Uploaded July 2026 | Updated September 2026, 2 weeks ago
Don't miss out! Join us at our next KubeCon + CloudNativeCon events in Shanghai, China (8-9 September, 2026) and Salt Lake City, United States (Nov 9–12, 2026). Connect with our current graduated, incubating, and sandbox projects as the community gathers to further the education and advancement of cloud native computing. Learn more at kubecon.io
The Next Evolution of Kubernetes: GPU-Centric Infrastructure for AI Workloads - Takao Indoh, Fujitsu Limited
With the emergence of generative AI and Agentic AI, the role of Kubernetes is shifting from CPU-based cloud infrastructure to GPU-centric infrastructure.
Faced with constraints such as explosively growing computational demand, GPU shortages, and soaring electricity costs, Kubernetes must optimize the use of available resources to sustainably scale AI workloads. In the case of Agentic AI in particular, a single request generates numerous downstream tasks, resulting in unpredictable computational demands, making it increasingly difficult to address these challenges with traditional cloud design philosophies alone.
In this presentation, we will introduce Fujitsu's perspective on the new challenges facing cloud-native infrastructure in the AI era, along with our solution based on Composable Disaggregated Infrastructure.
Don't miss out! Join us at our next KubeCon + CloudNativeCon events in Shanghai, China (8-9 September, 2026) and Salt Lake City, United States (Nov 9–12, 2026). Connect with our current graduated, incubating, and sandbox projects as the community gathers to further the education and advancement of cloud native computing. Learn more at kubecon.io
The Next Evolution of Kubernetes: GPU-Centric Infrastructure for AI Workloads - Takao Indoh, Fujitsu Limited
With the emergence of generative AI and Agentic AI, the role of Kubernetes is shifting from CPU-based cloud infrastructure to GPU-centric infrastructure.
Faced with constraints such as explosively growing computational demand, GPU shortages, and soaring electricity costs, Kubernetes must optimize the use of available resources to sustainably scale AI workloads. In the case of Agentic AI in particular, a single request generates numerous downstream tasks, resulting in unpredictable computational demands, making it increasingly difficult to address these challenges with traditional cloud design philosophies alone.
In this presentation, we will introduce Fujitsu's perspective on the new challenges facing cloud-native infrastructure in the AI era, along with our solution based on Composable Disaggregated Infrastructure.
