| To: Julius Wong who wrote (2602) | 12/5/2023 3:28:28 PM | | From: Selectric II | | | | What percentage of their shares does that represent? <10%?
"... Mark Stevens, a director at Nvidia since 2008. The former managing partner at venture capital firm Sequoia Capital filed a form 144 on Nov. 24 indicating the planned sale of 300,000 shares and sold 10,280 shares on Nov. 24 and Nov. 27. Stevens has sold hundreds of thousands of shares over the past few years and still has a stake in Nvidia worth about $2 billion, according to data compiled by Bloomberg...."
That tells me just about all I need to know. |
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| From: Glenn Petersen | 12/7/2023 6:15:30 AM | | | | | | Meta and Microsoft say they will buy AMD’s new AI chip as an alternative to Nvidia’s
PUBLISHED WED, DEC 6 20234:09 PM ESTUPDATED AN HOUR AGO
Kif Leswing @KIFLESWING
CNBC.com
KEY POINTS
- Meta, OpenAI, and Microsoft said they will use AMD’s newest AI chip, the Instinct MI300X — a sign that tech companies want alternatives to the expensive Nvidia graphics processors that have been essential for artificial intelligence.
- If the MI300X is good enough and inexpensive enough when it starts shipping early next year, it could lower costs for developing AI models.
- AMD CEO Lisa Su projected the market for AI chips will amount to $400 billion or more in 2027, and she said she hopes AMD has a sizable part of that market.
Lisa Su displays an AMD Instinct MI300 chip as she delivers a keynote address at CES 2023 in Las Vegas, Nevada, Jan. 4, 2023.
David Becker | Getty Images
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Meta, OpenAI, and Microsoft said at an AMD investor event Wednesday they will use AMD’s newest AI chip, the Instinct MI300X. It’s the biggest sign so far that technology companies are searching for alternatives to the expensive Nvidia graphics processors that have been essential for creating and deploying artificial intelligence programs such as OpenAI’s ChatGPT.
If AMD’s latest high-end chip is good enough for the technology companies and cloud service providers building and serving AI models when it starts shipping early next year, it could lower costs for developing AI models and put competitive pressure on Nvidia’s surging AI chip sales growth.
“All of the interest is in big iron and big GPUs for the cloud,” AMD CEO Lisa Su said Wednesday.
AMD says the MI300X is based on a new architecture, which often leads to significant performance gains. Its most distinctive feature is that it has 192GB of a cutting-edge, high-performance type of memory known as HBM3, which transfers data faster and can fit larger AI models.
Su directly compared the MI300X and the systems built with it to Nvidia’s main AI GPU, the H100.
“What this performance does is it just directly translates into a better user experience,” Su said. “When you ask a model something, you’d like it to come back faster, especially as responses get more complicated.”
The main question facing AMD is whether companies that have been building on Nvidia will invest the time and money to add another GPU supplier. “It takes work to adopt AMD,” Su said.
AMD on Wednesday told investors and partners that it had improved its software suite called ROCm to compete with Nvidia’s industry standard CUDA software, addressing a key shortcoming that had been one of the primary reasons AI developers currently prefer Nvidia.
Price will also be important. AMD didn’t reveal pricing for the MI300X on Wednesday, but Nvidia’s can cost around $40,000 for one chip, and Su told reporters that AMD’s chip would have to cost less to purchase and operate than Nvidia’s in order to persuade customers to buy it.
Who says they’ll use the MI300X?
AMD MI300X accelerator for artificial intelligence.
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On Wednesday, AMD said it had already signed up some of the companies most hungry for GPUs to use the chip. Meta and Microsoft were the two largest purchasers of Nvidia H100 GPUs in 2023, according to a recent report from research firm Omidia.
Meta said it will use MI300X GPUs for AI inference workloads such as processing AI stickers, image editing, and operating its assistant.
Microsoft’s CTO, Kevin Scott, said the company would offer access to MI300X chips through its Azure web service.
Oracle’s cloud will also use the chips.
