⚡ AI Infrastructure Guide

Your Path to AI Compute

Data centers are the backbone of AI. Whether you want to rent cloud time, build dedicated hardware, or pool resources with a community co-op — we'll help you find the right path.

☁️

Cloud AI

Rent GPU compute on demand from cloud providers. Low upfront cost, instant access, scales with your needs.

Beginner-friendly
🔧

Build / Co-locate

Own your hardware. Co-locate in a data center facility, or build out your own on-prem infrastructure for maximum control.

High control
🤝

Community Pool

Pool funds with friends, researchers, or your community to co-own AI compute. Shared cost, shared access, shared governance.

Most affordable
$0.60
GPU-hour (cloud spot)
10kW
Avg rack power density
Tier IV
99.995% uptime standard
PUE 1.2
Best-in-class efficiency

Data Center Advisor

Learn, plan, and find your path to AI compute infrastructure.

View architecture Health API

🏢 What Is a Data Center?

A data center is a physical facility that houses computer systems, servers, networking equipment, and storage. They provide the raw power, cooling, and connectivity that AI and cloud computing depend on.

  • Thousands to hundreds of thousands of servers
  • Redundant power (generators + UPS)
  • Precision cooling systems
  • High-bandwidth fiber connectivity
  • Physical security and access controls

🧠 AI-Specific Requirements

AI workloads — especially training large models — place extreme demands on infrastructure that general-purpose data centers weren't designed for.

  • GPU density: H100 racks need 30–80kW each
  • Memory bandwidth: NVLink / InfiniBand interconnects
  • Storage: High IOPS NVMe for datasets
  • Cooling: Liquid cooling often required
  • Network: 400GbE+ fabric between nodes

📊 Data Center Tiers

Tier Uptime SLA Redundancy Annual Downtime Best For
Tier I99.671%None28.8 hrDev / test
Tier II99.741%Partial22 hrSmall business
Tier III99.982%N+11.6 hrProduction workloads
Tier IV99.995%2N26 minMission-critical AI

⚖️ Comparing Your Options

Factor ☁️ Cloud 🔧 Build / Colo 🤝 Community
Upfront costLow ($0)Very high ($50k–$500k+)Medium (shared)
Monthly costHigh at scaleLow per-unitLowest per person
Setup timeMinutesMonths–yearsWeeks–months
Hardware controlNoneFullShared
ScalabilityInstantSlow (hardware)Group decision
Privacy / dataProvider TOSCompleteGroup policy
Technical skill neededLowVery highMedium

⚡ Power & Cooling Basics

Power Usage Effectiveness (PUE) measures efficiency. Best-in-class hyperscalers achieve PUE ≈ 1.1. Typical facilities run 1.4–1.6. Every watt of compute needs ~0.4–0.6W extra for cooling.

  • Air cooling: works below ~10kW/rack
  • Rear-door heat exchangers: 10–30kW/rack
  • Liquid cooling (direct): 30–100kW/rack
  • Immersion cooling: densest, most expensive

🌐 Network Connectivity

Connectivity is as critical as compute. For distributed AI training, latency between GPUs dominates performance.

  • NVLink: GPU-to-GPU within a node (TB/s)
  • InfiniBand HDR: cross-node fabric (200 Gb/s)
  • Ethernet (RoCE): cheaper, slightly higher latency
  • BGP peering: uplink to internet backbone
  • Cross-connects: direct paths to cloud providers

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🏛️ What Is a Community Data Center?

A community data center is cooperatively owned infrastructure where a group pools capital to purchase, house, and share dedicated AI compute. Think of it like a credit union for GPUs.

  • Shared ownership of physical hardware
  • Governance by member vote or designated ops team
  • Compute allocated by share / contribution
  • Data stays within the cooperative
  • Can host at a colo facility to reduce ops burden

💰 Why Pool Resources?

A single H100 GPU costs ~$30,000. A proper training cluster runs into millions. Pooling makes otherwise unaffordable compute accessible.

  • Cost per member drops dramatically
  • Access to hardware not available on spot markets
  • Long-term cost advantage over cloud at scale
  • Predictable capacity — no spot interruptions
  • Build institutional knowledge together

🔧 Starting a Compute Co-op

Starting a compute co-op takes planning but is achievable for organized groups with shared goals.

  • Define governance (LLC, co-op, or informal)
  • Agree on contribution tiers and compute allocation
  • Choose hardware target (used H100s, A100s, etc.)
  • Source a co-location facility with power guarantees
  • Set up Kubernetes or Slurm for job scheduling
  • Establish an ops rotation or hire a sysadmin
  • Document policies for usage, disputes, exits

📍 Recommended Co-location Providers

Colocation lets you own hardware without building your own facility. These providers offer GPU-friendly power densities:

  • Equinix — global, enterprise-grade, expensive
  • QTS / Switch — good mid-market options in the US
  • CoreSite — strong West Coast presence
  • Regional / local DCs — cheaper, less redundancy
  • Peer1 / Cogeco — Canadian and EU options

Target facilities with ≥20A circuits, liquid cooling support, and cross-connect options.

🧮 Community Pool Cost Estimator

📚 Resources

📄 Uptime Institute Tier Standard 💻 Open Compute Project — open hardware designs 💸 Vast.ai — cheapest GPU spot market ☁️ Lambda Labs — researcher-friendly GPU cloud 🚀 RunPod — flexible GPU pods 🤝 SF Compute — community compute cluster