Jensen Huang

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DOJ subpoenas Nvidia in deepening AI antitrust probe, report says

AI chips, antitrust, Antitrust law, Artificial Intelligence, competition law, GPUs, Jensen Huang, monopoly, NVIDIA, Policy, run:ai / /
DOJ subpoenas Nvidia in deepening AI antitrust probe, report says

The Department of Justice is reportedly deepening its probe into Nvidia. Officials have moved on from merely questioning competitors to subpoenaing Nvidia and other tech companies for evidence that could substantiate allegations that Nvidia is abusing its “dominant position in AI computing,” Bloomberg reported.

When news of the DOJ’s probe into the trillion-dollar company was first reported in June, Fast Company reported that scrutiny was intensifying merely because Nvidia was estimated to control “as much as 90 percent of the market for chips” capable of powering AI models. Experts told Fast Company that the DOJ probe might even be good for Nvidia’s business, noting that the market barely moved when the probe was first announced.

But the market’s confidence seemed to be shaken a little more on Tuesday, when Nvidia lost a “record-setting $279 billion” in market value following Bloomberg’s report. Nvidia’s losses became “the biggest single-day market-cap decline on record,” TheStreet reported.

People close to the DOJ’s investigation told Bloomberg that the DOJ’s “legally binding requests” require competitors “to provide information” on Nvidia’s suspected anticompetitive behaviors as a “dominant provider of AI processors.”

One concern is that Nvidia may be giving “preferential supply and pricing to customers who use its technology exclusively or buy its complete systems,” sources told Bloomberg. The DOJ is also reportedly probing Nvidia’s acquisition of RunAI—suspecting the deal may lock RunAI customers into using Nvidia chips.

Bloomberg’s report builds on a report last month from The Information that said that Advanced Micro Devices Inc. (AMD) and other Nvidia rivals were questioned by the DOJ—as well as third parties who could shed light on whether Nvidia potentially abused its market dominance in AI chips to pressure customers into buying more products.

According to Bloomberg’s sources, the DOJ is worried that “Nvidia is making it harder to switch to other suppliers and penalizes buyers that don’t exclusively use its artificial intelligence chips.”

In a statement to Bloomberg, Nvidia insisted that “Nvidia wins on merit, as reflected in our benchmark results and value to customers, who can choose whatever solution is best for them.” Additionally, Bloomberg noted that following a chip shortage in 2022, Nvidia CEO Jensen Huang has said that his company strives to prevent stockpiling of Nvidia’s coveted AI chips by prioritizing customers “who can make use of his products in ready-to-go data centers.”

Potential threats to Nvidia’s dominance

Despite the slump in shares, Nvidia’s market dominance seems unlikely to wane any time soon after its stock more than doubled this year. In an SEC filing this year, Nvidia bragged that its “accelerated computing ecosystem is bringing AI to every enterprise” with an “ecosystem” spanning “nearly 5 million developers and 40,000 companies.” Nvidia specifically highlighted that “more than 1,600 generative AI companies are building on Nvidia,” and according to Bloomberg, Nvidia will close out 2024 with more profits than the total sales of its closest competitor, AMD.

After the DOJ’s most recent big win, which successfully proved that Google has a monopoly on search, the DOJ appears intent on getting ahead of any tech companies’ ambitions to seize monopoly power and essentially become the Google of the AI industry. In June, DOJ antitrust chief Jonathan Kanter confirmed to the Financial Times that the DOJ is examining “monopoly choke points and the competitive landscape” in AI beyond just scrutinizing Nvidia.

According to Kanter, the DOJ is scrutinizing all aspects of the AI industry—”everything from computing power and the data used to train large language models, to cloud service providers, engineering talent and access to essential hardware such as graphics processing unit chips.” But in particular, the DOJ appears concerned that GPUs like Nvidia’s advanced AI chips remain a “scarce resource.” Kanter told the Financial Times that an “intervention” in “real time” to block a potential monopoly could be “the most meaningful intervention” and the least “invasive” as the AI industry grows.

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AI’s future in grave danger from Nvidia’s chokehold on chips, groups warn

AI chips, ai industry, Antitrust law, Artificial Intelligence, department of justice, intel, Jensen Huang, monopoly, NVIDIA, Policy / /

Controlling “the world’s computing destiny” —

Anti-monopoly groups want DOJ to probe Nvidia’s AI chip bundling, alleged price-fixing.