OpenAI said it would support AMD GPUs in one of its software products, called Triton, which isn’t a big large language model like GPT but is used in AI research to access chip features.
AMD isn’t forecasting massive sales for the chip yet, only projecting about $2 billion in total data center GPU revenue in 2024. Nvidia reported more than $14 billion in data center sales in the most recent quarter alone, although that metric includes chips other than GPUs.
However, AMD says the total market for AI GPUs could climb to $400 billion over the next four years, doubling the company’s previous projection. This shows how high expectations are and how coveted high-end AI chips have become — and why the company is now focusing investor attention on the product line.
Su also suggested to reporters that AMD doesn’t think that it needs to beat Nvidia to do well in the market.
“I think it’s clear to say that Nvidia has to be the vast majority of that right now,” Su told reporters, referring to the AI chip market. “We believe it could be $400 billion-plus in 2027. And we could get a nice piece of that.”
Meta and Microsoft to buy AMD's new AI chip as alternative to Nvidia (cnbc.com) |
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| To: Glenn Petersen who wrote (2606) | 12/9/2023 6:22:23 AM | | From: Glenn Petersen | | | |
He Built a Trillion-Dollar Company. He Wouldn’t Do It Again.
Jensen Huang, the CEO of the year’s most successful company, has a theory about the superpower of entrepreneurs
By Ben Cohen
Wall Street Journal
Updated Dec. 9, 2023 12:16 am ET
When he sat down in a booth at his local Denny’s and began plotting out the business that would change his life, Jensen Huang didn’t know that his startup would one day be worth $1 trillion. In fact, the only chief executive in Nvidia’s history didn’t know much of anything about what he was getting himself into.
But if he had known three decades ago what he knows today, he never would have founded one of the world’s most valuable companies.
“The reason for that is really quite simple,” Huang said recently. “Building Nvidia turned out to have been a million times harder than I expected.”
Nvidia was the stock market’s big winner of 2023, when the chip maker cracked $1 trillion in value. That would have seemed impossible 30 years ago, and it wasn’t especially probable just one year ago, before the AI boom made Nvidia worth more than Netflix, Nike and Novo Nordisk combined.
So why wouldn’t he do it again?
“If we realized the pain and suffering and how vulnerable you’re going to feel, the challenges that you’re going to endure, the embarrassment and the shame and the list of all the things that go wrong,” he said, “nobody in their right mind would do it.”
The candor from one of tech’s longest-tenured CEOs wasn’t just eye-opening. Huang’s comments were also a rare peek into the mind of one of the most successful entrepreneurs of his generation, someone who took an idea hatched over Grand Slam breakfasts and Super Bird turkey sandwiches and turned it into a trillion-dollar company. Along the way he learned an important, counterintuitive lesson.
Everyone in Silicon Valley knows they have to be resilient. Huang knows it also helps to be ignorant.
“I think that’s kind of the superpower of an entrepreneur,” he said. “They don’t know how hard it is. And they only ask themselves: How hard can it be? To this day, I trick my brain into thinking: How hard can it be?”
Really hard, as it turns out. He didn’t know that the original business plan had no chance of success. He didn’t know how many times he would fail. And he didn’t know just how much he didn’t know. But just because the 60-year-old billionaire says he wouldn’t do it again doesn’t mean he’s telling other people they shouldn’t. In fact, the opposite: Only they have the advantage of being undaunted by the difficulty of building a company.
Jensen Huang has been running Nvidia since his silvery hair was the color of his signature black leather jacket. PHOTO: I-HWA CHENG/AGENCE FRANCE-PRESSE/GETTY IMAGES
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Huang made his comments in a recent interview with Acquired, a tech podcast hosted by Ben Gilbert and David Rosenthal, who might know more about Nvidia’s history than anyone who didn’t live through it. After releasing three deeply researched, delightfully wonky episodes about the company’s strategy, the podcasters were invited to Nvidia headquarters for an interview with the CEO himself. (Huang declined to comment for this article.)
