On October 3, 2025, NVIDIA and Fujitsu officially announced the expansion of their strategic alliance, focusing on the development of full-stack AI infrastructure and the integration of AI agent technology. The initiative aims to build an “Agentic AI” system—featuring AI agents with continuous learning and evolution capabilities—and create customized Agent platforms for industries such as healthcare, manufacturing, and robotics, thereby advancing the implementation of enterprise-level AI applications. The two parties have clearly stated that the project will first be launched in Japan, with plans to complete infrastructure construction by 2030 and gradually expand globally, targeting a market scale of “trillions of yen.”
This collaboration is not a fresh start but an upgraded extension of their existing partnership, with the core lying in the deep integration of hardware and software. On the hardware front, Fujitsu will incorporate its Monaka CPU series, which will be seamlessly connected to NVIDIA GPUs via NVLink Fusion technology to form a customizable high-performance computing platform. This technology supports custom topological structures, boosting data transmission efficiency several times higher than traditional solutions. Meanwhile, the ARM architecture of the Monaka CPU optimizes energy efficiency, making it suitable for edge deployment scenarios, while NVIDIA GPUs handle high-intensity AI training tasks. Together, they meet computing needs across different scenarios. On the software and security front, the jointly developed Agent platform is built on NVIDIA’s NeMo framework and integrated with Fujitsu’s enterprise-grade security modules, enabling end-to-end encryption and multi-tenant isolation. This not only ensures enterprise data autonomy but also supports the continuous learning and optimization of AI agents. Fujitsu’s Kozuchi platform will also serve as an underlying support to develop secure agent AI capable of independently handling complex tasks—such as assisting in medical diagnosis and planning robot paths—breaking the limitations of traditional AI’s simple responsive interaction.
Technology Integration, Pilot Progress, and Ongoing Challenges
In terms of technology implementation and performance verification, the two parties have already conducted pilot tests in manufacturing scenarios. Results show that the integrated system can real-time optimize production lines through digital twin technology, reducing simulation cycles by 30%. Preliminary verification in some Japanese factories has also laid the foundation for technological maturity, with the first integrated system expected to be officially launched in 2026. Additionally, the collaboration extends to high-performance computing and quantum fields, with plans to launch a 10,000-qubit quantum computer and more energy-efficient AI chips by 2030, exploring hybrid computing models that combine quantum technology and AI to further break through the boundaries of existing computing capabilities.
However, challenges remain in project advancement: although NVLink technology offers a high bandwidth of 900GB/s, it is necessary to address issues related to precision thermal management and power distribution to avoid bottlenecks in data center-level deployment. At the same time, the stability of AI agents’ learning loops, data drift and hallucination problems, and the compatibility testing burden brought by NVLink Fusion customization all require continuous optimization. Fujitsu has planned to launch standardized modules in 2027 to simplify the system integration process—a key update highlighted in recent AI latest news.
Market Landscape and Future Expansion Plans
From the perspective of market background and competitive landscape, the global AI infrastructure market in 2025 is accelerating its transformation toward agent-based systems, with a surge in enterprise AI investment. Data shows that the penetration rate of agent AI in industrial applications is expected to rise from the current 15% to 35% by 2026, driven by factors such as labor shortages in manufacturing and personalized demand in healthcare. As a global manufacturing powerhouse, Japan already has a relevant market scale exceeding one trillion yen. Fujitsu’s local advantages in supercomputing and quantum research will help NVIDIA further penetrate the Asian supply chain. In terms of competition, NVIDIA has consolidated its leading position in the ecosystem through a multi-dimensional alliance strategy—previously reaching a 100 billion cooperation with OpenAI, investing 6.7 billion in a British startup, and injecting $5 billion into Intel.
Fujitsu, on the other hand, is leveraging this collaboration to transform from a traditional hardware manufacturer to a service-oriented model; in the 2025 fiscal year, its revenue reached 3.6 trillion yen, with AI business contributing over 20%. Unlike Microsoft and OpenAI’s collaboration, which focuses on cloud services, the alliance between NVIDIA and Fujitsu places greater emphasis on end-to-end optimization of the hardware-software stack, which is expected to reduce enterprise AI deployment costs by 15%.
Looking ahead, after completing the initial deployment in Japan by 2030, the two parties plan to expand the infrastructure to global data centers, covering manufacturing hubs in Southeast Asia and driving the robot automation rate to 50%. At the same time, in response to energy consumption pressures, the development of energy-efficient chips will be a core focus. The industry expects that such AI infrastructure alliances will stimulate investments exceeding one trillion US dollars, and the market scale of the quantum-AI integration field may reach $50 billion by 2030. However, it is necessary to remain vigilant about potential security vulnerabilities brought by the increased autonomy of AI agents, and enterprises need to strengthen auditing mechanisms simultaneously. Overall, the collaboration between NVIDIA and Fujitsu marks the evolution of AI infrastructure from mere computing power expansion to an “Agent + full-stack ecosystem” model. If breakthroughs can be achieved in areas such as hardware integration and platform customization, it is expected to become an important paradigm for enterprise-level AI deployment, providing a reference blueprint for the global transformation of infrastructure.
