GPU Colocation Pricing Guide 2026

GPU colocation pricing is one of the most opaque areas in the data center industry. Unlike traditional colocation where you can easily compare per-kW or per-rack costs, GPU hosting involves power density premiums, liquid cooling surcharges, and GPU-specific infrastructure fees. This guide breaks down every cost component so you can budget accurately for your AI infrastructure.

How GPU Colocation Pricing Works

GPU colocation pricing differs fundamentally from standard server colocation. Traditional colo charges $100-300 per kW/month for 5-15 kW racks. GPU colocation, on the other hand, typically involves racks drawing 30-100+ kW, requiring specialized cooling and power delivery that commands premium pricing.

Most providers structure GPU colocation pricing using one or more of these models:

• Per-kW pricing: The most common model, typically $150-400/kW/month for GPU-ready power at high densities
• Per-rack pricing: Full cabinet pricing ranging from $3,000-$15,000/month depending on power density and cooling type
• Per-GPU pricing: Some providers offer managed GPU colocation at $2-8 per GPU-hour equivalent
• Custom pricing: Large deployments (1+ MW) typically negotiate custom rates 20-40% below list prices

Cost Breakdown by Component

1. Power Costs (40-60% of Total)

Power is the single largest cost in GPU colocation. A single NVIDIA DGX H100 system draws approximately 10.2 kW, and a full rack of 4x DGX systems can draw over 40 kW. At scale, power costs can reach:

• Low-cost markets (Texas, Utah): $0.06-0.08/kWh → ~$1,800-2,400/rack/month for a 40kW rack
• Mid-range markets (Chicago, Phoenix): $0.08-0.12/kWh → ~$2,400-3,600/rack/month
• Premium markets (Northern Virginia, Silicon Valley): $0.10-0.16/kWh → ~$3,000-4,800/rack/month

2. Cooling Surcharges (10-20%)

High-density GPU racks require specialized cooling solutions. Expect these premiums above standard air-cooled pricing:

• Rear-door heat exchangers: +$500-1,500/rack/month
• Direct-to-chip liquid cooling: +$1,000-3,000/rack/month
• Immersion cooling: +$1,500-4,000/rack/month (but significantly lower PUE)

3. Space and Cross-Connects (10-15%)

Physical space costs include the cabinet itself, raised floor or slab space, and network cross-connects. GPU deployments typically require fewer cabinets per compute unit but need more space between racks for airflow or liquid cooling infrastructure.

• Cabinet rental: $500-2,000/month depending on size and market
• Cross-connects: $200-500/month per connection
• Cloud on-ramps: $500-1,000/month for direct connections to AWS, Azure, GCP

4. Network and Bandwidth (5-15%)

AI workloads, particularly training, generate massive amounts of data traffic between nodes. Bandwidth pricing varies significantly by provider:

• Committed bandwidth: $1-5/Mbps/month for dedicated Internet
• Burstable bandwidth: $0.50-3/Mbps/month with 95th percentile billing
• InfiniBand fabric: Varies by provider, often included in managed GPU offerings

5. Remote Hands and Management (5-10%)

• Basic remote hands: Often included (1-2 hours/month), then $75-150/hour
• Managed services: OS monitoring, hardware replacement: +$500-2,000/rack/month
• Full GPU management: Including driver updates, health monitoring: +$1,000-5,000/rack/month

GPU Colocation Pricing by GPU Type

NVIDIA H100 Colocation

The NVIDIA H100 remains the most in-demand GPU for AI training. Colocation costs for H100 systems typically range from:

• Single H100 server (8-GPU): $5,000-10,000/month all-in
• DGX H100 system: $8,000-15,000/month including power and cooling
• Full H100 cluster (32+ GPUs): Custom pricing, typically $3,000-6,000/GPU/month

NVIDIA H200 Colocation

The newer H200 with 141GB HBM3e memory commands a 20-40% premium over H100 pricing due to limited supply and higher power requirements.

NVIDIA A100 Colocation

A100 systems offer a more affordable entry point for AI workloads, with colocation costs typically 30-50% lower than equivalent H100 deployments. They remain excellent for inference workloads and smaller training runs.

Colocation vs Cloud: Cost Comparison

For a detailed analysis, see our guide on colocation vs cloud for AI workloads. The short version: colocation typically becomes more cost-effective at scale, particularly for sustained workloads running 60%+ utilization.

• Cloud H100 pricing: $2-4/GPU/hour on major cloud providers
• Colocation H100 equivalent: $1.50-3.50/GPU/hour at typical utilization rates
• Break-even point: Most organizations find colocation cheaper beyond 6-12 months of sustained use

How to Reduce GPU Colocation Costs

• Negotiate long-term contracts: 2-3 year commitments can save 15-25% over month-to-month pricing
• Choose cost-effective markets: Texas and Phoenix offer significantly lower power costs
• Optimize power utilization: Right-size your power allocation to avoid paying for unused capacity
• Bundle services: Combining colo with network and managed services often yields better per-unit pricing
• Consider liquid cooling: Higher upfront cost but 20-40% lower PUE can offset power expenses

Getting Accurate Quotes

GPU colocation pricing varies significantly by provider, market, and deployment size. The best approach is to get quotes from multiple providers in your target market. Use our directory to find GPU-ready facilities, then request custom quotes. For a broader look at all colocation costs, see our complete colocation pricing guide for 2026.