allow laptops to run Microsoft AI services on the device. The company has also invested in a number of chipmakers making lower-power processors to run AI algorithms outside of a smartphone or laptop.
Apple has been marketing its latest laptops and tablets as optimized for AI because of the neural engine on its chips. At its upcoming developer conference, Apple is planning to show off a slew of new AI features, likely running on the company’s iPhone-powering silicon.
Cheng Xin | Getty Images
Okta on Tuesday topped Wall Street’s third-quarter estimates and issued an upbeat outlook, but shares fell as the company did not provide guidance for fiscal 2027.
Shares of the identity management provider fell more than 3% in after-hours trading on Tuesday.
Here’s how the company did versus LSEG estimates:
- Earnings per share: 82 cents adjusted vs. 76 cents expected
- Revenue: $742 million vs. $730 million expected
Compared to previous third-quarter reports, Okta refrained from offering preliminary guidance for the upcoming fiscal year. Finance chief Brett Tighe cited seasonality in the fourth quarter, and said providing guidance would require “some conservatism.”
Okta released a capability that allows businesses to build AI agents and automate tasks during the third quarter.
CEO Todd McKinnon told CNBC that upside from AI agents haven’t been fully baked into results and could exceed Okta’s core total addressable market over the next five years.
“It’s not in the results yet, but we’re investing, and we’re capitalizing on the opportunity like it will be a big part of the future,” he said in a Tuesday interview.
Revenues increased almost 12% from $665 million in the year-ago period. Net income increased 169% to $43 million, or 24 cents per share, from $16 million, or breakeven, a year ago. Subscription revenues grew 11% to $724 million, ahead of a $715 million estimate.
For the current quarter, the cybersecurity company expects revenues between $748 million and $750 million and adjusted earnings of 84 cents to 85 cents per share. Analysts forecast $738 million in revenues and EPS of 84 cents for the fourth quarter.
Returning performance obligations, or the company’s subscription backlog, rose 17% from a year ago to $4.29 billion and surpassed a $4.17 billion estimate from StreetAccount.
This year has been a blockbuster period for cybersecurity companies, with major acquisition deals from the likes of Palo Alto Networks and Google and a raft of new initial public offerings from the sector.
Okta shares have gained about 4% this year.
WATCH: Earnings will drive small cap outperformance, says Bank of America’s Jill Carey Hall
Marvell Technology Group Ltd. headquarters in Santa Clara, California, on Sept. 6, 2024.
David Paul Morris | Bloomberg | Getty Images
Semiconductor company Marvell on Tuesday announced that it will acquire Celestial AI for at least $3.25 billion in cash and stock.
The purchase price could increase to $5.5 billion if Celestial hits revenue milestones, Marvell said.
Marvell shares rose 13% in extended trading Tuesday as the company reported third-quarter earnings that beat expectations and said on the earnings call that it expected data center revenue to rise 25% next year.
The deal is an aggressive move for Marvell to acquire complimentary technology to its semiconductor networking business. The addition of Celestial could enable Marvell to sell more chips and parts to companies that are currently committing to spend hundreds of billions of dollars on infrastructure for AI.
Marvell stock is down 18% so far in 2025 even as semiconductor rivals like Broadcom have seen big valuation increases driven by excitement around artificial intelligence.
Celestial is a startup focused on developing optical interconnect hardware, which it calls a “photonic fabric,” to connect high-performance computers. Celestial was reportedly valued at $2.5 billion in March in a funding round, and Intel CEO Lip-Bu Tan joined the startup’s board in January.
Optical connections are becoming increasingly important because the most advanced AI systems need those parts tie together dozens or hundreds of chips so they can work as one to train and run the biggest large-language models.
Currently, many AI chip connections are done using copper wires, but newer systems are increasingly using optical connections because they can transfer more data faster and enable physically longer cables. Optical connections also cost more.
“This builds on our technology leadership, broadens our addressable market in scale-up connectivity, and accelerates our roadmap to deliver the industry’s most complete connectivity platform for AI and cloud customers,” Marvell CEO Matt Murphy said in a statement.
Marvell said that the first application of Celestial technology would be to connect a system based on “large XPUs,” which are custom AI chips usually made by the companies investing billions in AI infrastructure.
On Tuesday, the company said that it could even integrate Celestial’s optical technology into custom chips, and based on customer traction, the startup’s technology would soon be integrated into custom AI chips and related parts called switches.
Amazon Web Services Vice President Dave Brown said in a statement that Marvell’s acquisition of Celestial will “help further accelerate optical scale-up innovation for next-generation AI deployments.”
The maximum payout for the deal will be triggered if Celestial can record $2 billion in cumulative revenue by the end of fiscal 2029. The deal is expected to close early next year.
In its third-quarter earnings on Tuesday, Marvell earnings of 76 cents per share on $2.08 billion in sales, versus LSEG expectations of 73 cents on $2.07 billion in sales. Marvell said that it expects fourth-quarter revenue to be $2.2 billion, slightly higher than LSEG’s forecast of $2.18 billion.
WATCH: Nvidia GPUs to Google TPUs: Breaking down all the AI chips
-
Sports2 years agoStory injured on diving stop, exits Red Sox game
-
Sports3 years ago‘Storybook stuff’: Inside the night Bryce Harper sent the Phillies to the World Series
-
Sports2 years agoGame 1 of WS least-watched in recorded history
-
Sports3 years agoButton battles heat exhaustion in NASCAR debut
-
Sports3 years agoMLB Rank 2023: Ranking baseball’s top 100 players
-
Sports4 years ago
Team Europe easily wins 4th straight Laver Cup
-
Environment3 years agoJapan and South Korea have a lot at stake in a free and open South China Sea
-
Environment1 year agoHere are the best electric bikes you can buy at every price level in October 2024