South Korea Embraces the AI Revolution: NVIDIA’s CEO Jensen Huang Launches Groundbreaking Collaboration at APEC Summit

South Korea’s Sovereign AI Blueprint at APEC: NVIDIA-Powered Infrastructure, Ecosystem, and Policy Alignment

The Asia-Pacific Economic Cooperation gathering in Gyeongju set the stage for South Korea’s bold move into sovereign AI, anchored by a sweeping collaboration with NVIDIA. Against the city’s tapestry of Silla-era sites and gleaming tech campuses, policymakers and industry leaders converged on a single premise: national resilience in AI requires both compute capacity and a homegrown ecosystem of models, tools, and talent. The Ministry of Science and ICT (MSIT) outlined a multi-year program to deploy as many as 50,000 of the latest NVIDIA GPUs across Korea’s sovereign clouds—operated by NHN Cloud, Kakao Corp., and Naver Cloud—with an initial tranche of 13,000 Blackwell GPUs already earmarked. This program lives alongside private “AI factories” that expand the nation’s compute footprint to well over a quarter-million accelerators, positioning Korea to train agentic systems, operate physical AI in factories, and build sector-specific models that respect jurisdictional data rules.

Diplomacy amplified the announcement. With world leaders, including President Trump and President Xi, among the 21 economies represented, the APEC CEO Summit signaled that AI is now core to economic competitiveness. The Gyeongju agenda elevated “AI for economic development,” and the proposed sovereign infrastructure anchors a strategy that treats GPUs as national industrial assets rather than commodity hardware. The practical goal is to push beyond pilot projects and enable production-scale deployments of digital twins, robotics, and advanced language systems tuned to local data and culture.

Consider “Hanul Robotics,” a mid-sized Korean manufacturer preparing to deploy inspection robots across smart factories from Ulsan to Gwangju. With sovereign AI services running in-country and GPUs provisioned through Naver Cloud, Hanul can train perception models on proprietary industrial video, run simulation in Omniverse-style digital twins, and keep sensitive operational data inside national boundaries. That’s not merely a compliance checkbox—it’s a flywheel for faster iteration, durable IP, and resilient supply chains. It’s also a way to participate in global model innovation; initiatives like forward-looking innovations in foundation models are shaping training strategies, while teams weigh trade-offs outlined in limitations and strategies for scaling deployments responsibly.

Policy makers emphasized that compute alone won’t suffice. Korea’s plan includes workforce upskilling, model governance, and startup enablement via accelerators and venture partners. The strategic emphasis falls on “agentic and physical AI”—software that reasons and acts, and systems that perceive and manipulate the physical world. Why the focus? Because productivity gains compound when AI isn’t just summarizing documents but orchestrating logistics, optimizing throughput on production lines, and powering service robots in hospitals and retail.

Design principles guiding the sovereign AI rollout

• 🧠 Build a model-native ecosystem (tools, data pipelines, benchmarks) rather than only racks of GPUs.
• 🏛️ Keep cultural and domain data in-country to strengthen language, speech, and industry-specific models.
• 🔐 Ensure security and resilience through sovereign clouds, diverse vendors, and standardized APIs.
• ⚙️ Prioritize physical AI use cases—digital twins, robotics, and simulation—where Korea’s industry excels.
• 🚀 Accelerate time-to-production with shared toolchains (e.g., NeMo, Omniverse) and reference architectures.

For developers, practical workflows are evolving rapidly. Teams are experimenting with longer-term memory features in assistants, applying structured prompt systems like the prompt formula to improve reliability, and evaluating enterprise guardrails. Document-heavy organizations are adopting archiving practices such as accessing archived conversations to preserve knowledge while controlling exposure. The policy, infrastructure, and developer culture moving in lockstep is precisely what gives Korea’s sovereign approach its velocity. The early insight: scale matters, but disciplined engineering—and cultural fit—multiplies value.

NVIDIA-Powered AI Factories: Samsung, SK, Hyundai Motor, and Naver Orchestrate a New Industrial Core

South Korea’s private sector is transforming data centers into AI factories—software-defined production lines that generate models, simulations, and policies for robots and autonomous systems. Samsung, SK Group (including SK Telecom and memory powerhouse Hynix), Hyundai Motor Group, and Naver each outlined deployments of up to 50,000 NVIDIA GPUs per site, with Naver exceeding 60,000. These facilities rely on CUDA‑X, cuLitho for semiconductor workflows, Omniverse for digital twins, and emerging model families like Nemotron for synthetic data and reasoning. The output is not just trained weights; it’s a continuously improving “digital backbone” for fabs, assembly lines, and mobility services.

