Tag Archives: Nvidias

Trump Sparks Concerns Over Nvidia’s Potential Sale of Advanced AI Chips in China

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Donald Trump has indicated that Nvidia can sell more advanced chips in China than is currently allowed.

During a Monday briefing, Trump addressed the recent development, revealing his groundbreaking agreements with NVIDIA and AMD. He has authorized an export license allowing the sale of previously restricted chips to China, with the US government receiving 15% of the sales revenue. The US president defended the deal after analysts labeled it as potentially resembling “shakedown” payments or unconstitutional export taxes. He expressed hope for further negotiations regarding a more advanced Nvidia chip.

Trump mentioned that Nvidia’s latest chip, Blackwell, would not be available for trade, but he is considering trading “a slightly negatively impacted version of Blackwell,” which could see a downgrade of 30-50%.

“I believe he’ll be back to discuss it, but it will be a significant yet unenhanced version,” he remarked, referring to Nvidia’s CEO Jensen Huang, who has had multiple discussions with Trump about China’s export limits.



Huang has yet to comment on the revenue-sharing agreement pertaining to the sales of Nvidia’s H20 chips and AMD’s Mi308 chips in China.

The H20 and Mi308 chips were prohibited from being sold to China in April, even though the low-power H20 was specially designed to meet the restrictions set by the Biden administration. Nvidia previously stated last month that they hoped to receive clearance to resume shipments soon.

Nvidia’s impact is a major driver of the AI boom, garnering significant interest from both China and the US, which has led to heightened scrutiny among analysts in Washington and concerns from Chinese officials.

“I’m worried about reports indicating the US government might take revenue from sales of chips akin to advanced H20 sales,” he told the Financial Times.

Trump justified the agreement on Monday: “I stated, ‘Listen, I want 20% if I approve this for you,'” emphasizing that he hasn’t received any personal money from the deal. He suggested that Huang provided 15% as part of the agreement.

“I permitted him only for the H20,” Trump clarified.

He referred to the H20 as an “outdated” chip that is “already in a different form for China.”

However, Harry Cleja, research director at the Washington office of the Carnegie Mellon Institute of Strategic Technology, labeled the H20 as a “second tier” AI chip.

“The H20 is not the premier training chip available, but the type of computing dominating AI tasks today—particularly the ‘inference’ model and ‘agent’ products—are what the field is focused on,” Kresja told the Guardian, referring to systems employing advanced inference to autonomously resolve complex issues.

“Lifting H20 export restrictions undoubtedly provides Beijing with the necessary tools to compete in the AI realm.”

The US government has been attempting for several years to defend national security, especially concerning artificial intelligence development and the provision of technology that could be weaponized.

China’s Foreign Ministry remarked on Monday that the country has consistently articulated its stance on US chip exports, accusing Washington of utilizing technology and trade measures to “maliciously suppress and hinder China.”

Revenue-sharing contracts are quite rare in the US, reflecting Trump’s latest interference in corporate decisions after pressuring executives to reinvest in American manufacturing. He has requested the resignation of Intel’s new CEO, Lip-Bu Tan, regarding its connections with Chinese companies.

Trump has also suggested imposing 100% tariffs on the global semiconductor market, exempting businesses that commit to investing in the US.

Taiwan’s TSMC, a leading semiconductor manufacturer, announced plans in April to expand its US operations through a $100 million investment. However, foreign investments of this magnitude require government approval from Taiwan.

The Guardian confirmed that TSMC has yet to apply for this approval. The company has not responded to requests for comment.

Source: www.theguardian.com

Nvidia’s finances to take a $5.5 billion hit amid US restrictions on AI chip exports to China.

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Nvidia has announced that it is expecting a $5.5 billion (£4.1 billion) impact following the ban imposed by Donald Trump’s administration on chip designers selling crucial artificial intelligence chips in China.

In an official statement released late Tuesday, the company disclosed that the H20 AI chip, specifically tailored for the Chinese market to comply with export regulations, will now require a special license for sale in China indefinitely.

