{"id":126687,"date":"2026-03-18T00:53:07","date_gmt":"2026-03-18T03:53:07","guid":{"rendered":"https:\/\/ces.einnews.com\/article\/900133887"},"modified":"2026-03-18T00:53:07","modified_gmt":"2026-03-18T03:53:07","slug":"nvidia-ceo-jensen-huang-reveals-chip-sales-in-china-are-about-to-restart","status":"publish","type":"post","link":"https:\/\/new7.shop\/zerocostfreehost\/index.php\/2026\/03\/18\/nvidia-ceo-jensen-huang-reveals-chip-sales-in-china-are-about-to-restart\/","title":{"rendered":"Nvidia CEO Jensen Huang reveals chip sales in China are about to restart"},"content":{"rendered":"
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After surprising the tech world on Monday with a prediction<\/a> that his company would see at least $1 trillion in chip orders through 2027, Nvidia Chief Executive Jensen Huang<\/a> spent today discussing the global dynamics and market forces that could make this a reality.<\/p>\n

Huang met with the media for nearly two hours today during the GTC gathering<\/a> in San Jose, answering a wide range of questions about Nvidia\u2019s announcements this week and the company\u2019s vision for AI. He took several questions about China, an area of particular interest given the potentially lucrative nature of the country\u2019s future business for the chip giant.<\/p>\n

Previous U.S. restrictions<\/a> on the sale of Nvidia\u2019s H200 processor in China have limited the firm\u2019s ability to sell to customers there. When asked about China in a similar session<\/a> with the media during CES in January, Huang said that he was waiting to see the purchase orders. That situation has apparently changed.<\/p>\n

\u201cWe have received purchase orders from many customers and we\u2019re in the process of restarting our manufacturing,\u201d Huang said on Tuesday. \u201cOur supply chain is getting fired up.\u201d<\/p>\n

Supply chain risk<\/h3>\n

While the restart of its business in China was good news for the company, there remain questions about Taiwan and the prospect<\/a> of China invading the territory and cutting off chip exports. Nvidia is expected to become the largest customer<\/a> for the Taiwan Semiconductor Manufacturing Co. later this year, and any disruption in the supply chain for its AI chips could have a significant impact on the company\u2019s growth.<\/p>\n

\u201cMy only hope is that we can all work together, stay in peace, look at the big picture, and stay calm,\u201d Huang said. \u201cI am 100% certain that the world will depend on Taiwan for a very long time.\u201d<\/p>\n

A disruption in its supply chain from Asia could affect another key part of Nvidia\u2019s future. This week\u2019s announcements<\/a> included the debut of the Groq 3 language processing unit or LPU, technology that will play a central role in the firm\u2019s AI inferencing strategy.<\/p>\n

Made by the foundry division<\/a> of Samsung Electronics in South Korea, Groq is an integral piece of Nvidia\u2019s focus on AI inferencing for multi-agent workloads. Pairing of the Groq 3 LPX with the new Vera Rubin NVL72 rack is designed to maximize efficiency across power, memory and compute. It could also maximize the revenue picture for Nvidia, according to Huang, who described how Groq\u2019s 25% role in its processing and storage solutions could add a similar percentage to its forecast for chip revenues.<\/p>\n

\u201cTheoretically, that $1 trillion could become $1.25 trillion,\u201d Huang told the media. \u201cThe entire storage industry is going to follow us.\u201d<\/p>\n

Growth expected for robotics<\/h3>\n

Nvidia\u2019s role as a central player in autonomous technology is also expected ultimately to contribute to its revenues. Nvidia\u2019s latest releases included blueprints<\/a> for AI training data generation to enable massive-scale processing for the AI models needed to drive the next generation of robots.<\/p>\n

Although Nvidia\u2019s automotive segment contributed only 1%<\/a> to the firm\u2019s total revenues in the past year, Huang expressed confidence that this picture will ultimately change.<\/p>\n

\u201cMost trillion-dollar businesses started at zero at one point,\u201d Huang said, bringing up the example of its CUDA computing platform and programming model. \u201cCUDA was zero percent of our business and 90% of our cost. It turned out that everything we did in the beginning cost us a lot of money and generated nothing.\u201d<\/p>\n

Nvidia\u2019s announcements<\/a> included an Open Physical AI Data Factory Blueprint to accelerate robotics, vision and autonomous vehicle deployment. The company\u2019s bet on physical AI is grounded in a belief that customers will ultimately need the full spectrum of Nvidia\u2019s compute architecture to achieve success.<\/p>\n

\u201cNvidia\u2019s autonomous vehicle business includes three computers,\u201d Huang noted. \u201cThe total business is much larger than people think. Customers are buying one of these computers or all three computers from us.\u201d<\/p>\n

Future tech and AI ethics<\/h3>\n

Missing from this week\u2019s GTC gathering has been much information about Feynman<\/a>, Nvidia\u2019s next-generation GPU microarchitecture that is planned for release in 2028. Although he briefly referenced Feynman during his keynote on Monday, Huang was asked to elaborate on the technology during his media briefing, and he had nothing further to add.<\/p>\n

Huang was more forthcoming in response to questions about the ethics surrounding AI. The issue has been in the news recently over controversy surrounding Anthropic PBC\u2019s refusal<\/a> to allow U.S. government use of its technology for mass surveillance or to build autonomous weapons. OpenAI Group PBC subsequently signed a $200 million deal with the Pentagon after Anthropic was removed from a contract.<\/p>\n

\u201cAI shouldn\u2019t break the law, AI should not promise functionality it does not have,\u201d Huang said. \u201cWe need AI to do a lot of great things for us. We need AI agentic systems to be inside of security. I need superfast AI agents to go protect me.\u201d<\/p>\n

As SiliconANGLE\u2019s analysts have noted<\/a>, the AI industry is rapidly shifting from training models to inference, the process of running them to generate results, and competitive advantage will be gained by those that can control and optimize the data path. Nvidia has made the case this week that it intends to define that path, which is keeping Huang, the man at the center of this key transformation, a very busy man indeed.<\/p>\n

\u201cMy experience with Nvidia today is it\u2019s making me busier today that I was six months ago,\u201d Huang said Huang, who waxed wistful about people a century ago having time drink lemonade on their porch. \u201cMy philosophy is, \u2018Don\u2019t get fired, don\u2019t get bored and don\u2019t die.\u2019 But each one of those three are very high-risk.\u201d<\/p>\n

Photo: Robert Hof\/SiliconANGLE<\/h5>\n
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