NVIDIA is driving integrated design of "AI factories" with power generation capabilities to support large-scale deployment of the next-generation Rubin platform. In simple terms, this means directly pairing power sources alongside data centers rather than relying solely on local grids.

This reflects a deeper trend: in AI infrastructure, power is ascending from "supporting infrastructure" to "core constraint." When a single facility's power requirements reach hundreds of megawatts or even GW scale, local grids typically lack sufficient capacity and cannot complete grid connection within reasonable timeframes. Building proprietary or proximate power sources becomes a practical choice for circumventing bottlenecks.

NVIDIA's promotion of power integration as part of platform design indicates it recognizes a fundamental truth: if customers acquire GPUs but lack power, those chips cannot be converted into deliverable computing capacity. This will further reinforce "self-provisioned power" as a standard practice for leading players.

For computing power companies like yours, the lesson is clear: when planning clusters, treat self-provisioned power generation, grid connection solutions, and power costs as primary constraints equal in importance to GPU procurement—not as afterthoughts. Energy strategy is becoming the decisive variable in computing power delivery capability.