The US government, engaged in a competition with China for AI supremacy, informed Nvidia that new regulations have been enacted to mitigate the risk of their products being utilized in Chinese supercomputers.

As a result, the chip manufacturer is set to incur $5.5 billion in losses for the financial quarter ending on April 27th due to its investment in H20 chips.

Nvidia, known for driving significant advancements in AI technology, has delivered substantial returns for investors, with its stock surging over 1,400% since 2020, making it one of the few trillion-dollar companies in the US.

However, the news on Tuesday caused Nvidia’s stock to fall by approximately 6% in after-hours trading, potentially wiping out billions of dollars in market value by Wednesday’s opening bell.

In Asia, chipmakers like Samsung Electronics and SK Hynix from South Korea saw a 3% decline in their stocks overnight, while US competitors like senior equity microdevices dropped by 7% in after-hours trading.

Although the chip industry has been exempt from the 10% tariff that began on April 5th, Trump indicated this week that he plans to impose tariffs on imported semiconductors and mentioned that some companies in this sector may have flexibility.

The US Department of Commerce has recently launched an investigation into the impact of chip and drug imports on national security.

The US heavily relies on chip imports from Taiwan, with Trump previously imposing a 32% tariff on products from the country before suspending most tariffs last week.

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Nvidia also revealed plans to invest up to $500 million in AI infrastructure in the US over the next four years to bolster its American manufacturing presence. While Nvidia designs chips, it outsources production to contractors, including Taiwanese semiconductor manufacturers.

Under the Biden administration, US officials had initially barred Nvidia and other AI chip manufacturers from selling advanced chips to China in October 2022. In response, Chinese authorities tightened controls over the tools and processors necessary for semiconductor production.

Source: www.theguardian.com

Nvidia’s First Revenue after Chinese Deepseek’s Debut Shock

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Nvidia is set to release its revenue report for the fourth quarter of 2024 on Wednesday evening. Investors will be closely watching for any signs of slowing demand for semiconductor chips. The company’s financials have come under scrutiny amid concerns that the AI market boom may be coming to an end, leading to a stratospheric 3.1TN rating.

Analysts are hopeful that Nvidia will maintain its position as a leading chip manufacturer in the AI industry. However, recent developments pose new challenges to the company’s market dominance. For example, a report from TD Cowen revealed that Microsoft, one of Nvidia’s major customers, was canceling leases with private data center operators, raising concerns about the sustainability of AI infrastructure investments.

This earnings call will also provide insight into the company’s financials and demand following the introduction of the Chinese AI model, Deepseek ai, which has surpassed many US models while requiring less training and investment. The introduction of Deepseek has boosted Nvidia’s valuation significantly, signaling a shift in the AI landscape.

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Despite Nvidia’s strong performance in the past, analysts are now looking for indicators that the company can sustain its position in the AI chip market amidst evolving demands for AI models.

Jacob Bourne, a technology analyst at Emarketer, commented, “The key question regarding Nvidia’s fourth-quarter revenues is whether they can continue to lead the evolution of AI, not just in terms of numbers. Even if Nvidia shows another quarter of stellar growth, the market’s response will depend on its ability to address these challenges.”

While some analysts believe that the impact of Deepseek’s launch may not be immediate for Nvidia, they predict that competitors like AMD and Intel could gain a foothold in the AI infrastructure market.

“DeepSeek has opened up new possibilities for low-performance AI applications, particularly for inference models, allowing more organizations to experiment with AI,” noted Nguyen.

Source: www.theguardian.com

NVIDIA’s stock price drops as US ramps up antitrust probe

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Shares in AI chip designer Nvidia have been falling overnight following reports that US authorities are stepping up an investigation into whether the company has violated competition laws.

The company’s shares fell 2.4% in after-hours trading, supplementing a fall of nearly 10% in regular trading, sending its market capitalisation down by $279bn (£212bn) to $2.6trn, the biggest one-day fall ever for a US company.