Huang has been running the company since his silvery hair was the color of his signature black leather jacket. Even after three decades on the job, Huang remains actively involved at Nvidia. He still manages 50 senior executives who report directly to him and attends product meetings with junior employees who weren’t alive when the company was born. There has never been a business worth so much that people know so little about. But the more the podcasters studied Nvidia’s success, the more they credited one person.
“That company is him,” Rosenthal told me. “He does everything but sweep the floors—and he may sweep the floors.”
So when it came time for one final question, they were curious: If he were 30 years old today, sitting in that Denny’s again, what kind of company would he be starting?
He said he wouldn’t start one at all. He might as well have said Nvidia’s chips were made of Doritos.
But his response began to make sense when he reflected on the wrenching years before this year. There are only five American companies worth at least $1 trillion right now. Apple, Microsoft and Alphabet’s stock prices have never dropped 85% from high to low. Amazon had one such drawdown. Nvidia survived two.
Those excruciating stretches in 2002 and 2008 now look so insignificant that you can barely see them on Nvidia’s historical stock chart. They didn’t feel that way at the time. And he got an unwelcome reminder of that feeling when the company lost half its value last year.
Huang circa 2003, not long after Nvidia survived a near-death experience during the dot-com bust. PHOTO: MEDIANEWS GROUP VIA GETTY IMAGES
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But after sputtering in 2022, Nvidia exploded in 2023. That’s because there has never been so much demand for GPUs, the advanced chips that provide oxygen for artificial intelligence, powering almost every piece of technology the nerdiest person you know is psyched about, and Huang’s company controls the supply. AI models require tens of thousands of these graphics-processing units that can handle lots of computational tasks at the same time, and they’re made almost entirely by Nvidia because Huang invested in GPUs long before there was a roaring market for them.
Nvidia’s central role in the AI economy is the reason it has tripled in value and beat every other company in the S&P 500 this year. It’s on pace for the best annual performance of any major stock in the past decade.
Which made the recent comments from one of the world’s richest men all the more curious.
Huang had a better year than anyone this side of Taylor Swift. But even at the height of his company’s success, he remains haunted by the prospect of failure. According to the New Yorker, Nvidia’s unofficial motto is his mantra from the startup’s early, uncertain years: “Our company is 30 days from going out of business.” At this point, Nvidia is worth more than the other American chip giants put together, and AI would have to destroy the world for Huang’s company to be out of business in a month. But he’s still driven by that fear.
“You’re always on the way to going out of business,” he recently said at Columbia Business School. “If you don’t internalize that sensibility, you will go out of business.”
The moments when his company nearly crashed are burned into Huang’s memory as permanently as the Nvidia logo tattooed on his arm.
When the world’s most valuable chip maker was founded in 1993 by Huang, Chris Malachowsky and Curtis Priem, the only people paying attention to them were the waiters of a Denny’s in San Jose, Calif. There was no reason to suspect three lousy customers guzzling too much coffee were laying the foundation of a revolutionary company. And when Huang told people he was making graphics cards for videogames, his own mother told him to get a real job.
But the secret to Nvidia’s early success wasn’t the people involved or the industry they set out to conquer. It was the unusual, informal governance structure they chose for their startup.
Huang was always in charge, and Malachowsky and Priem reported to him, but they made a deal that each founder would have authority in his own fiefdom.
“We would talk or argue over each other’s decisions, but we would default to the final decision of the person who had the expertise in that area,” Priem told me. “It wasn’t ‘agree to disagree.’ The decision terminated any disagreements and became the direction we were going.”
Nvidia’s chips are indispensable for the AI boom. PHOTO: MARLENA SLOSS/BLOOMBERG NEWS
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Their arrangement made Huang responsible for business operations and finding partners to manufacture its chips. But that was a huge burden for one person, which Priem learned the one time he told Huang what he thought he should do. “He unloaded on me,” said Priem, “telling me all the responsibilities he had and all the things he was juggling.” He was stunned: Huang had kept the pressures of his job to himself. “It was an oh-my-God moment for me to understand how alone he was in his role,” Priem told me.
