Case Study: Continental Automotive achieves 70% faster AI training and 14x more deep learning experiments with IBM Storage Scale

Developing autonomous driving solutions with faster, more flexible data storage and simplified management for AI

Continental Automotive needed a faster, more flexible way to manage petabytes of data for AI-driven autonomous driving development. Its challenge was speeding up training and validation for advanced driver assistance systems while handling distributed data, high-performance compute, and strict safety requirements. Continental worked with IBM, using IBM Storage Scale and NVIDIA DGX systems.

IBM implemented a high-performance, scalable storage and AI infrastructure with IBM Storage Scale System, NVMe-based storage, and NVIDIA DGX clusters, integrated with Continental’s Kubernetes environment. The result was a 70% improvement in AI training time, 14 times more deep learning experiments per month, and training cycles reduced from weeks to days, helping Continental accelerate safer autonomous driving innovation.

Continental Automotive

Robert Thiel

Head of Artificial Intelligence