Developing and training deep neural network models fast is key to delivering highly accurate perception systems for autonomous vehicles. But this demands an infrastructure capable of training these networks on massive amounts of data, as well as ingesting, curating, and labeling hundreds of thousands of images at scale.


It takes a high-performance, energy-efficient AI computing infrastructure to create the future of autonomous vehicles. The key to success is optimizing the data load for training and operating these vehicles without compromising safety. The more information that cars can gather and process, the faster and better AI can learn and make decisions.

Scaling your data center with GPU-powered NVIDIA® DGX systems is the best way to build an AI infrastructure that can deliver safe autonomous vehicles to consumers. Experience unprecedented performance with the NVIDIA DGX H100 AI powerhouse that’s accelerated by the groundbreaking performance of the NVIDIA H100 Tensor Core GPU. And easily scale with a turnkey AI data center solution using NVIDIA DGX SuperPOD, which lets you test millions of permutations in environmental conditions or transients and increase model accuracy for higher levels of safety, without impacting time to production.

  • Experiment faster, train larger models, and gain insights starting on day one.
  • Improve AI innovation with an open, end-to-end platform that extends from the data center to the car.
  • Streamline and accelerate your workflow with today's most popular deep learning frameworks and AI tools—on-location from the NGC catalog.

Zenuity is using a scalable AI platform to accelerate development of smarter, safer autonomous vehicles. See how.

Training Deep Neural Networks

See how Tesla is using an in-house supercomputer powered by NVIDIA A100 GPUs to train deep neural networks for autopilot and self-driving capabilities.

Training Deep Neural Networks

Discover How NVIDIA DGX system can deliver faster, more cost-effective AV training.