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Nvidia CEO Jensen Huang speaks during a press conference at The MGM during CES 2018 in Las Vegas on January 7, 2018.

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Software that can write passages of text or draw pictures that look like a human created them has kicked off a gold rush in the technology industry.

Companies like Microsoft and Google are fighting to integrate cutting-edge AI into their search engines, as billion-dollar competitors such as OpenAI and Stable Diffusion race ahead and release their software to the public.

Powering many of these applications is a roughly $10,000 chip that’s become one of the most critical tools in the artificial intelligence industry: The Nvidia A100.

The A100 has become the “workhorse” for artificial intelligence professionals at the moment, said Nathan Benaich, an investor who publishes a newsletter and report covering the AI industry, including a partial list of supercomputers using A100s. Nvidia takes 95% of the market for graphics processors that can be used for machine learning, according to New Street Research.

A.I. is the catalyst behind Nividia's earnings beat, says Susquehanna's Christopher Rolland

The A100 is ideally suited for the kind of machine learning models that power tools like ChatGPT, Bing AI, or Stable Diffusion. It’s able to perform many simple calculations simultaneously, which is important for training and using neural network models.

The technology behind the A100 was initially used to render sophisticated 3D graphics in games. It’s often called a graphics processor, or GPU, but these days Nvidia’s A100 is configured and targeted at machine learning tasks and runs in data centers, not inside glowing gaming PCs.

Big companies or startups working on software like chatbots and image generators require hundreds or thousands of Nvidia’s chips, and either purchase them on their own or secure access to the computers from a cloud provider.

Hundreds of GPUs are required to train artificial intelligence models, like large language models. The chips need to be powerful enough to crunch terabytes of data quickly to recognize patterns. After that, GPUs like the A100 are also needed for “inference,” or using the model to generate text, make predictions, or identify objects inside photos.

This means that AI companies need access to a lot of A100s. Some entrepreneurs in the space even see the number of A100s they have access to as a sign of progress.

“A year ago we had 32 A100s,” Stability AI CEO Emad Mostaque wrote on Twitter in January. “Dream big and stack moar GPUs kids. Brrr.” Stability AI is the company that helped develop Stable Diffusion, an image generator that drew attention last fall, and reportedly has a valuation of over $1 billion.

Now, Stability AI has access to over 5,400 A100 GPUs, according to one estimate from the State of AI report, which charts and tracks which companies and universities have the largest collection of A100 GPUs — although it doesn’t include cloud providers, which don’t publish their numbers publicly.

Nvidia’s riding the A.I. train

Nvidia stands to benefit from the AI hype cycle. During Wednesday’s fiscal fourth-quarter earnings report, although overall sales declined 21%, investors pushed the stock up about 14% on Thursday, mainly because the company’s AI chip business — reported as data centers — rose by 11% to more than $3.6 billion in sales during the quarter, showing continued growth.

Nvidia shares are up 65% so far in 2023, outpacing the S&P 500 and other semiconductor stocks alike.

Nvidia CEO Jensen Huang couldn’t stop talking about AI on a call with analysts on Wednesday, suggesting that the recent boom in artificial intelligence is at the center of the company’s strategy.

“The activity around the AI infrastructure that we built, and the activity around inferencing using Hopper and Ampere to influence large language models has just gone through the roof in the last 60 days,” Huang said. “There’s no question that whatever our views are of this year as we enter the year has been fairly dramatically changed as a result of the last 60, 90 days.”

Ampere is Nvidia’s code name for the A100 generation of chips. Hopper is the code name for the new generation, including H100, which recently started shipping.

More computers needed

Nvidia A100 processor

Nvidia

Compared to other kinds of software, like serving a webpage, which uses processing power occasionally in bursts for microseconds, machine learning tasks can take up the whole computer’s processing power, sometimes for hours or days.

This means companies that find themselves with a hit AI product often need to acquire more GPUs to handle peak periods or improve their models.

These GPUs aren’t cheap. In addition to a single A100 on a card that can be slotted into an existing server, many data centers use a system that includes eight A100 GPUs working together.

This system, Nvidia’s DGX A100, has a suggested price of nearly $200,000, although it comes with the chips needed. On Wednesday, Nvidia said it would sell cloud access to DGX systems directly, which will likely reduce the entry cost for tinkerers and researchers.

It’s easy to see how the cost of A100s can add up.

For example, an estimate from New Street Research found that the OpenAI-based ChatGPT model inside Bing’s search could require 8 GPUs to deliver a response to a question in less than one second.

At that rate, Microsoft would need over 20,000 8-GPU servers just to deploy the model in Bing to everyone, suggesting Microsoft’s feature could cost $4 billion in infrastructure spending.

