en.Wedoany.com Reported - Meta is adopting "rapid deployment structures" to build AI computing facilities at its New Albany data center campus in Ohio. The company has deployed six weatherproof tent-like structures on site to house AI computing hardware, compressing the time to bring data center computing capacity online to roughly half that of traditional construction methods.

These structures are not ordinary temporary tents but engineered shells designed for the rapid delivery of AI computing clusters. They use waterproof, puncture-resistant materials and aluminum support frames, and can accommodate GPU servers, networking equipment, power systems, and cooling infrastructure. Traditional hyperscale data centers typically require civil engineering, concrete structures, mechanical and electrical systems, grid connection, commissioning, and redundancy engineering before equipment can be gradually installed, with construction cycles often spanning years. By using rapid deployment structures adjacent to an existing data center campus, Meta is effectively converting part of the "building shell" from permanent heavy construction to lighter, faster modular hosting spaces, allowing AI chips, network clusters, and power systems to enter the installation and operational phase much earlier.

The New Albany campus is one of Meta's key AI infrastructure nodes. Public permit documents and satellite imagery show that Meta has recently advanced the construction of multiple rapid deployment structures in the area, each approximately 125,000 square feet, with some starting construction and completing main assembly between April and June 2026. For Meta, time has become a critical variable in the AI data center competition. Large model training, recommendation systems, advertising models, multimodal content generation, and AI assistant services continuously consume GPU resources, and the speed of bringing computing capacity online directly impacts model iteration cycles and product deployment capabilities. Rather than waiting for the delivery of complete permanent buildings, using rapidly deployable structures to host AI hardware allows the company to gain training and inference capacity sooner.

This approach also reflects that U.S. AI infrastructure construction is entering a more aggressive phase. In the past, the core constraints of data center construction were mainly land, server procurement, networking, and facility design. Now, power connection timelines, gas-fired power generation, modular power supply, cooling solutions, and local permitting also determine project speed. Near the New Albany campus, Meta has also deployed approximately 200 megawatts of modular gas-fired power generation facilities to bypass some grid connection waiting times. AI data centers have high load density and rapidly growing power consumption. Relying solely on traditional grid expansion could slow projects due to queue times. By combining on-site power, rapid structures, and standardized deployment, Meta is attempting to shift computing construction from "heavy asset, slow build" to "high-intensity, rapid delivery."

However, this model also brings new controversies and risks. While tent-like or rapid deployment structures can shorten construction timelines, they face stricter scrutiny regarding disaster resistance, equipment maintenance, cooling efficiency, fire safety, long-term durability, and energy emissions. AI chips are high-value, power-intensive, and require precise operating environments. Any issues with power, temperature, humidity, dust, or safety redundancy could affect cluster stability. While gas-fired power generation facilities can accelerate computing capacity, they may raise concerns among local communities about emissions, noise, and pressure on power infrastructure. Whether Meta can transform these rapid deployment structures from an emergency acceleration measure into a replicable data center engineering solution will depend on operational stability, cost efficiency, and regulatory compliance.

From an industry perspective, Meta's approach indicates that the AI computing competition is no longer just a chip procurement race but has shifted to a systemic competition involving "chips, buildings, power, networking, cooling, and construction speed." Whoever can turn GPUs from a procurement list into a running cluster faster can train models, launch services, and expand AI product capabilities sooner. The uniqueness of the New Albany project lies in pushing the traditionally cautious and standardized data center construction methods of internet companies closer to the pace of wartime engineering and rapid manufacturing. As Meta, Microsoft, Google, Amazon, xAI, and the OpenAI ecosystem continue to increase computing spending, similar rapid deployment structures, on-site power generation, and modular data center solutions are likely to appear in more U.S. AI infrastructure projects.

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