Samsung detailed a pipeline that blends computational lithography (cuLitho), physics-informed simulation, and Omniverse-based twins to shrink design cycles in advanced nodes. The company’s robotics teams are piloting Cosmos and Isaac GR00T to bootstrap dexterous manipulation at scale. SK Group outlined an AI cloud spanning up to 60,000 GPUs—some powered by RTX PRO 6000 Blackwell Server Edition—to serve Korean manufacturers and startups with sovereign, low-latency compute. Hyundai Motor is building a 50,000-GPU AI factory to train and validate models for autonomous driving, safety-critical perception, and factory orchestration using NVIDIA DRIVE Thor, NeMo, and Omniverse. LG Electronics features as a systems integrator across consumer devices and edge robotics, enabling downstream adoption in homes, hospitals, and logistics depots.

These factories shorten the path from simulation to reality. For instance, a digital twin of a Busan shipyard can test path-planning policies for yard vehicles overnight, then deploy those policies in the morning. As open-world synthetic environments mature, they support goal-directed agents that generalize beyond curated training sets. Meanwhile, engineering teams are reporting order-of-magnitude gains with AI physics acceleration, hinting at broader shifts in CAE and EDA. A rising cohort of robotics developers in Korea is also leaning into open-source frameworks for robotics to reduce integration friction and speed up field trials.

AI factory capabilities that unlock competitive advantage

• 🏗️ Digital twins for fabs, shipyards, and auto plants to de-risk process changes and capital expenditures.
• 🤖 Robot policy training with Isaac GR00T and Cosmos, bootstrapping skills from synthetic demonstrations.
• 🛰️ Autonomous mobility stacks integrated with DRIVE Thor for safer, software-defined vehicles.
• 🧩 Model distillation to compress large models into efficient edge deployments without losing capability.
• 📈 Closed-loop optimization where production data refines simulators and models, creating a compounding flywheel.

The industrial narrative dovetails with broader platform advances like long-context models that digest months of telemetry, and comparative strategy work such as OpenAI vs. Anthropic analyses that inform capability roadmaps. The near-term insight is pragmatic: Korea’s AI factories are designed to ship production systems, not tech demos—an ethos that compresses innovation cycles across hardware, software, and operations.

Korean Language Models, Healthcare AI, and Quantum Acceleration: Research Pillars for Sovereign Capability

While compute footprints capture headlines, Korea’s research stack is evolving just as quickly. MSIT launched a Sovereign AI Foundation Models program with LG AI Research, Naver Cloud, NC AI (aligned with NCSoft), SK Telecom, Upstage, and NVIDIA. The effort leverages NeMo and open Nemotron datasets to train Korean language models with robust reasoning and speech capabilities, tuned to local idioms and industry lexicons. Healthcare is a marquee vertical: LG’s EXAONE Path, built with the MONAI framework, supports cancer diagnosis workflows by combining imaging priors with clinical text modeling. Early hospital pilots underscore how multimodality transforms triage and treatment planning.

At the same time, the Korea Institute of Science and Technology Information (KISTI) is partnering with NVIDIA to establish a Center of Excellence for quantum computing and scientific AI. With the sixth-generation HANGANG supercomputer and the CUDA‑Q platform, KISTI will explore hybrid quantum-classical algorithms, physics-informed AI, and scientific foundation models using PhysicsNeMo. The goal is to route domain knowledge—materials science, fluid dynamics, biophysics—into simulators and generative models that produce experimentally testable hypotheses. As “mini-lab” automation advances, national labs and startups are tracking progress like miniature lab research that blends robotics and AI in compact experimental rigs.

Practical developer patterns are crystallizing as well. Teams building long-lived assistants are iterating on memory enhancements, journaling decisions and evaluations over time. Institutions with heavy knowledge operations are standardizing knowledge retention via archived assistant conversations, ensuring auditability and continuity. From a compute standpoint, KISTI’s work hints at a future where quantum resources accelerate subroutines inside classical training loops—especially for combinatorial optimization and molecular graph problems—without disrupting mainstream GPU-based workflows.

Research priorities shaping model quality and safety

• 🗣️ Native Korean language models that capture speech patterns, honorifics, and dialectal nuance.
• 🧬 Healthcare multimodality combining imaging, labs, and text for earlier and more precise diagnoses.
• 🧪 Physics-informed AI to boost sample efficiency and ensure results comply with known constraints.
• 🪄 Quantum-classical hybrids targeting niche accelerations in simulation and optimization.
• 🛡️ Governance and evaluation frameworks to monitor bias, robustness, and safety at deployment time.

As model builders assess toolchains and vendors, many are reviewing capability retrospectives and product matrices such as the 2025 assistant review and the evolving landscape of leading labs. The research insight is clear: Korea’s institutional strength—spanning semiconductors, automotive, healthcare, and basic science—makes it an ideal testbed for models that must interface with the physical world.

