Chimaek diplomacy in Seoul: NVIDIA and South Korea team up on 250,000 GPUs to ease the AI crunch

South Korea moves to scale AI with NVIDIA: what government teams should do next

NVIDIA CEO Jensen Huang is in Seoul for the first time in 15 years. Over chicken and fries with Samsung's Jay Y. Lee and Hyundai's Euisun Chung, the message was clear: build real AI capacity at home.

Symbolism aside, the Ministry of Science and ICT (MSIT) announced a continuous cooperation track with NVIDIA. The plan centers on securing infrastructure, boosting Physical AI, supporting startups, and deepening work with domestic companies and research institutes.

The agreement at a glance

• Compute at scale: More than 250,000 of NVIDIA's latest GPUs to be procured across public and private sectors, addressing the local shortage.
• Priority domains: Autonomous driving, Physical AI, and manufacturing AI with domestic partners.
• Startup support: Expanding NVIDIA-backed programs and local access to compute under review.
• Policy context: Builds on the government's earlier plan to secure 200,000 GPUs by 2030, signaling acceleration and broader scope.

Why this matters for the public sector

• Reduced bottlenecks: Agencies, universities, and labs get the compute they've been queuing for.
• Sovereign capability: More domestic training and inference option reduces reliance on overseas capacity.
• Standards and reuse: Shared infrastructure lowers cost per project and speeds up delivery.
• Industry spillover: Co-development with Samsung, Hyundai, and others can feed public services and safety-critical systems.

90-day action plan for ministries and agencies

• Map demand: Submit model types, dataset sizes, and expected GPU-hours by project and quarter. Prioritize public safety, health, and critical infrastructure.
• Prepare facilities: Validate energy capacity, cooling, and floor space. Pre-approve sites for regional clusters and edge locations.
• Network design: Specify high-throughput fabrics and low-latency links between clusters and major data holders. Plan for secure interconnects to universities and hospitals.
• Procurement route: Set framework agreements for GPU systems, networking, storage, and support. Include local integration and maintenance requirements.
• Shared clusters: Stand up national AI clusters with fair-share scheduling, quotas, and researcher onboarding. Offer managed environments (Kubernetes/Slurm, container registry, model catalog).
• Data and security: Enforce governance for sensitive data, audit trails, and incident response. Use privacy-preserving training where needed.
• People: Upskill engineers and program managers on model ops, cluster operations, and evaluation. Pair domain experts with ML leads for delivery.

Collaboration tracks to stand up now

• Autonomous systems: With mobility and transport agencies, run joint pilots with Hyundai and research labs for perception, mapping, and simulation. Focus on safety validation and data-sharing agreements.
• Physical AI: Work with robotics and public safety teams on inspection, logistics, and disaster response. Target measurable field trials rather than lab demos.
• Manufacturing AI: Coordinate with industry and SMEs on defect detection, scheduling, and predictive maintenance. Standardize data formats to speed adoption across suppliers.

Startup access and fairness

• Compute access: Create a national queue for startups with time-bound GPU allocations tied to milestones.
• Co-location: Offer slots in government or university clusters with clear SLAs and cost recovery.
• Fast-track programs: Link public-sector challenge briefs to startup pilots with staged funding and evaluation criteria.

Risks to manage

• Over-concentration: Avoid single-site or single-vendor lock-in. Keep portability through open tooling and standard interfaces.
• Cost creep: Track total cost of ownership (hardware, energy, cooling, staffing, software). Use utilization targets and auto-scaling.
• Data exposure: Segment networks, use confidential computing where available, and enforce strict access control.
• Sustainability: Plan for heat reuse and renewable energy contracts. Publish efficiency metrics.

What to measure quarterly

• Deployment: GPUs installed, active nodes, and uptime.
• Utilization: GPU-hours used vs. available, queue times, and preemption events.
• Delivery: Models trained, evaluations passed, and services launched in agencies.
• Impact: Cost per inference/training run, project cycle-time, and incident rates.
• Sustainability: Energy use, cooling efficiency, and emissions per compute unit.

Context and resources

For technical alignment and platform guidance, see NVIDIA's data center overview here. For policy updates and coordination, monitor MSIT announcements here.

If you're coordinating staff enablement across agencies, you can review role-based AI training options here.

Bottom line

The compute gap has been the blocker. With a path to 250,000+ GPUs and structured cooperation, the next constraint is execution. Get the sites ready, standardize the stack, and move priority workloads into production with clear metrics and guardrails.