NVIDIA and SK Group Launch “AI Factory” to Accelerate Manufacturing Transformation

NVIDIA announced a major strategic partnership with SK Group to build an “AI factory” in South Korea, aimed at driving the next generation of manufacturing and digital transformation. The venture will deploy more than 50,000 NVIDIA GPUs in its first phase (expected to complete by late 2027) and will serve SK Group’s subsidiaries including SK hynix and SK Telecom as well as external customers via GPU-as-a-service.

Key elements of the collaboration include:

• SK hynix will use NVIDIA’s CUDA-X technologies and the PhysicsNeMo framework to accelerate chip design simulations and technology computer-aided design (TCAD).

• SK hynix will build fab digital twins using the NVIDIA Omniverse libraries and RTX PRO Servers, enabling real-time simulation, monitoring and optimization of manufacturing operations with the goal of working toward robotic, self-optimising fabs.

• SK Telecom will deploy an industrial cloud built on RTX PRO 6000 Blackwell GPUs in Asia to enable startups, enterprises and government agencies to accelerate digital twin and robotics innovation.

• The infrastructure will support more than 40,000 employees of SK hynix through AI-powered agents built with NVIDIA NIM microservices and NVIDIA AI Enterprise software.

According to Jensen Huang, NVIDIA’s founder and CEO: “In the era of AI, a new kind of manufacturing plant has emerged: the AI factory.”

And SK Group Chairman Chey Tae-Won adds: “With the NVIDIA AI factory as our foundation, SK Group will forge the infrastructure that powers the next generation of memory, robotics, digital twins and intelligent AI agents.”

What this means for the manufacturing industry

This announcement is more than just a large-scale infrastructure investment it marks a further step into what many call the “smart factory” or Industry 4.0/5.0 era. Here are several implications:

1. Manufacturing becomes more digitised and AI-driven
With fab digital twins and high-power GPU infrastructure, manufacturing facilities can move beyond traditional process automation into real-time simulation, monitoring and optimisation. Digital twins allow factories to model different “what-if” scenarios (layout changes, machine breakdowns, supply-chain disruptions) before they happen.

For manufacturers, this means shorter ramp-up times, fewer costly disruptions, and the ability to simulate more complex production flows. SK’s use of digital twins to accelerate chip production is a concrete example.

2. Enhanced operational efficiency and agility
AI agents and GPU-as-a-service models (as SK Group is deploying) mean that manufacturing operations can respond more quickly to changing conditions demand swings, supply-chain issues, quality defects. According to research, digital twins and AI in manufacturing help reduce downtime, optimise production scheduling and increase resilience.  For businesses in manufacturing, that means cost savings (less waste, fewer stoppages), faster time-to-market, higher yields and the ability to customise more effectively.

3. New service/asset models emerge
The fact that SK Group intends to make the infrastructure available via GPU-as-a-service to external organisations signals a shift: manufacturing operations, simulation capabilities and digital twin infrastructure become not just in-house assets but services. This means industrial companies may increasingly partner with AI/compute providers instead of building everything internally.

4. Workforce evolution and skills shift
As factories adopt more AI, robotics, digital twins and real-time analytics, the nature of work on the factory floor changes. Roles shift from manual tasks to overseeing, interpreting and acting on AI outputs; from equipment repair to data-driven decision-making. Manufacturers will need new skills digital literacy, AI reasoning, simulation modelling and will need to retrain or hire accordingly.

5. Competitive differentiation and ecosystem effects
Firms that adopt advanced AI-driven manufacturing early can gain a competitive edge faster innovation, lower costs, higher quality, better adaptability. In the case of SK Group and NVIDIA, the collaboration also bolsters Korea’s manufacturing ecosystem and could attract further investment and talent. For global manufacturing peers, this sets a benchmark: the cost of late adoption may increase.

Also Read: OpenAI Unveils “gpt-oss-safeguard”: Open-Weight Safety Reasoning Models

Implications for business operations & strategy

For businesses operating in the manufacturing sector whether you’re in semiconductors, automotive, electronics, heavy industry or consumer goods the ramifications of this kind of “AI Factory” model are significant. Here are strategic considerations:

• Invest in simulation and digital twin capabilities: As the SK/NVIDIA initiative shows, digital twin modelling isn’t just experimental; it’s becoming a foundational operational layer. Choosing platforms, designing for digital-native modelling, investing in sensors/IoT and data infrastructure will be key.

• Evaluate compute/AI infrastructure partnerships: Not all firms will build 50,000-GPU factories. Some may partner with providers, adopt “as-a-service” models or outsource simulation and optimisation workloads. The model that SK Group is building as a service hints at how third-party infrastructure might serve multiple manufacturers.

• Align workforce and organisational change: Technology adoption must go hand in hand with change management. The shift to AI-driven operations means new workflows, new roles, data governance, decision models and skills. Organisations that handle this well will benefit; others risk bottlenecks or resistance.

• Embed flexibility and resilience in manufacturing design: One of the big advantages of digital twin + AI is the ability to simulate disruptions, model alternative flows and respond adaptively a capability that is increasingly important in a world of volatile supply chains, labour shortages and demand shifts. Building factories that are “software-defined” or “agent-augmented” gives future-proofing.

• Re-think business models: The possibility of offering manufacturing simulation, optimisation or even production as a service (through shared GPU infrastructure or cloud-based digital twin platforms) opens new revenue streams or operational cost models. Additionally, the competitive bar is rising: firms lagging behind may face higher capital/operating costs or quality challenges.

Risks and cautions

While the SK/NVIDIA announcement is ambitious and forward-looking, businesses should temper expectations with realistic planning:

• Large-scale infrastructure takes time to materialise: The AI factory’s first phase is slated for late 2027. Operational benefits will not be immediate.

• Integration complexity: Digital twins, AI agents, robotics and cloud/edge compute layers are complex systems. Making them all work in a production-grade, reliable environment requires expertise, governance and change management which many manufacturing firms may struggle with.

• Skills and culture gap: The shift to AI-driven manufacturing is as much about people and process as it is about tech. Underestimating the human factor is a common pitfall.

• Capital and operational costs: AI/robotics/digital twin infrastructure demands investment not only in hardware but in sensors, connectivity, data management, software and expertise. Firms must build a clear business case.

• Security, data and compliance: With advanced digital twins and large compute farms, issues of cyber-security, data integrity and regulatory compliance become more important.

• Dependence on third-party providers: If a manufacturing firm outsources its AI compute or simulation workloads, it must manage service levels, IP protection and cost control.

Conclusion

The collaboration between NVIDIA and SK Group signifies a major milestone in the evolution of manufacturing an era where the “factory of the future” is not just automated, but fully modelled, simulated, optimised and managed by intelligent agents and digital twins. For the manufacturing industry, this means a direct step toward higher productivity, faster innovation and greater agility.

For businesses, the key message is clear: the manufacturing value chain is being redefined. Traditional factories are becoming software- and AI-driven platforms. As this shift accelerates, companies that anticipate, invest and adapt will position themselves ahead of the curve. In a B2B context, firms who supply manufacturing equipment, software, simulation tools or services should view this not only as a technology upgrade but as a transformation of business models opening opportunities for new offerings, partnerships and services.