NVIDIA and LG Group Build an AI Factory to Advance Physical AI, Mobility and AI Infrastructure

2026-06-08 · Source: NVIDIA Blog · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Cloud Computing & IT Infrastructure · Depth: Intermediate, short

Summary

NVIDIA and LG Group are establishing an AI factory to accelerate LG's AI-driven businesses, encompassing robotics, autonomous driving, data center technologies, and GPU cloud services. This collaboration integrates NVIDIA's full-stack AI factory platform with LG's global leadership in consumer electronics, mobility, and smart spaces. The initiative focuses on advancing physical AI and robotics by using NVIDIA Isaac Sim, Isaac Lab, and Isaac GR00T for robot simulation and control, alongside a physical AI data factory leveraging NVIDIA Cosmos for synthetic data. Furthermore, the partnership extends to building NVIDIA DSX-aligned AI factory infrastructure, including cooling solutions and modular designs, and accelerating autonomous driving with NVIDIA DRIVE Hyperion and AGX platforms. Finally, they are advancing EXAONE, Korea's sovereign AI model, utilizing NVIDIA Blackwell GPUs, NeMo, Nemotron, and TensorRT-LLM for development and deployment across LG's enterprise.

Key takeaway

For AI Architects or Directors of AI/ML evaluating scalable infrastructure for physical AI or autonomous systems, this NVIDIA-LG partnership highlights a comprehensive full-stack approach. You should consider integrating simulation frameworks like NVIDIA Isaac and leveraging synthetic data generation to accelerate robotics development. Additionally, explore NVIDIA DSX-aligned modular, liquid-cooled data center designs to support high-performance AI factories and future GPU cloud services, ensuring your infrastructure can meet evolving demands.

Key insights

A full-stack AI factory integrates model development, data generation, simulation, and deployment for physical AI systems.

Principles

• Unified workflows streamline physical AI development.
• Synthetic data generation overcomes robotics training challenges.
• Modular, liquid-cooled infrastructure supports next-gen AI.

Method

The collaboration connects AI model development, physical AI data generation, robot simulation/training, edge deployment, and factory-scale digital twins into a unified workflow.

In practice

• Use Isaac Sim/Lab for robot simulation and validation.
• Explore Isaac GR00T for humanlike robot reasoning.
• Adopt NVIDIA DSX for scalable AI factory deployment.

Topics

• AI Factory
• Physical AI
• Robotics Simulation
• Autonomous Driving
• NVIDIA DSX
• Sovereign AI
• Synthetic Data Generation

Best for: AI Engineer, Machine Learning Engineer, NLP Engineer, AI Architect, Director of AI/ML, Robotics Engineer

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• Nvidia Makes Its Biggest Robotics Push Yet at CES 2026 — Nvidia is building a full-stack ecosystem to power general-purpose robotics, from chips to open foundation models and simulation.
• GTC 2026: Nvidia wants to swap robotics' data problem for a compute problem — Nvidia aims to dominate physical AI by converting data collection challenges into compute-intensive simulation problems.

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Editorial summary, takeaway, and curation by AIssential. Original article published by NVIDIA Blog.