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edge cases rarely encountered in everyday driving. To address these edge cases, we are leveraging the AI model‘ Heron,’ which has a deep understanding of Japanese culture and driving conditions, as well as the world model‘ Terra’, which can generate driving scenarios for edge cases that cannot be captured in the real world.

As Turing scales its AI workloads, how does the flexibility and scalability offered by NRT10’ s campus-style configuration support your evolving computational requirements?

A high-speed communication network between GPU nodes is essential for scaling up computing capacity. Because this network relies on cuttingedge specifications, wiring costs are very high, and any communication latency directly impacts computational efficiency. With a campus-style data centre, we can secure scalability while minimising wiring complexity and costs, which makes it especially appealing for us as we plan to expand.

What role do data centre partners like Digital Realty play in accelerating AI innovation, and how critical are these partnerships in the competitive race to achieve full driving automation?

The race to develop autonomous driving AI is accelerating. In circumstances where even a single-year delay can lead to a critical technological gap, a data centre that can consistently introduce and operate on the latest device platforms is considered an essential partner for maintaining our competitiveness. �

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