Accelerate Autonomous Vehicle AI Training and Development

NVIDIA DGX is a purpose-built AI supercomputer for training end-to-end AV foundation models at scale, including VLA reasoning models like NVIDIA Alpamayo.

Streamline end-to-end AV development with NVIDIA Physical AI Data Factory Blueprint for data curation (NVIDIA Cosmos™ Curator), augmentation (Cosmos Transfer, Cosmos Predict), and evaluation (Cosmos Evaluator). Also, accelerate training with NVIDIA CUDA-X™ AI and GPU-optimized kernels in NGC containers, and enhance inference with NVIDIA TensorRT™ and Triton™.

Accelerate scene reconstruction with NVIDIA Omniverse™ NuRec, amplify sensor variation with NVIDIA Cosmos Transfer, and run scalable closed-loop rollouts with NVIDIA AlpaSim. Together, they streamline end-to-end AV simulation workflows, from real-world data replay to controlled augmentation and policy-in-the-loop validation.

AVs generate terabytes of multimodal data from cameras, lidar, radar, and sensors. This data must be ingested, reconstructed, curated, and labeled at scale before it can be used to train AI models.

AV systems need to improve continuously, learning from new data, rare events, and edge cases to refine perception, prediction, and planning.

Optimize for high‑throughput replay of real‑world drives and scalable scene reconstruction, enabling efficient validation of changes and broad scenario coverage from fleet data.