Find the right infrastructure for every workload
From training large language models to deploying inference at the edge, every Enterprise AI workload has unique demands for power, cooling, connectivity, and scalability.
Use this powerful decision framework to map your AI ambitions to the ideal deployment model and physical infrastructure.
A smarter way to plan your AI infrastructure
Our AI decision framework gives you a clear, structured approach to match workloads with the right deployment model, whether that's cloud, on-premises, colocation, or edge. It helps you:
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Eliminate guesswork
Make infrastructure decisions grounded in workload-specific requirements
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Optimize spend
Avoid over-provisioning or under-building by right-sizing from the start
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Build future-ready infrastructure
Design with scalability and flexibility in mind as AI demands evolve
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Reduce time to deployment
Streamline planning so your AI initiatives go live faster
archetypes
Find the archetype that best reflects how your organization is using AI.
Explore Edge, Small, Large, and High-density AI cluster models and find the best match for your archetype.
Review the reference-design options associated with the four infrastructure models.
KEY INSIGHTS
The factors that drive AI decisions
Review the economics, bottlenecks, and assumptions before your next AI deployment.
Enterprise AI is not binary
The choices organizations face extend well beyond cloud versus on-premises and should be driven by scale and constraint intensity.
Portfolio approach prevents stranded capacity
A one-size-fits-all approach to AI infrastructure can produce stranded capacity or limit the performance and scalability of some workloads.
Economics flip at scale
When peak demand increasingly resembles steady demand, managing owned infrastructure often becomes more economical than consumption pricing.
Facility readiness is the bottleneck
Power availability, cooling capacity, and network fabric determine what can be deployed in an existing environment and on what timeline.
AI is becoming business-critical infrastructure
AI is rapidly becoming a load-bearing infrastructure for core business processes, changing the nature of infrastructure decisions.
Inference drives production demand
Production-scale generative inference can exceed the training demands of many traditional ML workloads because it becomes a continuous service on large models.
Go deeper into the insights behind the decision framework.
Download the report
