KubeCon + CloudNativeCon Japan 2025

As organizations scale their model training workloads in cloud-native environments, they face significant data processing and storage challenges: managing massive training datasets across distributed storage systems while ensuring optimal I/O performance. While Kubernetes excels at compute orchestration, the increasing distribution of data across multiple storage backends creates bottlenecks that impact training performance and infrastructure costs.

This presentation introduces a Kubernetes-native distributed caching system that utilizes NVMe storage to overcome data locality challenges. Haoyuan Li will also share real-world, large-scale production use cases to show how this architecture lowers data infrastructure costs, increases GPU utilization, and enables workload portability to navigate GPU scarcity challenges.