NVIDIA and Foxconn describe an “AI factory” as computing infrastructure built to turn data into AI models and tokens. Their collaboration combines that infrastructure with industrial uses such as manufacturing, robotics, electric vehicles, and generative AI. Announcements since 2023 have outlined projects in Taiwan, including a planned Blackwell computing center in Kaohsiung and a cloud AI factory with 10,000 Blackwell GPUs. Those are announced plans and specifications—not, by themselves, proof of completed or operational systems.
What NVIDIA and Foxconn mean by an AI factory
In their October 17, 2023 announcement, NVIDIA defined an AI factory as “an NVIDIA GPU computing infrastructure specially built for processing, refining and transforming vast amounts of data into valuable AI models and tokens.” That is the companies’ framing, rather than a neutral industry standard. The idea is to treat computing capacity as infrastructure for producing and running AI, much as a traditional factory uses equipment and processes to make physical goods.
The announcement named NVIDIA’s accelerated-computing platform, including GH200 Grace Hopper and NVIDIA AI Enterprise. NVIDIA said the infrastructure could support model training and inference, factory workflows, and simulations in a virtual environment before physical deployment. Those were intended capabilities in the announcement, not measured results from Foxconn deployments. NVIDIA’s October 2023 announcement also described applications spanning manufacturing and inspection, AI-powered electric vehicles, robotics, and language-based generative AI services.
What the partnership covers
The collaboration is broader than a single data center. It links computing infrastructure to industrial and public-facing applications, with different NVIDIA platforms named for different uses.
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- Manufacturing: Digitizing production and inspection workflows, with robotics and simulation intended to help plan or support factory operations.
- Electric vehicles: Foxconn’s Smart EV plans referenced NVIDIA DRIVE Hyperion 9 and DRIVE Thor.
- Robotics: The Smart Manufacturing plans referenced NVIDIA Isaac.
- Smart cities: The companies named NVIDIA Metropolis for this area.
- Generative AI: The infrastructure was described as supporting language-based AI services through training and inference.
These are platform and use-case plans as described in a corporate release; the announcement does not establish that every application has been deployed or quantify its results. NVIDIA founder and CEO Jensen Huang said, “A new type of manufacturing has emerged — the production of intelligence. And the data centers that produce it are AI factories,” while Foxconn chairman and CEO Young Liu said, “Most importantly, NVIDIA and Foxconn are building these factories together. We will be helping the whole industry move much faster into the new AI era,” according to that release.
How the announced projects developed
| Date | Announcement | What it establishes |
|---|---|---|
| October 17, 2023 | NVIDIA and Foxconn announced their collaboration. | Company-defined AI-factory concept, intended computing capabilities, and industrial application areas. NVIDIA announcement |
| June 4, 2024 | Foxconn said it planned an advanced computing center in Kaohsiung, Taiwan, with NVIDIA Blackwell at its core. | A stated plan; the announcement does not establish that the center was completed or operational. Foxconn announcement |
| November 19, 2024 | Foxconn described digital-twin work for manufacturing and supply-chain processes. | Foxconn said it was using NVIDIA Omniverse, Isaac, Modulus, and OpenUSD in the context of its Mexico factory. No quantified productivity gains were stated. Foxconn announcement |
| May 18, 2025 | NVIDIA described a Taiwan AI-factory supercomputer project involving Foxconn and Taiwan’s government. | NVIDIA said Foxconn subsidiary Big Innovation Company would provide compute as an NVIDIA Cloud Partner, with 10,000 Blackwell GPUs. The announcement does not establish current operating status. NVIDIA announcement |
What the Taiwan cloud AI factory is supposed to provide
NVIDIA’s May 2025 announcement described Blackwell Ultra systems, including GB300 NVL72, NVIDIA Quantum InfiniBand, and Spectrum-X Ethernet. Foxconn subsidiary Big Innovation Company was named as the infrastructure provider and NVIDIA Cloud Partner. TSMC was named as a user of the cloud infrastructure for research and development.
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The 10,000-GPU figure is a specification announced by NVIDIA for the planned system, not an independently verified count of installed or operating GPUs. The announcement also does not, on its own, establish project completion, present availability, benchmark performance, cost savings, or economic returns.
What makes an AI factory different from a conventional data center?
The announcements support a distinction in purpose, not a complete technical comparison. NVIDIA presents an AI factory as GPU computing organized around processing data into models and tokens and then supporting AI workloads. The Foxconn projects add industrial uses such as digital twins, robotics, and production workflows, as well as a cloud-delivery route. The cited company announcements do not provide a neutral, like-for-like comparison with a conventional data center’s hardware, operating costs, energy use, or performance.
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For a business assessing an AI-factory approach, useful questions are what work it needs to run, how closely compute must connect to physical operations, and how it wants to obtain capacity:
- Workload: Is the main need model training, inference, or both?
- Industrial task: Is compute for simulation and digital-twin planning, or for supporting an active production workflow?
- Delivery: Does the organization need owned or on-premises infrastructure, or cloud access such as the route announced for Taiwan?
The announcements do not provide a controlled vendor comparison, price comparison, or like-for-like benchmark, so they cannot establish which approach is faster or cheaper.
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How to judge claims about results and economics
NVIDIA’s current AI-factories page discusses tokens per second, tokens per watt, cost per token, utilization, and uptime as ways to characterize AI-factory economics. These are NVIDIA’s suggested metrics, not reported results for the Foxconn projects. They are most useful when a project publishes actual measurements, explains how they were collected, and connects them to the workload being served.
The announcements cited here do not establish independently benchmarked performance, achieved output, energy efficiency, realized cost savings, or productivity outcomes for the named projects. Foxconn’s digital-twin announcement identifies tools and intended manufacturing and supply-chain uses, but does not quantify gains. Treat announced system specifications and intended use cases as project descriptions—not evidence of a financial return.
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