NVIDIA Expands U.S. AI Chip and Supercomputer Manufacturing with Blackwell Rollout

NVIDIA has launched a major U.S. manufacturing expansion for its next-gen AI infrastructure. Blackwell chips will now be produced at TSMC’s Arizona facilities, with AI supercomputers assembled in Texas by Foxconn and Wistron. Backed by partners like Amkor and SPIL, NVIDIA is localizing its AI supply chain from silicon to system integration—laying the foundation for “AI factories” powered by robotics, Omniverse digital twins, and real-time automation. By 2029, NVIDIA aims to manufacture up to $500B in AI infrastructure domestically.
NVIDIA Expands U.S. AI Chip and Supercomputer Manufacturing with Blackwell Rollout
Image Credit: Nvidia

NVIDIA Builds Domestic AI Infrastructure with TSMC, Foxconn, and Wistron

NVIDIA has officially announced a major expansion of its AI infrastructure footprint—this time on U.S. soil. For the first time in the company’s history, NVIDIA will manufacture its AI supercomputers and next-generation semiconductors entirely within the United States.


In collaboration with manufacturing giants TSMC, Foxconn, and Wistron, NVIDIA is establishing over one million square feet of dedicated production capacity in Arizona and Texas. This move supports not just chip manufacturing but the entire lifecycle of AI supercomputer development—from silicon fabrication and testing to packaging and system integration.

The initiative signals a fundamental shift in the AI supply chain and reflects growing pressure for technological sovereignty, supply chain resilience, and the onshoring of strategic infrastructure.

NVIDIA Blackwell AI Chips Begin Production in Arizona with Full Supercomputer Builds in Texas

NVIDIA’s new Blackwell chipsets—tailored for AI model training and inference—have officially entered production at TSMC’s advanced node facilities in Phoenix, Arizona. These chips are at the heart of NVIDIA’s next-generation computing systems, designed to handle the computational demands of modern large language models (LLMs) and Generative AI.

Down the supply chain, two major supercomputer manufacturing sites are being launched: one in Houston, operated by Foxconn, and another in Dallas, operated by Wistron. These factories will assemble, test, and integrate the full AI computing platforms powered by the Blackwell architecture.

Mass production is expected to scale significantly over the next 12–15 months, with NVIDIA signaling that these plants will play a pivotal role in meeting global demand for AI processing power.

Building a Domestic AI Supply Chain—From Silicon to System Integration

NVIDIA is addressing more than just chip production. The entire value chain—from chip packaging to end-to-end testing—is being localized. The company is partnering with Amkor and SPIL in Arizona for backend manufacturing processes, which are typically outsourced to Asia. These partnerships support the packaging of advanced chipsets and ensure seamless integration into full-stack AI supercomputers.

By 2029, NVIDIA aims to manufacture up to $500 billion worth of AI infrastructure in the U.S., a bold strategy that emphasizes economic impact alongside technical advancement. It also showcases a commitment to national priorities such as supply chain independence, high-tech job creation, and domestic innovation.

NVIDIA’s AI Factories Signal a Shift in Global Tech Infrastructure

NVIDIA describes these new manufacturing sites as “AI factories”—data center-grade facilities built solely for AI workloads. Unlike traditional compute environments, these factories are optimized for real-time data processing, model training, inference, and advanced analytics.

Tens of such gigawatt-scale AI factories are expected to be built in the coming years to support use cases across sectors like healthcare, financial services, automotive, and telecom.

These facilities will be vital for delivering high-throughput AI capabilities to power applications like digital twins, autonomous systems, virtual assistants, and generative AI tools.

NVIDIA Uses Omniverse and Robotics to Power Smart AI Factories

To streamline operations, NVIDIA plans to use its own technology stack to design and run these factories. Using the NVIDIA Omniverse, the company will build high-fidelity digital twins of its production facilities to simulate workflows, test equipment placement, and optimize throughput before physical deployment.

Additionally, NVIDIA Isaac GR00T, the company’s robotics platform, will automate large portions of the manufacturing process. These smart robots will handle component assembly, automated inspection, and logistics, reducing error margins and increasing productivity across sites.

This integration of AI, robotics, and automation signals a new standard in factory operations, merging digital infrastructure with physical manufacturing in real time.