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AI’s future in grave danger from Nvidia’s chokehold on chips, groups warn

Sen. Elizabeth Warren (D-Mass.) has joined progressive groups—including Demand Progress, Open Markets Institute, and the Tech Oversight Project—pressuring the US Department of Justice to investigate Nvidia’s dominance in the AI chip market due to alleged antitrust concerns, Reuters reported.

In a letter to the DOJ’s chief antitrust enforcer, Jonathan Kanter, groups demanding more Big Tech oversight raised alarms that Nvidia’s top rivals apparently “are struggling to gain traction” because “Nvidia’s near-absolute dominance of the market is difficult to counter” and “funders are wary of backing its rivals.”

Nvidia is currently “the world’s most valuable public company,” their letter said, worth more than $3 trillion after taking near-total control of the high-performance AI chip market. Particularly “astonishing,” the letter said, was Nvidia’s dominance in the market for GPU accelerator chips, which are at the heart of today’s leading AI. Groups urged Kanter to probe Nvidia’s business practices to ensure that rivals aren’t permanently blocked from competing.

According to the advocacy groups that strongly oppose Big Tech monopolies, Nvidia “now holds an 80 percent overall global market share in GPU chips and a 98 percent share in the data center market.” This “puts it in a position to crowd out competitors and set global pricing and the terms of trade,” the letter warned.

Earlier this year, inside sources reported that the DOJ and the Federal Trade Commission reached a deal where the DOJ would probe Nvidia’s alleged anti-competitive behavior in the booming AI industry, and the FTC would probe OpenAI and Microsoft. But there has been no official Nvidia probe announced, prompting progressive groups to push harder for the DOJ to recognize what they view as a “dire danger to the open market” that “well deserves DOJ scrutiny.”

Ultimately, the advocacy groups told Kanter that they fear Nvidia wielding “control over the world’s computing destiny,” noting that Nvidia’s cloud computing data centers don’t just power “Big Tech’s consumer products” but also “underpin every aspect of contemporary society, including the financial system, logistics, healthcare, and defense.”

They claimed that Nvidia is “leveraging” its “scarce chips” to force customers to buy its “chips, networking, and programming software as a package.” Such bundling and “price-fixing,” their letter warned, appear to be “the same kinds of anti-competitive tactics that the courts, in response to actions brought by the Department of Justice against other companies, have found to be illegal” and could perhaps “stifle innovation.”

Although data from TechInsights suggested that Nvidia’s chip shortage and cost actually helped companies like AMD and Intel sell chips in 2023, both Nvidia rivals reported losses in market share earlier this year, Yahoo Finance reported.

Perhaps most closely monitoring Nvidia’s dominance, France antitrust authorities launched an investigation into Nvidia last month over antitrust concerns, the letter said, “making it the first enforcer to act against the computer chip maker,” Reuters reported.

Since then, the European Union and the United Kingdom, as well as the US, have heightened scrutiny, but their seeming lag to follow through with an official investigation may only embolden Nvidia, as the company allegedly “believes its market behavior is above the law,” the progressive groups wrote. Suspicious behavior includes allegations that “Nvidia has continued to sell chips to Chinese customers and provide them computing access” despite a “Department of Commerce ban on trading with Chinese companies due to national security and human rights concerns.”

“Its chips have been confirmed to be reaching blacklisted Chinese entities,” their letter warned, citing a Wall Street Journal report.

Nvidia’s dominance apparently impacts everyone involved with AI. According to the letter, Nvidia seemingly “determining who receives inventory from a limited supply, setting premium pricing, and contractually blocking customers from doing business with competitors” is “alarming” the entire AI industry. That includes “both small companies (who find their supply choked off) and the Big Tech AI giants.”

Kanter will likely be receptive to the letter. In June, Fast Company reported that Kanter told an audience at an AI conference that there are “structures and trends in AI that should give us pause.” He further suggested that any technology that “relies on massive amounts of data and computing power” can “give already dominant firms a substantial advantage,” according to Fast Company’s summary of his remarks.

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Nvidia jumps ahead of itself and reveals next-gen “Rubin” AI chips in keynote tease

AI, AI accelerators, Biz & IT, Blackwell, chatgpt, chatgtp, computex, GH200, GPU, GPUs, Grace Hopper Superchip, Jensen Huang, machine learning, NVIDIA, Nvidia Rubin, Taiwan, Vera CPU, Vera Rubin / /

Swing beat —

“I’m not sure yet whether I’m going to regret this,” says CEO Jensen Huang at Computex 2024.

