What if a factory not only automated tasks, but also thought for itself to improve every minute of production? That is exactly the horizon Samsung and Nvidia have just outlined with their announced “AI Megafactory”, a joint project that will deploy more than 50,000 Nvidia GPUs to fundamentally transform how semiconductors are designed, tested and manufactured. Far from being a makeshift alliance, this move is the natural evolution of a historic collaboration: Samsung’s DRAM powered Nvidia’s first graphics cards, and now the two are teaming up to bring artificial intelligence to the heart of the fabrication process.
The idea is ambitious: that AI will span the entire value chain, from chip design and lithography to machine operation and quality control, with systems capable of analyzing, predicting and optimizing in real time. The result is not a traditional factory, but an infrastructure that learns, adjusts and accelerates each phase with data and advanced models.
AI Megafactory: chips designed with AI
Samsung plans to inject AI into every stage of its manufacturing flow, and early figures already show a considerable leap. Thanks to cuLitho and CUDA‑X from Nvidia applied to the optical proximity correction process, the company has achieved a 20‑fold improvement in computational lithography. Translated into real impact, that means detecting and correcting variations in circuit patterns much faster and with higher precision, drastically shortening development cycles and reducing the risk of defects.
In a world where every nanometer counts, accelerating the lithography phase is like moving from classic rendering to ray tracing: it changes the rules of the game because it allows you to see, with greater fidelity and in advance, how structures will behave in silicon. By combining specific compute libraries with GPUs designed for intensive workloads, Samsung improves both the quality and the pace of the pipeline, affecting time‑to‑market as well as the consistency of large‑scale production.
Moreover, this AI is not limited to theory; it also intervenes in equipment operation and quality control, where early detection and continuous optimization translate into fewer reworks and higher wafer yields. Ultimately, the megafactory does not aim merely to produce more, but to produce better, with data as fuel for every decision.
Digital twins and robots: the self‑improving factory
To fine‑tune that engine, Samsung is building digital twins of its facilities using Nvidia Omniverse. With these virtual replicas, engineers can simulate the entire production process, identify bottlenecks and test changes before applying them on the real plant floor. Iterating in a safe and faithful environment allows adjusting layouts, flows and process parameters without assuming stoppages or unnecessary risks, and accelerates the adoption of improvements that would otherwise take weeks to validate.
The physical layer also becomes smarter. With Nvidia’s Jetson Thor robotics platform, automation steps up and robots can collaborate with human operators with greater precision and autonomy. The aim is not to replace, but to coordinate: for machines to execute with surgical accuracy and for people to focus on supervision and decision‑making where experience adds the most value.
What is the scope of the megafactory? It doesn’t stop at chips: the plan includes producing next‑generation semiconductors, mobile devices and robotics, and extending this AI infrastructure to Samsung’s major global hubs, including its Taylor (Texas) complex. Additionally, the swarm of more than 50,000 GPUs will not only optimize manufacturing, but also power the company’s own AI models, which already run on more than 400 million devices. It’s like moving from a home Raspberry Pi cluster to a planet‑scale distributed brain, where every improvement learned in the cloud feeds back into the production line and the devices in our pockets.
HBM4 and the race for smarter factories
The alliance is mutual. Samsung makes the high‑bandwidth memory that fuels Nvidia’s chips, and now the two companies are co‑developing the fourth generation, HBM4, which aims to reach speeds of up to 11 gigabits per second, surpassing current industry standards. In a context where AI models are increasingly large and data‑hungry, that memory is the bridge that allows GPUs to keep up without hitting bottlenecks.
Market momentum is backing the push: Nvidia has revealed an order book of $500 billion for its Blackwell generation, and, in parallel, other South Korean giants like SK Group and Hyundai are preparing similar GPU clusters. The competition is no longer just about who makes the fastest chip, but about who orchestrates the smartest factory, with digital twins, collaborative robots and AI‑driven feedback loops that turn production into a self‑optimizing system.
For traditional manufacturers, the message is clear: those who do not adopt AI‑native production risk being left behind as manufacturing becomes software, data and simulation. Samsung and Nvidia are charting that transition with a megafactory that integrates massive compute, high‑performance memory and proprietary models working in tandem. If the past was measured in nodes and nanometers, the future will be measured in the intelligence of plants and the speed at which they learn.
