AI Infrastructure Requirements: What You Need

Building or deploying AI infrastructure requires careful planning across multiple dimensions — from GPU selection and networking to power delivery and cooling. Whether you're training large language models, running computer vision inference, or fine-tuning foundation models, this guide covers every infrastructure component you need to consider.

GPU Compute Requirements

Choosing the Right GPU

Your GPU selection drives every other infrastructure decision. The current landscape in 2026:

• NVIDIA H100 (80GB HBM3): The workhorse for AI training. 3,958 TFLOPS FP8 performance. Still the most widely available high-end GPU in colocation facilities.
• NVIDIA H200 (141GB HBM3e): 1.7x more memory bandwidth than H100. Ideal for large model inference and memory-constrained training workloads.
• NVIDIA B100/B200: Blackwell architecture with 2-2.5x the training performance of H100. Limited availability in colo.
• NVIDIA A100 (40/80GB): Previous generation but still capable for inference and smaller training runs at 30-50% lower cost.
• AMD MI300X (192GB HBM3): Competitive alternative with massive memory capacity. Growing ecosystem support.

Sizing Your GPU Deployment

The number of GPUs you need depends on your workload:

• Fine-tuning (7-13B parameters): 1-8 GPUs, weeks to complete
• Training (13-70B parameters): 32-256 GPUs, weeks to months
• Training (70B+ parameters): 256-4,096+ GPUs, months of continuous operation
• Inference (serving): 1-8 GPUs per model instance, scaled horizontally

Networking Requirements

GPU-to-GPU Interconnect

For multi-node training, the interconnect between GPUs is often the bottleneck. Requirements by workload:

• Single-node (1-8 GPUs): NVLink handles intra-node communication at 900 GB/s. No special network needed.
• Multi-node training: InfiniBand NDR (400 Gbps per port) is the gold standard. NVIDIA Quantum-2 switches provide non-blocking fabric for up to 32,000 GPUs.
• Alternative: RoCE v2 (RDMA over Converged Ethernet) at 400 GbE can work for smaller clusters but has higher latency than InfiniBand for large-scale training.

Storage Network

AI training requires fast access to large datasets. Plan for:

• Parallel file systems: GPFS, Lustre, or WekaFS delivering 100+ GB/s aggregate throughput
• NVMe storage: Local NVMe SSDs for checkpoint storage and data staging
• Object storage connectivity: High-bandwidth links to S3-compatible storage for datasets

External Connectivity

Your data center needs robust external connectivity for data ingestion, model deployment, and management:

• Multiple 100 GbE+ uplinks to diverse carriers
• Direct cloud on-ramps for hybrid deployments
• Low-latency paths to users (for inference serving)

Power Requirements

AI infrastructure is extremely power-hungry. Plan for these power draws:

• Single GPU server (8x H100): 6-10 kW
• DGX H100 system: 10.2 kW
• Full GPU rack (4x DGX): 40+ kW
• 32-node cluster: ~330 kW (including networking and storage)
• 256-node cluster: ~2.6 MW
• 1,024-node cluster: ~10+ MW

Beyond raw power capacity, ensure your facility offers:

• Redundancy: 2N power feeds for training workloads that can't tolerate interruption
• Quality: Clean power with proper conditioning to protect sensitive GPU hardware
• Scalability: Room to grow power allocation as you expand

Power costs vary dramatically by market. Compare GPU colocation pricing across markets to find the best value. Lower-cost markets like Texas can save 30%+ on power costs versus premium markets.

Cooling Requirements

At 40+ kW per rack, standard air cooling cannot keep GPU hardware within operating temperatures. Your cooling requirements include:

• Direct-to-chip liquid cooling: Required for most modern GPU clusters at scale
• Coolant distribution units (CDUs): To manage liquid cooling loops
• Heat rejection capacity: Cooling towers or dry coolers sized for your total heat load
• Redundancy: N+1 cooling redundancy to prevent thermal shutdowns during maintenance

Read our detailed data center cooling types guide for a full comparison of cooling technologies.

Storage Requirements

Training Data Storage

• Capacity: 10 TB to 10+ PB depending on dataset size
• Throughput: 10-100+ GB/s aggregate read performance to keep GPUs fed
• Latency: Sub-millisecond for random access patterns

Checkpoint Storage

• Capacity: 5-50 TB per training run (model weights + optimizer state)
• Write speed: Fast enough to checkpoint without significantly interrupting training
• Durability: Critical — losing checkpoints can waste weeks of training compute

Physical Space Requirements

GPU infrastructure requires more physical planning than standard IT:

• Floor loading: GPU racks can weigh 2,000-3,000 lbs, exceeding some raised floor capacities
• Rack spacing: Liquid cooling infrastructure may require wider aisles
• Cable management: InfiniBand and power cabling for high-density racks is substantial
• Staging area: Space for hardware assembly, burn-in testing, and spare parts

Data Center Selection Criteria

When choosing a data center for AI workloads, prioritize facilities that meet these minimum requirements:

• ✅ 40+ kW per rack power delivery
• ✅ Liquid cooling infrastructure (deployed or available)
• ✅ Tier III+ reliability with 2N power
• ✅ Support for InfiniBand or high-speed Ethernet fabric
• ✅ Multiple network carriers and cloud on-ramps
• ✅ Experienced staff with GPU deployment knowledge
• ✅ Expansion capacity for future growth

Browse our AI-ready data center directory to find facilities that meet these requirements, or explore by market: Northern Virginia, Texas, Phoenix, or Chicago.