Fujitsu and Nvidia team up to build AI infrastructure for robotics by 2030

Fujitsu and Nvidia team up to build AI infrastructure for agents in robotics and beyond

Fujitsu has agreed with Nvidia to co-develop AI infrastructure that enables software agents across robotics and other sectors. The plan centers on combining Fujitsu's CPUs with Nvidia's GPUs to deliver large-scale training and real-time inference. The companies are targeting initial infrastructure build-out by 2030.

At a press event in Tokyo, Fujitsu's leadership emphasized that more compute will push AI forward, and that a full-stack approach can be adapted to healthcare, manufacturing, and customer service. Nvidia's leadership echoed the need to build the AI backbone in Japan and globally. Fujitsu also announced a partnership with Yaskawa Electric to create smart robots using Yaskawa's AI robotics technology.

Why this matters for Operations

• Capacity planning: Expect demand for GPU-accelerated workloads (training and edge inference). Start modeling power, cooling, and floor space for mixed CPU/GPU clusters.
• Hybrid compute strategy: On-prem plus cloud will be standard to manage cost, latency, and data sovereignty. Define what runs where by workload and risk profile.
• Data readiness: Agentic systems depend on clean, permissioned, event-rich data. Prioritize data pipelines, governance, and observability before scaling pilots.
• Vendor management: Integration across CPUs, GPUs, networking, and MLOps stacks will require clear SLAs, upgrade paths, and exit options to reduce lock-in.
• Risk and compliance: Map how autonomous actions are approved, logged, and audited. Build human-in-the-loop controls for safety-critical tasks.
• Supply chain realism: GPU lead times and costs will fluctuate. Secure allocations early and model cost scenarios for 12-36 months.

What to prepare in the next 12-24 months

• Run 2-3 high-impact pilots per site: one in robotics/automation (e.g., pick-and-place, visual inspection), one in customer operations (agent assist), and one in predictive maintenance.
• Stand up an AI operations playbook: incident response for models/agents, drift monitoring, rollback procedures, and model/version registries.
• Standardize the stack: containerized inference, GPU scheduling, and storage tiers for hot/cold data. Align on observability (metrics, traces, logs) for model and system health.
• Train frontline teams: upskill supervisors, technicians, and planners on AI-assisted workflows and exception handling.
• Budget by outcome: treat GPU/AI spend as a portfolio tied to cycle time, throughput, quality, and service levels-retire pilots that don't return value.

Where agents will likely show up first

• Manufacturing: vision-guided robotics, adaptive quality checks, line balancing, and autonomous material handling.
• Healthcare operations: scheduling, triage support, document processing, and prior-authorization workflows.
• Customer service: multimodal agent assist, self-serve issue resolution, and knowledge retrieval with approval gates.
• Field service: route planning, part forecasting, and procedure guidance with on-device inference.

For Operations leaders, the signal is clear: agent-capable infrastructure is moving from concept to build phase. Align facilities, data, and teams now so you can plug into GPU-backed stacks as they become available.

Learn more about the platforms involved:
Nvidia AI platform
Yaskawa Electric (robotics)

If you're planning workforce upskilling for AI-enabled operations, explore role-based options here:
Complete AI Training - Courses by Job