Bloomberg reported that overnight, the Department of Justice sent subpoenas to Nvidia and other tech companies, taking steps to legally compel recipients to hand over information.

Nvidia executives are said to be concerned that the company is making it difficult for customers to switch to other semiconductor suppliers and penalizing buyers that refuse to give them exclusive use of Nvidia’s AI chips.

The moves mark an intensification of the U.S. antitrust investigation and bring the government one step closer to filing formal charges against Nvidia.

Tuesday’s sell-off came amid a market-wide sell-off sparked by weak U.S. manufacturing data that raised broader concerns among investors about the outlook for the U.S. economy. Manufacturing contracted at a moderate pace in August, with new orders, production and employment levels declining, according to the Institute for Supply Management’s monthly survey of factories.

That sent the S&P 500 down more than 2%, while the tech-heavy Nasdaq Composite Index fell nearly 3.3%. Uncertainty spread to Asia, where Japan’s Nikkei fell 4.2% on Wednesday and Australia’s S&P/ASX 200 index fell 1.9%.

This has exacerbated recent volatile trading for Nvidia and other AI-related stocks, including Google, Apple and Amazon, as investors worry that the real impact — and tangible benefits — of the much-touted AI revolution may still be a long way off.

Founded in 1993, Nvidia primarily designed chips for video games, but during the cryptocurrency boom it realized its processing technology could be used to mine digital coins. Since then, the company has shifted its focus to artificial intelligence, riding a new wave of excitement about the potential of large-scale language models.

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The company last week reported a 122% increase in second-quarter revenue, but signs of slowing growth, especially around its next-generation AI chip, code-named “Blackwell,” have spooked investors.

An Nvidia spokesman said: “We win on merit, as reflected in our benchmark results and value to customers, so they can choose the solution that’s best for them.”

Source: www.theguardian.com

Exploring the Exciting Features of Nvidia’s Latest AI Superchip

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Nvidia, a chipmaker, has once again solidified its position in artificial intelligence by introducing a new “superchip,” a quantum computing service, and a set of tools to assist in the development of general-purpose humanoid robots. Let’s delve into what the company is up to and the implications of their advancements.

What is NVIDIA doing?

At their annual development conference, Nvidia unveiled the latest generation of AI chips, known as the “Blackwell” series, used to power high-end data centers for training cutting-edge AI models like GPT, Claude, and Gemini. Among them is the Blackwell B200, an upgrade to the existing H100 AI chip. Using this new chip, training large AI models like GPT-4 would require significantly fewer chips and less power, potentially leading to more efficient power usage in the AI industry.

What makes a chip “super”?

In addition to the B200, Nvidia introduced the GB200 “superchip,” which combines two B200 chips on one board to enhance processing efficiency. This setup can significantly reduce energy consumption and improve overall performance, making it an attractive option for running advanced AI models like chatbots.

What if I want it to be bigger?

For those looking for even more power, Nvidia offers the GB200 NVL72, which can be configured with multiple B200 chips to create a powerful AI data center capable of handling complex tasks. Despite the high cost of these advanced chips, they provide significant capabilities for AI development.

What about my robot?

Nvidia’s Project GR00T aims to create foundational models for controlling humanoid robots, enabling them to understand natural language, mimic human behavior, and interact with the real world. Combined with technologies like Jetson Thor, Nvidia is paving the way for autonomous machines that can perform diverse tasks efficiently.

quantum?

While Nvidia is not directly involved in quantum cloud computing, they are venturing into this realm by offering a service that simulates quantum computing using AI chips. This enables researchers to test quantum ideas without the need for expensive quantum computers, with plans to provide access to third-party quantum computers in the future.

Source: www.theguardian.com

Is Nvidia’s $1 trillion valuation sustainable, or is Apple poised to take the crown?

