The data center is becoming the new unit of compute, and we need to rethink data center design to support this shift in computing architecture.

The AI data center of the future differs significantly from traditional data centers, as it requires a more industrial approach to infrastructure, energy, and scale.

The scale of mega cluster buildouts for AI is unprecedented, with data center power consumption expected to rise significantly, driven by the demands of AI training and inference.

The nature of data center tasks is shifting from traditional database access to inference and training, which require thousands of GPUs to be connected in high-speed networks for efficiency.

The future of computing will involve asynchronous workloads where AI systems perform extensive research before returning results, necessitating robust infrastructure to handle increased loads.

Elon Musk's xAI is building a data center with 200,000 GPUs, which is currently the largest cluster, surpassing those of Meta and OpenAI, which have 128,000 and 100,000 GPUs respectively.

The future of computing will involve running incredibly large neural networks with high performance and low power consumption, requiring different solutions for training and inference workloads.

Elon Musk's approach to AI infrastructure is to have the biggest cluster fully connected in one building, while Google has to spread its resources across multiple sites, which affects efficiency.

We decided to simplify data centers by integrating all components into a single building block, making them more reliable and easier to manage, while also trying to replicate Apple's model by providing large building blocks for customers to build their own data centers, similar to how Apple built the iPhone with integrated hardware and software.