“If you’re from Microsoft, and you want to scale that, at the scale of Bing, that’s maybe $4 billion. If you want to scale at the scale of Google, which serves 8 or 9 billion queries every day, you actually need to spend $80 billion on DGXs.” said Antoine Chkaiban, a technology analyst at New Street Research. “The numbers we came up with are huge. But they’re simply the reflection of the fact that every single user taking to such a large language model requires a massive supercomputer while they’re using it.”

The latest version of Stable Diffusion, an image generator, was trained on 256 A100 GPUs, or 32 machines with 8 A100s each, according to information online posted by Stability AI, totaling 200,000 compute hours.

At the market price, training the model alone cost $600,000, Stability AI CEO Mostaque said on Twitter, suggesting in a tweet exchange the price was unusually inexpensive compared to rivals. That doesn’t count the cost of “inference,” or deploying the model.

Huang, Nvidia’s CEO, said in an interview with CNBC’s Katie Tarasov that the company’s products are actually inexpensive for the amount of computation that these kinds of models need.

“We took what otherwise would be a $1 billion data center running CPUs, and we shrunk it down into a data center of $100 million,” Huang said. “Now, $100 million, when you put that in the cloud and shared by 100 companies, is almost nothing.”

Huang said that Nvidia’s GPUs allow startups to train models for a much lower cost than if they used a traditional computer processor.

“Now you could build something like a large language model, like a GPT, for something like $10, $20 million,” Huang said. “That’s really, really affordable.”

New competition

Nvidia isn’t the only company making GPUs for artificial intelligence uses. AMD and Intel have competing graphics processors, and big cloud companies like Google and Amazon are developing and deploying their own chips specially designed for AI workloads.

Still, “AI hardware remains strongly consolidated to NVIDIA,” according to the State of AI compute report. As of December, more than 21,000 open-source AI papers said they used Nvidia chips.

Most researchers included in the State of AI Compute Index used the V100, Nvidia’s chip that came out in 2017, but A100 grew fast in 2022 to be the third-most used Nvidia chip, just behind a $1500-or-less consumer graphics chip originally intended for gaming.

The A100 also has the distinction of being one of only a few chips to have export controls placed on it because of national defense reasons. Last fall, Nvidia said in an SEC filing that the U.S. government imposed a license requirement barring the export of the A100 and the H100 to China, Hong Kong, and Russia.

“The USG indicated that the new license requirement will address the risk that the covered products may be used in, or diverted to, a ‘military end use’ or ‘military end user’ in China and Russia,” Nvidia said in its filing. Nvidia previously said it adapted some of its chips for the Chinese market to comply with U.S. export restrictions.

The fiercest competition for the A100 may be its successor. The A100 was first introduced in 2020, an eternity ago in chip cycles. The H100, introduced in 2022, is starting to be produced in volume — in fact, Nvidia recorded more revenue from H100 chips in the quarter ending in January than the A100, it said on Wednesday, although the H100 is more expensive per unit.

The H100, Nvidia says, is the first one of its data center GPUs to be optimized for transformers, an increasingly important technique that many of the latest and top AI applications use. Nvidia said on Wednesday that it wants to make AI training over 1 million percent faster. That could mean that, eventually, AI companies wouldn’t need so many Nvidia chips.

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U.S. lifts chip software curbs on China amid trade truce, Synopsys says

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U.S. lifts chip software curbs on China amid trade truce, Synopsys says

Synopsys logo is seen displayed on a smartphone with the flag of China in the background.

Sopa Images | Lightrocket | Getty Images

The U.S. government has rescinded its export restrictions on chip design software to China, U.S.-based Synopsys announced Thursday. 

“Synopsys is working to restore access to the recently restricted products in China,” it said in a statement

The U.S. had reportedly told several chip design software companies, including Synopsys, in May that they were required to obtain licenses before exporting goods, such as software and chemicals for semiconductors, to China. 

The U.S. Commerce Department did not immediately respond to a request for comment from CNBC.

The news comes after China signaled last week that they are making progress on a trade truce with the U.S. and confirmed conditional agreements to resume some exchanges of rare earths and advanced technology.

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Datadog stock jumps 10% on tech company’s inclusion in S&P 500 index

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Datadog stock jumps 10% on tech company’s inclusion in S&P 500 index

The Datadog stand is being displayed on day one of the AWS Summit Seoul 2024 at the COEX Convention and Exhibition Center in Seoul, South Korea, on May 16, 2024.

Chris Jung | Nurphoto | Getty Images

Datadog shares were up 10% in extended trading on Wednesday after S&P Global said the monitoring software provider will replace Juniper Networks in the S&P 500 U.S. stock index.