AI-RAN to 6G: Samsung, SK Telecom, KT Corporation, and LGU+ Reimagine Networks for Physical AI

To unlock mobile-scale AI, Korea’s carriers and research institutes are evolving radio access networks into AI‑RAN—intelligent, energy-aware, GPU-accelerated base stations. In collaboration with Samsung, SK Telecom, ETRI, KT Corporation, LGU+, and Yonsei University, NVIDIA is enabling compute offload from devices to cell sites, reducing energy draw and extending battery life. This architecture is a prerequisite for physical AI at the edge: drones, delivery robots, and AR assistants that demand real-time perception and planning without carrying datacenter-class chips onboard.

Why does this matter beyond telecom? Because network-aware inference pipelines let cities orchestrate fleets of robots, vehicles, and sensors with tighter latency guarantees. A hospital in Daegu can dispatch an indoor delivery bot that streams video to a neighborhood edge node for segmentation while receiving refined navigation policies back over a 6G link. For startups building embodied agents, AI‑RAN means faster iteration loops—policies run on the network, metrics stream to a sovereign cloud, and model updates propagate rapidly without cumbersome device recalls.

Interoperability is equally important. Carriers are testing RIC (RAN Intelligent Controller) apps that schedule GPU workloads, balance energy budgets, and prioritize mission-critical QoS for public safety or industrial automation. The horizon includes fused localization from radio and vision, collaborative SLAM across edge nodes, and federated robot learning. Reference systems borrow from broader innovation threads like emerging robot frameworks and productivity practices outlined in enterprise productivity playbooks, which guide teams on orchestrating multi-agent workflows.

AI‑RAN building blocks and expected gains

• 📶 Base-station GPUs to run perception and language tasks near users and robots.
• 🔋 Energy savings through network offload, prolonging device battery life and enabling smaller form factors.
• ⏱️ Lower latency by keeping inference within metro networks, essential for safety-critical autonomy.
• 🧭 RIC apps to manage AI workloads and QoS policies dynamically.
• 🔗 Federated learning to improve models without moving sensitive data out of local jurisdictions.

For developers exploring network-native AI apps, content creation and real-time streaming use cases are also surfacing—from low-latency avatars to on-the-fly video editing supported by tools like the latest AI video generators. Application teams are pairing these with plugin ecosystems to orchestrate multi-service workflows safely. The operational insight: networks that “think” are the substrate for embodied AI at scale.

Culture, Gaming, and Consumer AI: GeForce’s 25-Year Legacy Meets Korea’s Next-Gen Creators

Korea’s AI story is cultural as much as it is industrial. In Seoul, NVIDIA celebrated 25 years of GeForce with a fan festival showcasing RTX innovations and hands-on demos. NCSoft offered early gameplay for AION 2 and CINDER CITY, both using DLSS 4 with Multi‑Frame Generation, while KRAFTON unveiled PUBG Ally, an AI co-playable character built on NVIDIA ACE. The festival blended tradition and modernity, featuring a special StarCraft matchup between legends Hong Jin-ho (YellOw) and Lee Yoon-yeol (NaDa), high-energy performances by K‑TIGERS, and a set by global K‑POP act LE SSERAFIM. The message was unmistakable: Korea’s consumer base is ready for AI-native experiences that are expressive, social, and persistent.

The creator economy is undergoing parallel shifts. Streamers and indie studios are exploring generative video workflows accelerated by RTX and cloud GPUs, using tools similar to the top video generators to storyboard virtual scenes and compress post-production time. Productivity-minded creators tune their pipelines with guidance from AI productivity strategies, while studios standardize on prompt templates akin to the prompt formula to reduce variance in content tone and style. Reviews such as the 2025 assistant review help teams choose capable copilots for scripting and localization, and critical perspectives like unfiltered chatbot analyses guide safety practices for live communities.

Consumer discourse is complex and vibrant. On the one hand, there’s excitement around novel companion apps and immersive chat experiences—trends that include controversial categories cataloged in consumer companions. On the other, there’s a push for durable guardrails, with communities debating disclosure, moderation, and creator attribution. Korean platforms are experimenting with watermarking and provenance, and with RTX tools embedded in game engines, mod communities can remix worlds faster than ever. Meanwhile, crossovers with enterprise continue: avatar tech piloted for streaming later surfaces in customer support avatars that run on AI‑RAN edge nodes, and cinematic tools inform marketing pipelines for automakers and electronics brands.

Consumer vectors accelerating Korea’s AI adoption

• 🎮 Gaming-first innovation where DLSS, ACE, and RTX tech trickle into mainstream creative tools.
• 🎥 Creator economy workflows that cut costs and time to publish with generative video and audio.
• 🗣️ Real-time avatars and agents for streaming and customer engagement, powered by edge inference.
• 🧩 Plugin-based ecosystems combining specialized models into cohesive workflows.
• 🧭 Responsible use discussions spanning watermarking, moderation, and attribution.