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Nvidia's CEO Jensen Huang delivers his keystone speech ahead of Computex 2024 in Taipei on June 2, 2024.

Enlarge / Nvidia’s CEO Jensen Huang delivers his keystone speech ahead of Computex 2024 in Taipei on June 2, 2024.

On Sunday, Nvidia CEO Jensen Huang reached beyond Blackwell and revealed the company’s next-generation AI-accelerating GPU platform during his keynote at Computex 2024 in Taiwan. Huang also detailed plans for an annual tick-tock-style upgrade cycle of its AI acceleration platforms, mentioning an upcoming Blackwell Ultra chip slated for 2025 and a subsequent platform called “Rubin” set for 2026.

Nvidia’s data center GPUs currently power a large majority of cloud-based AI models, such as ChatGPT, in both development (training) and deployment (inference) phases, and investors are keeping a close watch on the company, with expectations to keep that run going.

During the keynote, Huang seemed somewhat hesitant to make the Rubin announcement, perhaps wary of invoking the so-called Osborne effect, whereby a company’s premature announcement of the next iteration of a tech product eats into the current iteration’s sales. “This is the very first time that this next click as been made,” Huang said, holding up his presentation remote just before the Rubin announcement. “And I’m not sure yet whether I’m going to regret this or not.”

Nvidia Keynote at Computex 2023.

The Rubin AI platform, expected in 2026, will use HBM4 (a new form of high-bandwidth memory) and NVLink 6 Switch, operating at 3,600GBps. Following that launch, Nvidia will release a tick-tock iteration called “Rubin Ultra.” While Huang did not provide extensive specifications for the upcoming products, he promised cost and energy savings related to the new chipsets.

During the keynote, Huang also introduced a new ARM-based CPU called “Vera,” which will be featured on a new accelerator board called “Vera Rubin,” alongside one of the Rubin GPUs.

Much like Nvidia’s Grace Hopper architecture, which combines a “Grace” CPU and a “Hopper” GPU to pay tribute to the pioneering computer scientist of the same name, Vera Rubin refers to Vera Florence Cooper Rubin (1928–2016), an American astronomer who made discoveries in the field of deep space astronomy. She is best known for her pioneering work on galaxy rotation rates, which provided strong evidence for the existence of dark matter.

A calculated risk

Nvidia CEO Jensen Huang reveals the

Enlarge / Nvidia CEO Jensen Huang reveals the “Rubin” AI platform for the first time during his keynote at Computex 2024 on June 2, 2024.

Nvidia’s reveal of Rubin is not a surprise in the sense that most big tech companies are continuously working on follow-up products well in advance of release, but it’s notable because it comes just three months after the company revealed Blackwell, which is barely out of the gate and not yet widely shipping.

At the moment, the company seems to be comfortable leapfrogging itself with new announcements and catching up later; Nvidia just announced that its GH200 Grace Hopper “Superchip,” unveiled one year ago at Computex 2023, is now in full production.

With Nvidia stock rising and the company possessing an estimated 70–95 percent of the data center GPU market share, the Rubin reveal is a calculated risk that seems to come from a place of confidence. That confidence could turn out to be misplaced if a so-called “AI bubble” pops or if Nvidia misjudges the capabilities of its competitors. The announcement may also stem from pressure to continue Nvidia’s astronomical growth in market cap with nonstop promises of improving technology.

Accordingly, Huang has been eager to showcase the company’s plans to continue pushing silicon fabrication tech to its limits and widely broadcast that Nvidia plans to keep releasing new AI chips at a steady cadence.

“Our company has a one-year rhythm. Our basic philosophy is very simple: build the entire data center scale, disaggregate and sell to you parts on a one-year rhythm, and we push everything to technology limits,” Huang said during Sunday’s Computex keynote.

Despite Nvidia’s recent market performance, the company’s run may not continue indefinitely. With ample money pouring into the data center AI space, Nvidia isn’t alone in developing accelerator chips. Competitors like AMD (with the Instinct series) and Intel (with Guadi 3) also want to win a slice of the data center GPU market away from Nvidia’s current command of the AI-accelerator space. And OpenAI’s Sam Altman is trying to encourage diversified production of GPU hardware that will power the company’s next generation of AI models in the years ahead.