S&P Global is making the change effective before the beginning of trading on July 9, according to a statement.

Computer server maker Hewlett Packard Enterprise, also a constituent of the index, said earlier on Wednesday that it had completed its acquisition of Juniper, which makes data center networking hardware. HPE disclosed in a filing that it paid $13.4 billion to Juniper shareholders.

Over the weekend, the two companies reached a settlement with the U.S. Justice Department, which had sued in opposition to the deal. As part of the settlement, HPE agreed to divest its global Instant On campus and branch business.

While tech already makes up an outsized portion of the S&P 500, the index has has been continuously lifting its exposure as the industry expands into more areas of society.

DoorDash was the latest tech company to join during the last rebalancing in March. Cloud software vendor Workday was added in December, and that was preceded earlier in 2024 with the additions of Palantir, Dell, CrowdStrike, GoDaddy and Super Micro Computer.

Stocks often rally when they’re added to a major index, as fund managers need to rebalance their portfolios to reflect the changes.

New York-based Datadog went public in 2019. The company generated $24.6 million in net income on $761.6 million in revenue in the first quarter of 2025, according to a statement. Competitors include Cisco, which bought Splunk last year, as well as Elastic and cloud infrastructure providers such as Amazon and Microsoft.

Datadog has underperformed the broader tech sector so far this year. The stock was down 5.5% as of Wednesday’s close, while the Nasdaq was up 5.6%. Still, with a market cap of $46.6 billion, Datadog’s valuation is significantly higher than the median for that index.

— CNBC’s Ari Levy contributed to this report.

CNBC: Datadog CEO Olivier Pomel on the cloud computing outlook

Datadog CEO Olivier Pomel on the cloud computing outlook

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Ether and related stocks gain amid the latest crypto craze: Tokenization

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Ether and related stocks gain amid the latest crypto craze: Tokenization

A representation of cryptocurrency Ethereum is placed on a PC motherboard in this illustration taken on June 16, 2023.

Dado Ruvic | Reuters

Stocks tied to the price of ether, better known as ETH, were higher on Wednesday, reflecting renewed enthusiasm for the crypto asset amid a surge of interest in stablecoins and tokenization.

BitMine Immersion Technologies, a bitcoin miner that announced plans this week to make ETH its primary treasury reserve asset, jumped about 20%. It’s gained more than 1,000% since the announcement. Betting platform SharpLink Gaming, which has also initiated an ETH treasury strategy, added more than 11%. Bit Digital, which last week exited bitcoin mining to focus on its ETH treasury and staking plans, jumped more than 6%.

“We’re finally at the point where real use cases are emerging, and stablecoins have been the first version of that at scale but they’re going to open the door to a much bigger story around tokenizing other assets and using digital assets in new ways,” Devin Ryan, head of financial technology research at Citizens.

On Tuesday, as bitcoin ETFs snapped a 15-day streak of inflows, ether ETFs saw $40 million in inflows led by BlackRock’s iShares Ethereum Trust. ETH ETFs came back to life in June after much concern that they were becoming zombie funds.

The price of the coin itself was last higher by 5%, according to Coin Metrics, though it’s still down 24% this year.

Ethereum has been struggling with an identity crisis fueled by uncertainty about the network’s value proposition, weaker revenue since its last big technical upgrade and increasing competition from Solana. Market volatility, driven by geopolitical uncertainty this year, has not helped.

The Ethereum network’s smart contracts capability makes it a prominent platform for the tokenization of traditional assets, which includes U.S. dollar-pegged stablecoins. Fundstrat’s Tom Lee this week called Ethereum “the backbone and architecture” of stablecoins. Both Tether (USDT) and Circle‘s USD Coin (USDC) are issued on the network.

Fundstrat's Tom Lee on being named chairman of BitMine Immersion Technologies

BlackRock’s tokenized money market fund (known as BUIDL, which stands for USD Institutional Digital Liquidity Fund) also launched on Ethereum last year before expanding to other blockchain networks.

Tokenization is the process of issuing digital representations on a blockchain network of publicly traded securities, real world assets or any other form of value. Holders of tokenized assets don’t have outright ownership of the assets themselves.

The latest wave of interest in ETH-related assets follows an announcement by Robinhood this week that it will enable trading of tokenized U.S. stocks and ETFs across Europe, after a groundswell of interest in stablecoins throughout June following Circle’s IPO and the Senate passage of its proposed stablecoin bill, the GENIUS Act.

Ether, which turns 10 years old at the end of July, is sitting about 75% off its all-time high.

Don’t miss these cryptocurrency insights from CNBC Pro:

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