Data Center:
AI Factories
AI factories are hyperscale data centers purpose-built for training and serving large AI models. Unlike general-purpose cloud facilities, they are optimized for extreme density, GPU/accelerator interconnect, and high-bandwidth fabrics. The term “AI factory” reflects their role as industrial-scale production sites for intelligence — turning vast data inputs into trained models that power autonomous systems, digital platforms, and robotics.
Overview
- Purpose: Train foundation models (LLMs, diffusion models, multimodal AI) and serve inference at scale.
- Scale: 50–500 MW single facilities, often grouped into 1–2 GW campuses.
- Key Features: GPU/TPU racks, liquid cooling, low-latency fabrics, massive storage for training datasets.
- Comparison: Unlike enterprise data centers (transactional) or cloud centers (multi-purpose), AI factories are single-mission facilities: maximizing AI throughput.
Architecture & Design Patterns
- Accelerator Density: Thousands to hundreds of thousands of GPUs/ASICs per site.
- Rack Design: Direct-to-chip or immersion cooling, 50–100+ kW per rack.
- Cluster Fabrics: InfiniBand, Ethernet RoCE, CXL — optimized for training scale-out.
- Data Storage: High-performance object and parallel file systems (Lustre, GPFS, Ceph).
- Power Demand: 50–100 MW per hall; dual 230–500 kV grid tie-ins standard.
- Resilience: Redundant feeds, onsite BESS, microgrids, and failover fabrics.
- Digital Twin Integration: Used for planning, workload scheduling, and energy optimization.
Bill of Materials (BOM)
| Domain |
Examples |
Role |
| Accelerators |
NVIDIA H100/H200, AMD MI300, Google TPU v5, Tesla Dojo D1 (legacy) |
Core compute engines for training/inference |
| Networking |
NVIDIA Quantum InfiniBand, Broadcom Ethernet, CXL memory pooling |
Links GPUs across racks/pods |
| Storage |
DDN ExaScaler, WekaIO, NetApp AFF, Lustre clusters |
High-throughput dataset ingest and checkpointing |
| Cooling |
Asetek D2C, Submer immersion, rear-door HXs |
Removes heat from GPU racks at >80 kW density |
| Power Systems |
MV switchgear, solid-state transformers, UPS + BESS |
Delivers stable megawatt-scale power |
| Orchestration |
Slurm, Kubernetes, custom schedulers |
Allocates jobs across 100k+ GPUs |
Key Challenges
- Energy Demand: 1–2 GW per campus strains regional grids.
- Cooling Density: Racks above 80–100 kW require liquid/immersion cooling.
- Fabric Scaling: Maintaining low-latency interconnects across tens of thousands of GPUs.
- Supply Chain: Long lead times for GPUs, switchgear, transformers, and chillers.
- Resilience: Balancing N+1 redundancy with cost and efficiency.
- Carbon Pressure: ESG mandates require clean energy matching even at unprecedented scale.
Vendors & Operators
| Vendor / Operator |
Solution |
Domain |
Key Features |
| NVIDIA |
DGX Cloud, DGX SuperPOD reference design |
Compute / Clusters |
GPU reference racks + InfiniBand fabrics |
| Microsoft Azure |
AI superclusters |
Hyperscale |
GPU clusters for OpenAI and enterprise workloads |
| Google |
TPU-based AI clusters |
Hyperscale |
Custom TPU pods integrated with Google Cloud |
| Meta |
Research SuperCluster (RSC) |
Hyperscale |
16k+ GPUs for model training |
| Amazon (AWS) |
Trainium/Inferentia + GPU clusters |
Hyperscale |
Custom AI silicon + GPU-based superclusters |
| Tesla |
Cortex (Austin), Colossus (Memphis) |
Vertically Integrated |
GPU clusters with Tesla energy integration |
| xAI |
Colossus supercluster |
AI-Specific |
100k+ NVIDIA GPUs for Grok training |
Future Outlook
- Exascale AI: Clusters surpassing exaflop training capacity by late 2020s.
- Liquid Cooling Standardization: Immersion and D2C become baseline, not optional.
- Federated Factories: Multiple AI factories linked via high-speed backbone networks.
- AI-Optimized Energy: Workload scheduling matched to carbon-free power availability.
- Chip Diversification: GPUs, TPUs, custom ASICs competing for training dominance.
- National Strategies: AI factories treated as critical infrastructure in the U.S., EU, China, and Gulf states.
FAQ
- How are AI factories different from hyperscale data centers? AI factories are optimized for training clusters (GPU density, fabrics), whereas hyperscale sites serve diverse cloud workloads.
- What is the biggest bottleneck? Power and cooling infrastructure, followed closely by GPU availability.
- Why are they called factories? They “manufacture” trained AI models at industrial scale, akin to a production plant.
- Can AI factories run inference as well? Yes, but most are skewed toward training; inference clusters are usually more distributed.
- Where are AI factories being built? U.S. (Texas, Virginia, Arizona), EU (Ireland, Nordics), Asia (Singapore, South Korea), Middle East (Saudi Arabia, UAE).