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EEveryone wants to be like Apple. The world’s largest publicly traded company, with a flagship product that prints money, a cultural footprint that has reached world-historical significance, and the Ford of the 21st century.

At a surface level, it’s clear which companies get hammered in this comparison. If you send out a well-crafted, smartly designed home appliance in a nice box, someone somewhere will compare you to the Cupertino giant.

Digging a little deeper allows for more meaningful comparisons. Apple isn’t just defined by its style, it’s also defined by its focus. A small number of computers, phones, and tablets in a small number of configurations account for most of the revenue.

That focus has allowed the company to develop a reputation for quality. Of course, it also contributed to its formidable media strategy, making every product launch an industry event in a way that few have been able to imitate before. This is what I was thinking nearly a decade ago when I referred to gaming giant Blizzard, creator of World of Warcraft and Diablo, as “his Apple of gaming.” (Now owned by Microsoft and plagued by allegations of misconduct, Blizzard’s star has since fallen.)

But there’s something else that makes Apple what it is today, and it’s difficult for startups to imitate. The Apple they see is just the latest evolution of a company that was an industry giant before the latest generation of founders were born. The Apple II, Mac, and iMac all shaped computing for 25 years before the iPod turned Apple into a consumer electronics company. And the iPod gave Apple another decade of growth and sophistication, until the iPhone came along and created the behemoth it is today.

Now let’s talk about Nvidia.

$1 trillion is not cool

…

Source: www.theguardian.com

‘Incredible Valor’: The Legacy of Grace Hopper in Nvidia’s Monumental $2 Trillion Chip Empire | Computing

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In the demanding technical field of semiconductor manufacturing, hardcover book-sized processors stand out. Nvidia’s H-100. On Friday, the Santa Clara, Calif., company was valued at more than $2 trillion. The next step will likely be a chip named after U.S. Navy Rear Adm. “Amazing Grace” Hopper, who was instrumental in designing and implementing the programming language.


Nvidia supplies about 80% of the global market for chips used in AI applications. The company’s H-100 chips (the “H is for hopper”) are now so valuable that they have to be transported in armored vehicles, and demand is so great that some customers have to wait 6 months to receive it.

Hopper’s importance to Nvidia, and to AI computing more generally, was reinforced last summer when Nvidia founder and CEO Jensen Fan announced the next generation accelerated computing and generation AI chip, the GH200 Grace Hopper. It was emphasized when they named it a Super Chip.





Admiral Grace Hopper in 1985. Photo: Associated Press

Hopper was born in New York City in 1906, graduated from Vassar College in 1928 with degrees in mathematics and physics, and joined the Navy after the United States entered World War II following the attack on Pearl Harbor.

According to a biography from Yale University, Initially rejected by the Navy because of her age and small stature, she was commissioned and assigned to Harvard University’s Ship Bureau Computation Project, where she worked on the Mark I, America’s first electromechanical computer, calculating the rocket’s trajectory and reaction force, aircraft gun range table, and minesweeper calibration.

After the war, Hopper joined the Eckhart-Mauchly Computer Corporation (later Sperry Rand), where she pioneered the idea of automatic programming. In 1952, she developed the first compiler, a program that translated written instructions into computer code.

“What I was looking for when I started learning English [programming] was to bring in another whole group who could easily use computers. I kept asking for a more user-friendly language. Most of what we have learned from academics and computer science people has never been adapted to humans,” Hopper explained in a 1980 interview.

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Hopper retired as a rear admiral at age 79, making her the oldest active duty officer in the U.S. military. The year before her death in 1992, she was awarded the National Medal of Technology by President George H.W. Bush. She was posthumously awarded the Presidential Medal of Freedom, the highest civilian honor, in 2016.

In a 1983 interview on “60 Minutes”, Hopper was asked if the computer revolution was over. Hopper replied: “No, we’re just getting started. I got a Model T.”

Source: www.theguardian.com

An Interview with Nvidia’s Deepu Talla on Robotics Technology

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A version of this Q&A first appeared in Actuator, TechCrunch’s free robotics newsletter. Subscribe here.