For cultural analysts, the pattern is familiar: Korea’s early adoption of PC‑bang culture and esports primed consumers for AI-native experiences. Today, tools spanning plugin ecosystems, model innovation roadmaps, and comparative lab studies like OpenAI vs. Anthropic provide a compass for creators and studios planning their next move. The cultural insight: as fans embrace AI-enhanced worlds, Korea’s consumer feedback loop becomes a strategic asset for the nation’s sovereign AI ambitions.

Startups, Skills, and Capital: Building Korea’s AI Middle Class of Builders

A nation-scale AI strategy succeeds when it produces a deep bench of founders, engineers, and product leaders. To that end, NVIDIA is expanding the NVIDIA Inception program in Korea, creating a startup alliance that pairs compute access with venture support from IMM Investment, Korea Investment Partners, and SBVA. Startups can tap sovereign cloud credits through partners like SK Telecom, receive mentorship on model evaluation and deployment, and get hands-on training via the Deep Learning Institute. A dedicated Center of Excellence—powered by RTX PRO 6000 Blackwell GPUs—will help founders prototype physical AI applications, from last‑meter logistics bots to AR-enabled technicians.

Why is the middle layer of the ecosystem so pivotal? Because big corporations and national labs excel at foundational infrastructure, while startups translate those capabilities into specialized, high-velocity products. Korea’s startup scene is already building model-intensive SaaS for shipbuilding, smart factories, and hospitality. Product teams draw from playbooks such as productivity strategies to scale operations, and evaluate workflow resilience with resources like the limitations and mitigation strategies that help secure production rollouts. Founders also study long-context modeling to build tools that recall months of procedures and inventory states.

On the application frontier, embodied AI startups are tapping simulation advances and robotics stacks, cross-pollinating with global work highlighted in synthetic worlds research. Developer communities debate governance and transparency, often referencing capability reviews and safety critiques to shape roadmaps. In parallel, public-sector agencies sponsor hackathons centered on public services—transit optimization in Busan, marine safety in Jeju—grounding AI enthusiasm in measurable outcomes.

Startup and talent levers that accelerate deployment

• 🧑‍💻 Compute credits via sovereign clouds to lower the barrier to model training and evaluation.
• 📚 Upskilling through the Deep Learning Institute, focused on LLMOps, evaluation, and simulation.
• 🤝 VC partnerships that combine capital with go-to-market and policy mentorship.
• 🧪 Reference architectures for robotics and digital twins to avoid reinventing the wheel.
• 🧭 Governance toolkits for bias, safety, and monitoring to move from pilots to production.

Founders are also watching the assistant ecosystem mature—features like memory and organization-friendly capabilities such as conversation archiving have direct implications for customer success tools and internal copilot design. The entrepreneurial insight: Korea’s AI middle class will thrive by translating sovereign infrastructure into focused, measurable wins in vertical software, robotics, and network-native services.

What does ‘sovereign AI’ mean in the Korean context?

It refers to nationally governed AI stacks—compute, data pipelines, models, and deployment platforms—operated under Korean jurisdiction. The approach blends NVIDIA-powered GPU infrastructure with local clouds (NHN Cloud, Kakao, Naver) and governance to protect data, accelerate industry-specific models, and ensure resilience.

How many GPUs are being deployed and where will they be used first?

The public sovereign cloud program targets up to 50,000 GPUs, starting with an initial 13,000 NVIDIA Blackwell units. Private AI factories by Samsung, SK Group/Hynix, Hyundai Motor, and Naver add tens of thousands more. Early use cases include digital twins, robotics, semiconductor workflows, autonomous driving, and large Korean language models.

Which telecom partners are enabling AI-RAN and why is it important?

Samsung, SK Telecom, KT Corporation, LGU+, ETRI, and Yonsei University are collaborating to build AI-RAN and 6G paths. Offloading AI to base stations reduces device energy, lowers latency, and unlocks reliable, mission-critical AI for robots, vehicles, and real-time consumer experiences.

How are startups and SMEs included in the plan?

Through NVIDIA Inception and a new startup alliance, founders receive compute access, mentorship, and training. A Center of Excellence equipped with RTX PRO 6000 Blackwell GPUs supports rapid prototyping for physical AI, and carriers like SK Telecom provide sovereign cloud access.

What is the role of research institutions like KISTI and LG AI Research?

They anchor foundational progress. KISTI advances hybrid quantum-classical methods with HANGANG, CUDA-Q, and PhysicsNeMo, while LG AI Research develops domain models such as EXAONE Path for healthcare using MONAI. These efforts ensure that sovereign AI is scientifically grounded and clinically relevant.

Rachel Tanaka

Rachel has spent the last decade analyzing LLMs and generative AI. She writes with surgical precision and a deep technical foundation, yet never loses sight of the bigger picture: how AI is reshaping human creativity, business, and ethics.