A trillion dollars in market value hasn’t made Huang’s job any easier. These days, his company must navigate tight U.S. regulations meant to stifle China’s access to powerful chips, not to mention increased competition from rivals at home desperate to pierce Nvidia’s dominance.
But it was much harder when Nvidia wasn’t as successful.
After the company released its first product, a graphics card that flopped, Huang laid off half the workforce. Running out of money, teetering on the edge of bankruptcy, he bet the company on the 1997 chip that saved Nvidia. But the decade after Huang’s company went public in 1999 would bring two more brutal stretches during the dot-com bust and global financial crisis. Even when markets rallied, Nvidia didn’t. From 2008 to 2013, when the S&P 500 was up 25%, Nvidia was down 50%.
The whole company was worth less than the $6 billion that Huang personally made in a single day of trading this year.
Nvidia stagnated as Huang plowed money into a new platform for accelerated computing, one that would allow developers to do anything they wanted with GPUs. Wall Street was skeptical of his vision of the future. But there was one group of people who could see it: AI researchers. Once they began using Nvidia’s chips to train neural networks, they realized the transformational potential of Huang’s tools.
And then he decided to put his chips on the table again.
The initial breakthroughs in deep learning compelled Huang to make another bet-the-company move on AI. Nvidia began work in 2012 on the system that would become its first AI supercomputer. Huang delivered it four years later to OpenAI, whose researchers would use Nvidia’s GPUs to educate ChatGPT, which became the hottest app in tech history when it was released last year.
But this was the year those chips became the picks and shovels of a gold rush.
Now there are young entrepreneurs sitting in their own metaphorical Denny’s, dreaming about building companies and completely unaware of how hard it’s going to be.
Despite Nvidia’s success, Huang says he wouldn’t have started the company if he had realized how difficult it would be. PHOTO: I-HWA CHENG/BLOOMBERG NEWS
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Because how hard could it be?
He Built a Trillion-Dollar Company. He Wouldn’t Do It Again. - WSJ |
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| From: Frank Sully | 12/12/2023 9:20:07 PM | | | | | | AMD's Answer to NVIDIA? - 30 Minure Excerpt from last week's presentation comparing AMD's MI300X TO NVIDIA's H100. Note that they don't compare to new NVIDIA H200 super hip.
youtu.be |
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| To: Glenn Petersen who wrote (2607) | 12/29/2023 6:37:31 AM | | From: Glenn Petersen | | | | Nvidia to launch slower version of its gaming chip in China to comply with U.S. export controls
PUBLISHED FRI, DEC 29 20235:09 AM EST
UPDATED AN HOUR AGO
Ryan Browne @RYAN_BROWNE_
CNBC.com
KEY POINTS
- U.S. chipmaking giant Nvidia is set to launch an adjusted version of a gaming processor with slower performance in China to comply with U.S. export restrictions.
- A product page on Nvidia’s website for Chinese consumers shows that the new Nvidia RTX 4090D has 11% fewer processing cores than versions sold outside of China.
- A Nvidia spokesperson told Reuters that the chip was “designed to fully comply with U.S. government export controls.”
U.S. chipmaking giant Nvidia is set to launch an adjusted version of a gaming processor with slower performance in China to comply with U.S. export restrictions.
A product page on Nvidia’s website for Chinese consumers shows that the new Nvidia RTX 4090D — which a spokesperson says will be launched in January, according to Reuters — has 11% fewer “CUDA” (Compute Unified Device Architecture) cores than versions of the chip that are sold outside of China.
RTX is Nvidia’s line of advanced gaming GPUs, or graphics processing units. The company’s CUDA architecture is essentially a GPU equivalent for CPU cores, which are processing units.
Nvidia was not immediately available for comment when contacted by CNBC, but a company spokesperson told Reuters that the chip has been “designed to fully comply with U.S. government export controls.”
Nvidia “extensively engaged with the U.S. government” while developing the product, the spokesperson said, according to Reuters.