U.S. AI Manufacturing Expansion Fuels Jobs and Global Tech Leadership

NVIDIA’s U.S.-based production is expected to generate hundreds of thousands of jobs, from factory technicians to software engineers. It also strengthens the U.S. position in the global race to dominate AI, semiconductors, and advanced computing.

According to Jensen Huang, Founder and CEO of NVIDIA, “The engines of the world’s AI infrastructure are being built in the United States for the first time. Adding American manufacturing helps us better meet the incredible and growing demand for AI chips and supercomputers, strengthens our supply chain, and boosts our resiliency.”

A Strategic Move That Sets the Tone for the AI-First Economy

NVIDIA’s announcement isn’t just about moving manufacturing closer to home—it’s a signal to the broader tech ecosystem. As AI becomes foundational to everything from drug discovery and cybersecurity to smart cities and self-driving vehicles, companies will need more localized, secure, and scalable AI infrastructure.

By integrating semiconductor manufacturing with edge computing, digital twins, and AI software frameworks under one national footprint, NVIDIA is building a comprehensive blueprint for the AI-powered future.


Recent Content

Verizon Business and Nokia will deploy six private 5G networks across Thames Freeport’s major logistics sites, including the Port of Tilbury, London Gateway, and Ford Dagenham to create a high-performance digital infrastructure supporting real-time logistics, AI automation, and edge computing. With plans to generate 5,000 skilled jobs and power sustainable trade, this initiative positions Thames Freeport as a next-gen smart trade corridor.
Hrvatski Telekom’s NextGen 5G Airports project will deploy Private 5G Networks at Zagreb, Zadar, and Pula Airports to boost safety, efficiency, and airport automation. By combining 5G Standalone, Edge Computing, AI, and IoT, the initiative enables drones, smart cameras, and AI tablets to digitize inspections, secure perimeters, and streamline operations, redefining aviation connectivity in Croatia.
SK Group and AWS are partnering to build South Korea’s largest AI data center in Ulsan with a $5.13 billion investment. The facility will launch with 60,000 GPUs and 103 MW capacity, expanding to 1 GW, creating up to 78,000 jobs. This milestone boosts South Korea’s AI leadership, data sovereignty, and positions Ulsan as a major AI hub in Asia.
This article critiques the common practice of exhaustive data cleaning before implementing AI, labeling it a consultant-driven “scam.” Data cleaning is a never-ending and expensive process, delaying AI implementation while competitors move forward. Instead, I champion a “clean as you go” approach, emphasizing starting with a specific AI use case and cleaning data only as needed. Smart companies prioritize iterative improvement by using AI to fill in data gaps and building safeguards around imperfect data, ultimately achieving faster results. The core message is it’s more important to prioritize action over perfection, enabling quicker AI adoption and thereby competitive advantage.
Edge AI is reshaping broadband customer experience by powering smart routers, proactive troubleshooting, conversational AI, and personalized Wi-Fi management. Learn how leading ISPs like Comcast and Charter use edge computing to boost reliability, security, and customer satisfaction.
The pressure to adopt artificial intelligence is intense, yet many enterprises are rushing into deployment without adequate safeguards. This article explores the significant risks of unchecked AI deployment, highlighting examples like the UK Post Office Horizon scandal, Air Canada’s chatbot debacle, and Zillow’s real estate failure to demonstrate the potential for financial, reputational, and societal damage. It examines the pitfalls of bias in training data, the problem of “hallucinations” in generative AI, and the economic and societal costs of AI failures. Emphasizing the importance of human oversight, data quality, explainability, ethical guidelines, and robust security, the article urges organizations to proactively navigate the challenges of AI adoption. It advises against delaying implementation, as competitors are already integrating AI, and advocates for a cautious, informed approach to mitigate risks and maximize the potential for success in the AI era.
Whitepaper
Explore the Private Network Edition of 5G Magazine, your guide to the latest in private 5G/LTE and CBRS networks. This edition spotlights 11 award categories including private 5G/LTE leader, neutral host leader, and rising startups. It features insights from industry leaders like Jason Wallin of John Deere and an analysis...
Whitepaper
Discover the potential of mobile networks in modern warfare through our extensive whitepaper. Dive into its strategic significance, understand its security risks, and gain insights on optimizing mobile networks in critical situations. An essential guide for defense planners and cybersecurity enthusiasts....

It seems we can't find what you're looking for.

Download Magazine

With Subscription

Subscribe To Our Newsletter

Scroll to Top