Designing Enterprise AI Infrastructure: Lessons From Hyperscaler AI Networks

AI adoption is accelerating across the enterprise, creating new demands for performance, scalability, and operational efficiency. According to Gartner®, AI infrastructure is constrained by networking, not compute," as network architecture has become "the primary determinant of utilization, reliability and value." Gartner further projects that "by the end of 2027, more than half of data center switching spend will support AI workloads," underscoring the growing strategic importance of networking in AI environments.

As enterprises scale AI training and inference environments, network design increasingly determines utilization, reliability, and overall investment efficiency. Gartner notes that organizations that treat AI networking as a strategic design decision can improve infrastructure efficiency, reduce operational risk, and delay unnecessary capital expenditures.

The report also references a significant industry shift toward Ethernet-based AI networking. Gartner states that "AI networking has largely consolidated around Ethernet and RDMA (RoCE)," and recommends organizations "use Ethernet with RoCE as the standard for AI cluster networking" due to its performance, ecosystem maturity, and alignment with vendor and industry direction.

While power and facility considerations remain important, Gartner emphasizes that the network is the primary architectural decision. As AI infrastructure expands beyond enterprise-owned environments, connectivity becomes increasingly critical to delivering reliable access to AI resources, data, and applications.

Gartner also recommends that organizations treat training and inference differently. "Training workloads should remain within a single site," while "inference distribution should follow standard multiregion architectures." As AI inference becomes more distributed across regions, organizations will need resilient, high-capacity connectivity to connect users, data, applications, and AI infrastructure wherever they reside.

Download the report to learn how strategic network design can improve AI performance, reliability, and infrastructure efficiency.

Source: Gartner, Designing Enterprise AI Infrastructure: Lessons From Hyperscaler AI Networks, Nauman Raja, Andrew Lerner, 3 August 2026. Gartner is a trademark of Gartner, Inc. and/or its affiliates.