How to Choose an AI Data Center

Choosing a data center for AI workloads is fundamentally different from selecting one for traditional IT. GPU-intensive training and inference demand exceptional power density, specialized cooling, high-bandwidth networking, and infrastructure that most conventional facilities simply cannot provide. This guide walks you through every critical factor to evaluate.

Step 1: Define Your AI Workload Requirements

Before evaluating facilities, clearly define your AI infrastructure requirements:

• Training vs inference: Training requires sustained high-power compute with GPU-to-GPU interconnects. Inference is typically lower power but demands low latency and high availability.
• GPU count and type: How many GPUs do you need now and in 12-24 months? What generation — H100, H200, B100?
• Power per rack: A single 8-GPU server draws 5-10 kW. A full DGX cluster rack draws 40-100+ kW. Know your density requirements.
• Network requirements: Multi-node training needs InfiniBand or RoCE fabric. Inference may only need standard Ethernet.
• Data residency: Regulatory requirements may restrict your geographic options.
• Timeline: How quickly do you need to deploy? Some facilities can provision in weeks; new builds take 12-18 months.

Step 2: Evaluate Power Infrastructure

Power is the most critical factor for AI data centers. Evaluate these elements:

Power Capacity

Ensure the facility can deliver the power density you need — not just today, but for planned expansion. Ask about available capacity in MW, per-rack power limits, and whether they can support 40+ kW racks without modifications.

Power Redundancy

AI training runs can take days or weeks. A power interruption doesn't just cause downtime — it can waste days of compute. Look for Tier III or Tier IV facilities with 2N power redundancy, on-site generators with 48+ hours of fuel, and automatic transfer switches with sub-second failover.

Power Pricing

Power costs represent 40-60% of total GPU colocation costs. Understand whether power is billed per kWh (metered), per kW committed (fixed), or a hybrid model. Markets like Texas and Phoenix offer significantly cheaper power than coastal markets.

Step 3: Assess Cooling Capabilities

Standard air cooling maxes out at approximately 20-25 kW per rack — far below what modern GPU servers demand. Your facility must support advanced cooling technologies:

• Liquid cooling (direct-to-chip): Essential for racks above 40 kW. Removes heat directly from GPU dies using coolant loops.
• Rear-door heat exchangers: Good for moderate densities (20-40 kW). Adds liquid cooling to the back of standard racks.
• Immersion cooling: Submerges entire servers in dielectric fluid. Best for maximum density and energy efficiency.

Ask whether the facility has liquid cooling infrastructure already deployed or requires a buildout. Pre-existing liquid cooling infrastructure saves 3-6 months of deployment time.

Step 4: Verify Network Infrastructure

Internal Fabric

Multi-node AI training requires high-bandwidth, low-latency interconnects between GPUs. Look for facilities that support InfiniBand (400 Gbps or higher) or RoCE (RDMA over Converged Ethernet). Ask about available fabric options and whether the facility has experience supporting GPU clusters.

External Connectivity

Evaluate carrier diversity (minimum 3+ carriers), available cloud on-ramps (direct connections to AWS, Azure, GCP), dark fiber paths to other facilities, and latency to your users or data sources.

Step 5: Evaluate the Provider's AI Experience

Not all colocation providers understand AI workloads. Ask potential providers:

• How many GPU deployments do they currently support?
• What is their largest GPU cluster?
• Do they have staff experienced with NVIDIA DGX, HGX, or similar platforms?
• Can they provide references from AI/ML customers?
• Do they offer managed GPU services or just space and power?

Step 6: Consider Location and Market

Your choice of market affects costs, connectivity, and compliance. Top markets for AI workloads include:

• Northern Virginia — maximum connectivity, premium pricing
• Texas (DFW/Austin) — affordable power, growing ecosystem
• Phoenix, Arizona — low power costs, solar availability
• Chicago — central location, strong financial sector presence

Step 7: Review Contracts and SLAs

AI colocation contracts have unique considerations:

• Term length: Longer terms (2-3 years) yield 15-25% savings but reduce flexibility
• Power SLA: Ensure 99.999% uptime guarantee with meaningful financial penalties
• Cooling SLA: Temperature and humidity guarantees specific to high-density deployments
• Scaling provisions: Right of first refusal on adjacent capacity for expansion
• Exit terms: Understand early termination fees and equipment removal timelines

Step 8: Plan for Growth

AI infrastructure needs grow rapidly. Your chosen facility should be able to scale with you:

• Available expansion capacity on the same campus
• Ability to increase per-rack power density over time
• Network infrastructure that scales without forklift upgrades
• Provider roadmap for next-generation cooling and power

Selection Checklist

Use this quick checklist when evaluating facilities:

• ✅ Supports required power density (40+ kW/rack)
• ✅ Liquid cooling available or planned
• ✅ 3+ network carriers with cloud on-ramps
• ✅ Tier III+ with 2N power redundancy
• ✅ Proven experience with GPU deployments
• ✅ Expansion capacity available
• ✅ Competitive pricing for your market
• ✅ SLAs that cover high-density scenarios