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Publication Date: 2026-10-16 02:30:00
Highlights
- Beyond GPUs: Enterprise AI demands a full-stack strategy—not just hardware.
- Smart Utilization: Shifting workloads require smarter placement to control rising costs.
- Hybrid Balance: Modern infrastructure favors a flexible, balanced hybrid model.
The next big enterprise AI decision may not be which model to deploy. It may be where to run it. For CIOs, the challenge is larger than buying GPUs. AI workloads place demands on servers, storage, networking and the software that ties these components together.
“It’s not just about the hardware. How do you really build your complete full stack solution to really run it, including models and inferencing?” says Anindya Sengupta, VP of AI, Cloud-native and NeoCloud/SPs, Nutanix, discussing the infrastructure requirements of enterprise AI.
The distinction matters because adding compute capacity is only one part of the problem. Enterprises must also decide how workloads are deployed, how resources are allocated and how the underlying environment is operated as applications and models change.
The problem is no longer just buying compute
The spending is already spread across several layers. Enterprises…