Washington has imposed export restrictions on China that prevent companies in the country from accessing some of the most advanced chips from American firms.
The export rules primarily target chips that enable AI applications, but gaming-focused processors are also in the firing line as many also have potential uses in artificial intelligence.
The Nvidia RTX 4090 was included on the list of banned U.S.-made chips, according to an Oct. 17 Securities and Exchange Commission filing. On its website, Nvidia says the RTX 4090D chip leverages AI to enhance performance.
Shares of Nvidia have surged in 2023, more than tripling in price year-to-date. The company has benefited from a rush of demand for AI, thanks in no small part to the immense buzz caused by OpenAI’s ChatGPT chatbot.
Nvidia brings slower gaming chip version to China to bypass U.S. rules (cnbc.com) |
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| From: Glenn Petersen | 1/7/2024 7:01:55 AM | | | | | | Nvidia’s New China Pickle: Customers Don’t Want Its Downgraded Chips
Chinese buyers are pushing back against the lower-powered AI chips it hopes to sell to them in response to U.S. export curbs
By Raffaele Huang
Bloomberg
Jan. 6, 2024 11:00 pm ET
Nvidia, based in Santa Clara, Calif., is trying to meet Chinese clients’ demands while complying with U.S. export rules. PHOTO: PHILIP PACHECO/BLOOMBERG NEWS
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SINGAPORE—After U.S. regulations barred Nvidia from selling its high-performance artificial-intelligence chips to China in October, the company’s engineers quickly designed a new lineup to comply with the tightened rules.
The U.S. tech company may have found some wiggle room, but it faces a bigger problem: Chinese cloud companies—some of Nvidia’s biggest customers globally—aren’t so keen on buying its lower-powered AI chips.
Alibaba Group and Tencent are among China’s biggest cloud companies that have been testing Nvidia samples since November. They have indicated to Nvidia that they would order far fewer chips from the company this year than they had originally planned to buy of its now-banned previous chips, people familiar with the matter said.
In the short term, Nvidia’s downgrading of its processors is narrowing the performance gap with local alternatives, making China-made chips increasingly attractive to buyers.
Alibaba and Tencent are shifting some advanced semiconductor orders to homegrown companies such as Huawei Technologies and relying more on chips they can develop in-house, the people said. So too are China’s other top two buyers—AI pioneer Baidu and TikTok owner ByteDance.
In the longer term, Chinese buyers are uncertain about Nvidia’s ability to continue to supply them, given U.S. regulators have pledged to review the chip-export controls regularly and could further tighten performance limits, the people said. Tech firms are modifying their business strategies to prepare for a future with less access to Nvidia’s products, and to avoid the costly process of constantly adjusting their technology to adapt to new chips.
For Santa Clara, Calif.-based Nvidia, threading the needle between the demands of U.S. regulators and supplying chips that Chinese clients need is getting more difficult. The company has billions of dollars of unfulfilled orders for its earlier chips, and China is one of its biggest markets, historically contributing about a fifth of its revenue.
Nvidia CEO Jensen Huang has acknowledged Huawei as a ‘formidable competitor’ to the company in China. PHOTO: WALID BERRAZEG/ZUMA PRESS
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Nvidia’s chips are currently in higher demand than it can meet. Nonetheless, geopolitical tensions pose the longer-term risk of lost sales in the world’s second-biggest economy, which is pursuing AI development as a strategic priority.
Chinese cloud companies currently source around 80% of high-end AI chips from Nvidia, and that is likely to decline to 50%-60% in the next five years, said Frank Kung, an analyst at tech-research firm TrendForce. He added that tightening U.S. chip controls in the future would create additional pressure on Nvidia’s China sales.
Nvidia said it is working to offer products that comply with U.S. rules to customers worldwide.
Nvidia has said it doesn’t see a short-term financial impact from restrictions on shipment of its AI chips to China because it can find other buyers for them. But Chief Financial Officer Colette Kress said last year that in the long run, prohibiting the sale of AI chips in China would keep the U.S. industry from being able to compete and lead in one of the world’s largest markets.
