Wayve to Train AI Driver Models on CoreWeave Cloud with Blackwell GPUs

Unite.AI
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Wayve has partnered with CoreWeave to train its AI Driver foundation models on CoreWeave Cloud using NVIDIA Blackwell GPUs, leveraging extensive real-world driving data from over 70 countries. This collaboration aims to accelerate the development and commercial deployment of autonomous driving systems through large-scale training, simulation, and evaluation workflows.

What happened

Wayve has entered a multi-year partnership with CoreWeave to train its AI Driver foundation models on CoreWeave Cloud using NVIDIA Blackwell GPUs. The agreement, announced on November 9, 2026, will use real-world driving data from over 70 countries to support training, simulation, and evaluation of autonomous driving systems. Wayve will utilize CoreWeave's cloud infrastructure to run large-scale experiments and model validation before deploying updates to its AI Driver. The collaboration is intended to speed up the development and commercial rollout of Wayve's autonomous vehicle technology.

Key details

  • Wayve's AI Driver models will be trained using NVIDIA Blackwell GPUs on CoreWeave Cloud.
  • The training will use driving data collected from more than 70 countries.
  • CoreWeave's infrastructure supports petabyte-scale storage and high-speed networking for sensor and simulation data.
  • Simulation and evaluation workflows are used to validate model safety before real-world deployment.
  • The agreement is described as multi-year and began with an announcement on November 9, 2026.

Why it matters

Companies developing autonomous vehicles, such as Wayve, gain access to more powerful cloud computing resources for training and validating their models. This enables faster iteration cycles, allowing new software versions to be tested and validated more quickly and safely before being deployed in real vehicles. The partnership also supports the operational requirements for handling large volumes of sensor data and maintaining traceability, which are critical for safety and regulatory compliance in autonomous driving systems.


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