Fluidstack 1500MW AI Data Center Campus In Cameron County

Status: Under Construction   |   Location: East of FM 509 and west of FM 106, near Rio Hondo and Valley International Airport, Cameron County, Texas, USA

Project Overview

Fluidstack Cameron County Data Center Campus is a large-scale AI-focused infrastructure development planned on unincorporated land between FM 509 and FM 106 near Rio Hondo in Cameron County, Texas. The first phase represents a planned $4 billion investment and is designed around up to 1.5 GW of utility power capacity, positioning the campus for some of the highest-density computing requirements associated with frontier artificial intelligence training and inference workloads. The project is purpose-built to accommodate large AI compute clusters that require substantial electricity, advanced cooling infrastructure, and reliable grid connectivity. Its planned utility power integration is a central component of the development, providing the electrical capacity required to support intensive GPU-based computing operations at campus scale. The facility also incorporates closed-loop water recirculation systems supplied by East Rio Hondo Water Supply Corporation, an approach designed to support cooling requirements while limiting reliance on continuous freshwater consumption. Sound attenuation berms are included in the site design to address operational noise associated with large-scale power and cooling equipment. The development is also structured to operate without local tax abatements or county subsidies, distinguishing the project from data center developments that depend on local economic incentives. Its location near Rio Hondo provides access to regional transportation and utility infrastructure while placing the campus within the broader South Texas data center development landscape. The combination of multi-gigawatt potential, dedicated AI infrastructure, substantial utility power capacity, closed-loop cooling, and direct grid integration establishes the campus as a significant planned development for high-density AI computing. As AI training and inference workloads require increasingly concentrated computing resources, the project’s infrastructure configuration is designed to accommodate large-scale deployments while maintaining dedicated power, cooling, and site infrastructure for future AI capacity.

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