Switching to Huawei Chips
Over the past two years, the Biden administration has imposed two rounds of export sanctions to curtail China’s access to advanced chips and technology that the U.S. says Beijing could use to advance its military and surveillance capabilities. Nvidia Chief Executive Jensen Huang has said he still hopes to supply high-end processors to China and is working with customers in China to get export licenses.
After the first curbs in October 2022, Nvidia modified the chips it sold in China by scaling back their performance to fall below the thresholds that would require U.S. government oversight. It sold more than $1 billion worth of such chips to Chinese customers in 2023.
When the U.S. a year later cut off more Nvidia chip exports to China without a license, the chip maker developed another new lineup of less powerful processors for Chinese buyers, which it plans to release early this year, The Wall Street Journal reported. Last month, Nvidia launched the GeForce RTX 4090 D, a modified version of its top-of-the-line gaming chip adapted after the latest U.S. curbs.
Commerce Secretary Gina Raimondo met in August with Chinese Premier Li Qiang during a trip in which export controls were high on her agenda. PHOTO: RAO AIMIN/ZUMA PRESS
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Chinese companies have been testing samples of Nvidia’s new highest-performing AI chip in the coming lineup, called the H20. The chip allows data to transmit among multiple processors efficiently, making it a better option than homegrown alternatives for building chip clusters needed to handle the computational workloads of AI, some testers said.
Still, testers said they needed more of the H20s to simulate the computing power that they got from Nvidia’s previous chips, increasing their costs.
The most advanced Chinese chips are as capable of handling inference—in which a trained AI model comes up with predictions—and less complex training tasks as are U.S. chips, developers say.
Huawei, acknowledged by Nvidia’s Huang to be a “formidable competitor” to the firm in China, is gaining ground at the expense of the U.S. chip maker.
In 2023, Huawei received orders for at least 5,000 Ascend 910B chips from major Chinese internet companies, people familiar with the matter said. The chip is considered the closest available Chinese alternative to Nvidia’s export-barred high-performance A100.
The chips are meant to be delivered through 2024, they said, as Huawei faces production constraints because of U.S. sanctions.
Chinese official procurements, such as those from state-owned telecom operators, have called for the adoption of homegrown chips such as Huawei’s. China Telecom acquired about $390 million worth of AI servers powered by Huawei chips in October, while China Unicom spent at least $20 million in 2022, according to company purchasing documents.
Huawei has stepped up efforts to expand its software ecosystem and plans to launch a new high-end AI chip as soon as the second half of 2024, the people said.
A few government-backed AI computing centers have sourced Huawei’s chips since the U.S. imposed curbs in 2022.
AI App Maker Skips Nvidia Chips
Alibaba’s chip arm, T-Head, is also developing a new specific-purpose AI processor under its Hanguang label, people familiar with the matter said.
“If the restrictions are likely to only get tighter in the next few years, you’d better start thinking about alternatives now,” said a senior executive at Alibaba Cloud.
The frenzy over generative AI earlier last year spurred demand for Nvidia’s advanced chips as major Chinese companies and startups tried to develop their own large language models. Now, many smaller players are scaling back such efforts and shifting to focus on AI applications.
Kenneth Yang, a Shanghai-based co-founder of a healthcare AI startup, said he plans to skip Nvidia’s latest China-focused chips and lease AI processing power from Baidu or Huawei instead.
“It’s about spending money in a smart way,” said Yang, who is developing a nursing-care app.
Engineers at Chinese technology companies say Nvidia’s chips will remain a purchasing priority over the next 12 months, given Nvidia’s more extensive product ecosystem and as local alternatives continue to be in short supply.
In the longer run, U.S. curbs are likely to push the Chinese to develop their own technologies, said Kevin Xu, founder of hedge fund Interconnected Capital, who authors a newsletter about tech and geopolitics.
