AI data centers have some unique needs compared to traditional ones, leading to specific property requests. Here’s a breakdown of the key properties:

For AI training:

  • High computational power: This involves access to powerful GPUs, CPUs, and specialized hardware like TPUs for massive parallel processing. Locations with access to high-capacity power grids are crucial.
  • Dense server racks: Training often involves thousands of servers packed close together for efficient data exchange. This necessitates spacious facilities with high cooling capacity to handle the generated heat.
  • Plentiful land and scalability: Training facilities might be located outside urban areas due to lower costs and land availability. Room for expansion is important to accommodate future needs.
  • Reliable high-speed network: Transferring massive datasets for training requires robust fiber-optic connections and proximity to internet exchange points.

For AI inference:

  • Low latency: Applications like real-time speech recognition or autonomous vehicles demand minimal processing delay. Edge data centers closer to users are preferred for these scenarios.
  • High bandwidth: Streaming data and delivering results require significant network capacity. Reliable and redundant connections are essential.
  • Efficient cooling: Densely packed servers for inference also generate substantial heat, requiring advanced cooling systems for optimal performance.
  • Secure environment: Protecting sensitive AI models and data is paramount. Robust physical and cybersecurity measures are necessary.


  • Renewable energy sources: Data centers are increasingly prioritizing sustainable solutions to reduce their carbon footprint. Access to renewable energy sources like solar or wind power is becoming a key factor.
  • Cost-effectiveness: Finding balance between essential features and operational costs is crucial. Locations with lower land and energy costs are attractive.

Remember, these are general requests, and specific needs might vary depending on the type of AI application and the organization’s priorities.