High-speed networking technology that enables thousands of AI accelerators to work together by moving data with extremely low latency and high bandwidth.

A measure of AI efficiency that shows how much AI output can be generated for the energy consumed. Improving tokens per watt helps organizations scale AI more sustainably.

Training is how AI models learn from massive datasets. Inferencing is when a trained model applies what it has learned to generate responses, predictions, or insights in real time.

The network fabric connects servers, storage, and AI systems, enabling the fast movement of data needed for large-scale AI workloads.

Graphics Processing Units (GPUs) are specialized processors designed to handle the complex calculations required for AI training and inferencing. This datacenter runs a single, massive cluster of interconnected GPUs from leading partners.

Direct liquid cooling removes heat more efficiently than traditional air cooling, helping support the intensive performance demands of next-generation AI systems.

Heat exchangers transfer heat away from AI equipment and are a key component of modern liquid cooling systems. They can also help existing datacenters support higher-density AI workloads.