“After this current phase of stockpiling is done, Nvidia’s China business will become a sacrificial lamb,” he said.
—Asa Fitch and Yuka Hayashi contributed to this article.
Write to Raffaele Huang at raffaele.huang@wsj.com
Nvidia’s New China Pickle: Customers Don’t Want Its Downgraded Chips - WSJ (archive.ph) |
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| From: Glenn Petersen | 1/30/2024 5:41:29 AM | | | | | |
Nvidia’s Big Tech Rivals Put Their Own A.I. Chips on the Table
Chafing at their dependence, Amazon, Google, Meta and Microsoft are racing to cut into Nvidia’s dominant share of the market.
By Cade Metz, Karen Weise and Mike Isaac
Reporting from San Francisco and Seattle
New York Times
Jan. 29, 2024, 5:00 a.m. ET
In September, Amazon said it would invest up to $4 billion in Anthropic, a San Francisco start-up working on artificial intelligence.
Soon after, an Amazon executive sent a private message to an executive at another company. He said Anthropic had won the deal because it agreed to build its A.I. using specialized computer chips designed by Amazon.
Amazon, he wrote, wanted to create a viable competitor to the chipmaker Nvidia, a key partner and kingmaker in the all-important field of artificial intelligence.
The boom in generative A.I. over the last year exposed just how dependent big tech companies had become on Nvidia. They cannot build chatbots and other A.I. systems without a special kind of chip that Nvidia has mastered over the past several years. They have spent billions of dollars on Nvidia’s systems, and the chipmaker has not kept up with the demand.
So Amazon and other giants of the industry — including Google, Meta and Microsoft — are building A.I. chips of their own. With these chips, the tech giants could control their own destiny. They could rein in costs, eliminate chip shortages and eventually sell access to their chips to businesses that use their cloud services.
While Nvidia sold 2.5 million chips last year, Google spent $2 billion to $3 billion building about a million of its own A.I. chips, said Pierre Ferragu, an analyst at New Street Research. Amazon spent $200 million on 100,000 chips last year, he estimated. Microsoft said it had begun testing its first A.I. chip.
But this work is a balancing act between competing with Nvidia while working closely with the chipmaker and its increasingly powerful chief executive, Jensen Huang.
Mr. Huang’s company accounts for more than 70 percent of A.I. chip sales, according to the research firm Omdia. It supplies an even larger percentage of the systems used in the creation of generative A.I. Nvidia’s sales have shot up 206 percent over the past year, and the company has added about a trillion dollars in market value.
What’s revenue to Nvidia is a cost for the tech giants. Orders from Microsoft and Meta made up about a quarter of Nvidia’s sales in the past two full quarters, said Gil Luria, an analyst at the investment bank D.A. Davidson.
Nvidia sells its chips for about $15,000 each, while Google spends an average of just $2,000 to $3,000 on each of its own, according to Mr. Ferragu.
“When they encountered a vendor that held them over a barrel, they reacted very strongly,” Mr. Luria said.
Companies constantly court Mr. Huang, jockeying to be at the front of the line for his chips. He regularly appears on event stages with their chief executives, and the companies are quick to say they remain committed to their partnerships with Nvidia. They all plan to keep offering its chips alongside their own.
While the big tech companies are moving into Nvidia’s business, it is moving into theirs. Last year, Nvidia started its own cloud service where businesses can use its chips, and it is funneling chips into a new wave of cloud providers, such as CoreWeave, that compete with the big three: Amazon, Google and Microsoft.
“The tensions here are a thousand times the usual jockeying between customers and suppliers,” said Charles Fitzgerald, a technology consultant and investor.
Nvidia declined to comment.
The A.I. chip market is projected to more than double by 2027, to roughly $140 billion, according to the research firm Gartner. Venerable chipmakers like AMD and Intel are also building specialized A.I. chips, as are start-ups such as Cerebras and SambaNova. But Amazon and other tech giants can do things that smaller competitors cannot.
