Insight · AI Infrastructure
AI Training vs. Inference Infrastructure Requirements
Training and inference stress infrastructure differently. Where each runs best depends on power, latency, and growth patterns.
· 7 min read
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WORKLOAD — AI TRAINING
INFRASTRUCTURE CONSIDERATIONS
Training jobs run near peak power for long periods. Electrical capacity and redundancy must be planned for steady-state load.
Current accelerator generations increasingly require liquid-ready or direct-to-chip capable environments.
Cable pathways, row layout, and distance limits shape cluster topology.
Clusters expand by generation. Plan contiguous growth capacity from the outset.
TYPICAL PROFILE
| Typical scale | Multi-megawatt, phased |
|---|---|
| Rack density | Documented per site |
| Cooling | Liquid-ready / direct-to-chip capable |
| Latency to users | Not a primary constraint |
| Deployment models | Dedicated hall, powered shell, powered land |
SOLUTIONS
RESOURCES
Insight · AI Infrastructure
Training and inference stress infrastructure differently. Where each runs best depends on power, latency, and growth patterns.
· 7 min read
Technical Guide · Cooling
The terms 'liquid-ready,' 'hybrid,' and 'direct-to-chip capable' mean different things. Here is how to read them.
· 8 min read
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