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Nvidia announces “moonshot” to create embodied human-level AI in robot form

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Here come the robots —

As companies race to pair AI with general-purpose humanoid robots, Nvidia’s GR00T emerges.

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An illustration of a humanoid robot created by Nvidia.

Enlarge / An illustration of a humanoid robot created by Nvidia.

Nvidia

In sci-fi films, the rise of humanlike artificial intelligence often comes hand in hand with a physical platform, such as an android or robot. While the most advanced AI language models so far seem mostly like disembodied voices echoing from an anonymous data center, they might not remain that way for long. Some companies like Google, Figure, Microsoft, Tesla, Boston Dynamics, and others are working toward giving AI models a body. This is called “embodiment,” and AI chipmaker Nvidia wants to accelerate the process.

“Building foundation models for general humanoid robots is one of the most exciting problems to solve in AI today,” said Nvidia CEO Jensen Huang in a statement. Huang spent a portion of Nvidia’s annual GTC conference keynote on Monday going over Nvidia’s robotics efforts. “The next generation of robotics will likely be humanoid robotics,” Huang said. “We now have the necessary technology to imagine generalized human robotics.”

To that end, Nvidia announced Project GR00T, a general-purpose foundation model for humanoid robots. As a type of AI model itself, Nvidia hopes GR00T (which stands for “Generalist Robot 00 Technology” but sounds a lot like a famous Marvel character) will serve as an AI mind for robots, enabling them to learn skills and solve various tasks on the fly. In a tweet, Nvidia researcher Linxi “Jim” Fan called the project “our moonshot to solve embodied AGI in the physical world.”

AGI, or artificial general intelligence, is a poorly defined term that usually refers to hypothetical human-level AI (or beyond) that can learn any task a human could without specialized training. Given a capable enough humanoid body driven by AGI, one could imagine fully autonomous robotic assistants or workers. Of course, some experts think that true AGI is long way off, so it’s possible that Nvidia’s goal is more aspirational than realistic. But that’s also what makes Nvidia’s plan a moonshot.

NVIDIA Robotics: A Journey From AVs to Humanoids.

“The GR00T model will enable a robot to understand multimodal instructions, such as language, video, and demonstration, and perform a variety of useful tasks,” wrote Fan on X. “We are collaborating with many leading humanoid companies around the world, so that GR00T may transfer across embodiments and help the ecosystem thrive.” We reached out to Nvidia researchers, including Fan, for comment but did not hear back by press time.

Nvidia is designing GR00T to understand natural language and emulate human movements, potentially allowing robots to learn coordination, dexterity, and other skills necessary for navigating and interacting with the real world like a person. And as it turns out, Nvidia says that making robots shaped like humans might be the key to creating functional robot assistants.

The humanoid key

Robotics startup figure, an Nvidia partner, recently showed off its humanoid

Enlarge / Robotics startup figure, an Nvidia partner, recently showed off its humanoid “Figure 01” robot.

Figure

So far, we’ve seen plenty of robotics platforms that aren’t human-shaped, including robot vacuum cleaners, autonomous weed pullers, industrial units used in automobile manufacturing, and even research arms that can fold laundry. So why focus on imitating the human form? “In a way, human robotics is likely easier,” said Huang in his GTC keynote. “And the reason for that is because we have a lot more imitation training data that we can provide robots, because we are constructed in a very similar way.”

That means that researchers can feed samples of training data captured from human movement into AI models that control robot movement, teaching them how to better move and balance themselves. Also, humanoid robots are particularly convenient because they can fit anywhere a person can, and we’ve designed a world of physical objects and interfaces (such as tools, furniture, stairs, and appliances) to be used or manipulated by the human form.

Along with GR00T, Nvidia also debuted a new computer platform called Jetson Thor, based on NVIDIA’s Thor system-on-a-chip (SoC), as part of the new Blackwell GPU architecture, which it hopes will power this new generation of humanoid robots. The SoC reportedly includes a transformer engine capable of 800 teraflops of 8-bit floating point AI computation for running models like GR00T.

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Nvidia unveils Blackwell B200, the “world’s most powerful chip” designed for AI

AI, AI inference, Biz & IT, chatgpt, chatgtp, GPU, GPUs, Jensen Huang, large language models, LLMs, machine learning, NVIDIA, openai, supercomputers / /

There’s no knowing where we’re rowing —

208B transistor chip can reportedly reduce AI cost and energy consumption by up to 25x.