“In theory, if they can reach a high enough volume and they can get their costs down, these companies should be able to provide something that is even better than Nvidia,” said Naveen Rao, who founded one of the first A.I. chip start-ups and later sold it to Intel.
Nvidia builds what are called graphics processing units, or G.P.U.s, which it originally designed to help render images for video games. But a decade ago, academic researchers realized these chips were also really good at building the systems, called neural networks, that now drive generative A.I.
As this technology took off, Mr. Huang quickly began modifying Nvidia’s chips and related software for A.I., and they became the de facto standard. Most software systems used to train A.I. technologies were tailored to work with Nvidia’s chips.
“Nvidia’s got great chips, and more importantly, they have an incredible ecosystem,” said Dave Brown, who runs Amazon’s chip efforts. That makes getting customers to use a new kind of A.I. chip “very, very challenging,” he said.
Rewriting software code to use a new chip is so difficult and time-consuming, many companies don’t even try, said Mike Schroepfer, an adviser and former chief technology officer at Meta. “The problem with technological development is that so much of it dies before it even gets started,” he said.
Rani Borkar, who oversees Microsoft’s hardware infrastructure, said Microsoft and its peers needed to make it “seamless” for customers to move between chips from different companies.
Amazon, Mr. Brown said, is working to make switching between chips “as simple as it can possibly be.”
Some tech giants have found success making their own chips. Apple designs the silicon in iPhones and Macs, and Amazon has deployed more than two million of its own traditional server chips in its cloud computing data centers. But achievements like these take years of hardware and software development.
Google has the biggest head start in developing A.I. chips. In 2017, it introduced its tensor processing unit, or T.P.U., named after a kind of calculation vital to building artificial intelligence. Google used tens of thousands of T.P.U.s to build A.I. products, including its online chatbot, Google Bard. And other companies have used the chip through Google’s cloud service to build similar technologies, including the high-profile start-up Cohere.
Amazon is now on the second generation of Trainium, its chip for building A.I. systems, and has a second chip made just for serving up A.I. models to customers. In May, Meta announced plans to work on an A.I. chip tailored to its needs, though it is not yet in use. In November, Microsoft announced its first A.I. chip, Maia, which will focus initially on running Microsoft’s own A.I. products.
“If Microsoft builds its own chips, it builds exactly what it needs for the lowest possible cost,” Mr. Luria said.
Nvidia’s rivals have used their investments in high-profile A.I. start-ups to fuel use of their chips. Microsoft has committed $13 billion to OpenAI, the maker of the ChatGPT chatbot, and its Maia chip will serve OpenAI’s technologies to Microsoft’s customers. Like Amazon, Google has invested billions in Anthropic, and it is using Google’s A.I. chips, too.
Anthropic, which has used chips from both Nvidia and Google, is among a handful of companies working to build A.I. using as many specialized chips as they can get their hands on. Amazon said that if companies like Anthropic used Amazon’s chips on an increasingly large scale and even helped design future chips, doing so could reduce the cost and improve the performance of these processors. Anthropic declined to comment.
But none of these companies will overtake Nvidia anytime soon. Its chips may be pricey, but are among the fastest on the market. And the company will continue to improve their speed.
Mr. Rao said his company, Databricks, trained some experimental A.I. systems using Amazon’s A.I. chips, but built its largest and most important systems using Nvidia chips because they provided higher performance and played nicely with a wider range of software.
“We have many years of hard innovation ahead of us,” Amazon’s Mr. Brown said. “Nvidia is not going to be standing still.”
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Cade Metz writes about artificial intelligence, driverless cars, robotics, virtual reality and other emerging areas of technology. More about Cade Metz
Karen Weise writes about technology and is based in Seattle. Her coverage focuses on Amazon and Microsoft, two of the most powerful companies in America. More about Karen Weise
Mike Isaac is a technology correspondent for The Times based in San Francisco. He regularly covers Facebook and Silicon Valley. More about Mike Isaac
Nvidia’s Big Tech Rivals Put Their Own A.I. Chips on the Table - The New York Times (nytimes.com) |
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