We conclude our year-end robotics Q&A series with this entry by Deepu Talla. In October, he visited NVIDIA’s Bay Area headquarters. Talla has served as Vice President and General Manager of Embedded & Edge Computing for this leading chip company for over 10 years. He provides unique insight into the current state and future direction of robotics in 2023. Over the past few years, NVIDIA has established the leading platform for robotics simulation, prototyping, and deployment.

Previous Q&A:

Image credits: Nvidia

What role will generative AI play in the future of robotics?

We are already seeing productivity gains from generative AI across a variety of industries. It is clear that the impact of GenAI will be transformative across robotics, from simulation to design and more.

  • Simulation: Models can now accelerate simulation development by bridging the gap between 3D technical artists and developers by building scenes, building environments, and generating assets. These GenAI assets will see increased use in synthetic data generation, robotic skill training, and software testing.
  • Multimodal AI: Transformer-based models improve robots’ ability to better understand the world around them, allowing them to operate in more environments and complete complex tasks.
  • Robot (re)programming: Improves the ability to define tasks and functions in a simple language to make robots more versatile/multipurpose.
  • Design: Novel mechanical designs (end effectors, etc.) to improve efficiency.

What do you think about the humanoid form factor?

Designing autonomous robots is difficult. Humanoids are even more difficult. Unlike most of his AMRs, which primarily understand floor-level obstacles, humanoids are locomotion manipulators that require multimodal AI to have a deeper understanding of their surrounding environment. It requires a huge amount of sensor processing, advanced control, and skill execution.

Breakthroughs in generative AI capabilities for building foundational models are making the robotic skills needed for humanoids more commonplace. In parallel, we are also seeing advances in simulation that can train AI-based control and perception systems.

What will be the next major category of robots after manufacturing and warehousing?

Markets where companies are feeling the effects of labor shortages and demographic changes will continue to coincide with corresponding robotic opportunities. This spans robotics companies across a variety of industries, from agriculture to last-mile delivery to retail and more.

The main challenge in building various categories of autonomous robots is building the 3D virtual world needed to simulate and test the stack. Again, generative AI helps by allowing developers to build realistic simulation environments faster. Integrating AI into robotics will enable greater automation in environments that are less active and “robot friendly.”

How far have true general-purpose robots evolved?

We continue to see robots becoming more intelligent and able to perform multiple tasks in a given environment. We hope to continue to focus on mission-specific issues while making it more generalizable. True universal embodied autonomy is even further afield.

Will household robots (beyond vacuum cleaners) become commonplace within the next 10 years?

We plan to make useful personal assistants, lawn mowers, and robots available for joint use to assist the elderly.

The trade-offs that have hampered home robots so far are between how much someone will pay for their robot and whether the robot provides that value. Robot vacuums have long offered value for their price point, which is why they’re growing in popularity.

And as robots become smarter, having an intuitive user interface will be key to increasing adoption. Robots that can map their environment and receive voice commands will be easier for home consumers to use than robots that require programming.

The next category to take off will likely focus on things like automated outdoor lawn care. Other domestic robots, such as personal/healthcare assistants, also hold promise, but must address some of the indoor challenges encountered in dynamic, unstructured home environments.

What are some important robotics stories/trends that aren’t getting enough coverage?

The need for a platform approach. Many robotics startups are unable to scale because they develop robots suited to specific tasks or environments. For large-scale commercial realization, it is important to develop more versatile robots. That is, robots that can quickly add new skills or bring existing skills to new environments.

Robotics engineers need a platform with tools and libraries to train and test AI for robotics. The platform should provide simulation capabilities to train models, generate synthetic data, run the entire robotics software stack, and be able to run modern generative AI models directly on the robot.

Tomorrow’s successful startups and robotics companies will need to focus on developing new robotic skills and automation tasks, and take full advantage of available end-to-end development platforms.

Source: techcrunch.com