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The GB200

Enlarge / The GB200 “superchip” covered with a fanciful blue explosion.

Nvidia / Benj Edwards

On Monday, Nvidia unveiled the Blackwell B200 tensor core chip—the company’s most powerful single-chip GPU, with 208 billion transistors—which Nvidia claims can reduce AI inference operating costs (such as running ChatGPT) and energy consumption by up to 25 times compared to the H100. The company also unveiled the GB200, a “superchip” that combines two B200 chips and a Grace CPU for even more performance.

The news came as part of Nvidia’s annual GTC conference, which is taking place this week at the San Jose Convention Center. Nvidia CEO Jensen Huang delivered the keynote Monday afternoon. “We need bigger GPUs,” Huang said during his keynote. The Blackwell platform will allow the training of trillion-parameter AI models that will make today’s generative AI models look rudimentary in comparison, he said. For reference, OpenAI’s GPT-3, launched in 2020, included 175 billion parameters. Parameter count is a rough indicator of AI model complexity.

Nvidia named the Blackwell architecture after David Harold Blackwell, a mathematician who specialized in game theory and statistics and was the first Black scholar inducted into the National Academy of Sciences. The platform introduces six technologies for accelerated computing, including a second-generation Transformer Engine, fifth-generation NVLink, RAS Engine, secure AI capabilities, and a decompression engine for accelerated database queries.

Press photo of the Grace Blackwell GB200 chip, which combines two B200 GPUs with a Grace CPU into one chip.

Enlarge / Press photo of the Grace Blackwell GB200 chip, which combines two B200 GPUs with a Grace CPU into one chip.

Several major organizations, such as Amazon Web Services, Dell Technologies, Google, Meta, Microsoft, OpenAI, Oracle, Tesla, and xAI, are expected to adopt the Blackwell platform, and Nvidia’s press release is replete with canned quotes from tech CEOs (key Nvidia customers) like Mark Zuckerberg and Sam Altman praising the platform.

GPUs, once only designed for gaming acceleration, are especially well suited for AI tasks because their massively parallel architecture accelerates the immense number of matrix multiplication tasks necessary to run today’s neural networks. With the dawn of new deep learning architectures in the 2010s, Nvidia found itself in an ideal position to capitalize on the AI revolution and began designing specialized GPUs just for the task of accelerating AI models.

Nvidia’s data center focus has made the company wildly rich and valuable, and these new chips continue the trend. Nvidia’s gaming GPU revenue ($2.9 billion in the last quarter) is dwarfed in comparison to data center revenue (at $18.4 billion), and that shows no signs of stopping.

A beast within a beast

Press photo of the Nvidia GB200 NVL72 data center computer system.

Enlarge / Press photo of the Nvidia GB200 NVL72 data center computer system.

The aforementioned Grace Blackwell GB200 chip arrives as a key part of the new NVIDIA GB200 NVL72, a multi-node, liquid-cooled data center computer system designed specifically for AI training and inference tasks. It combines 36 GB200s (that’s 72 B200 GPUs and 36 Grace CPUs total), interconnected by fifth-generation NVLink, which links chips together to multiply performance.

A specification chart for the Nvidia GB200 NVL72 system.

Enlarge / A specification chart for the Nvidia GB200 NVL72 system.

“The GB200 NVL72 provides up to a 30x performance increase compared to the same number of NVIDIA H100 Tensor Core GPUs for LLM inference workloads and reduces cost and energy consumption by up to 25x,” Nvidia said.

That kind of speed-up could potentially save money and time while running today’s AI models, but it will also allow for more complex AI models to be built. Generative AI models—like the kind that power Google Gemini and AI image generators—are famously computationally hungry. Shortages of compute power have widely been cited as holding back progress and research in the AI field, and the search for more compute has led to figures like OpenAI CEO Sam Altman trying to broker deals to create new chip foundries.

While Nvidia’s claims about the Blackwell platform’s capabilities are significant, it’s worth noting that its real-world performance and adoption of the technology remain to be seen as organizations begin to implement and utilize the platform themselves. Competitors like Intel and AMD are also looking to grab a piece of Nvidia’s AI pie.

Nvidia says that Blackwell-based products will be available from various partners starting later this year.

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