# Designing infrastructure for enterprise AI — retained record

> Retained news record from news:datacenterdynamics.

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source: [news](https://highsignal.app/data/news) (news:datacenterdynamics)  published 2026-10-30 15:34 ingested 2026-10-31 02:54

[original source ↗](https://www.datacenterdynamics.com/en/whitepapers/designing-infrastructure-for-enterprise-ai/)

## retained text



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As enterprise AI shifts from experiments to production, organizations are entering a "second wave" requiring dedicated, self-managed environments. Because standard public clouds cannot address enterprise governance, operational, and thermal demands, relying on them risks stranded capital, capacity bottlenecks, and deployment friction.
Developed by Vertiv and STL Partners, this whitepaper provides a seven-step framework to help IT leaders move beyond the cloud-versus-on-premise debate and right-size physical infrastructure to their specific workloads.
Download your copy now to explore:
- Six archetypes: Classifies training and inference workloads to standardize requirements
- Placement matrix: Directs workloads to edge, hybrid, cloud, or on-prem using scale and four constraints (criticality, sensitivity, latency, gravity)
- Four infrastructure models: Standardizes physical deployments across Edge, Small, Large, and High-Density tiers (5 to 240+ kW/rack)
- Phased roadmap: Sequentially structures Assess, Select, Deploy, and Validate steps to manage capacity and TCO
```



document fetched 2026-10-31 02:54 · canonical https://datacenterdynamics.com/en/whitepapers/designing-infrastructure-for-enterprise-ai