As autonomous driving shifts from modular architectures to End-to-End (E2E) frameworks—exemplified by NVIDIA’s Alpamayo-R1 the demand for scalable, high-performance data infrastructure has reached a critical tipping point. These VLA-based models require seamless access to massive, distributed datasets that mirror the complexity of scientific computing. This session explores how IBM Storage Scale provides the foundation for these modern AI workloads. We will dive into how parallel file systems and advanced data orchestration unify geographically dispersed data, optimize resource utilization, and accelerate the transition from raw sensor input to actionable driving policies.