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GigaModular™ is a centrally controlled modular CDU platform purpose-built to help operators deploy and scale AI infrastructure capacity for AI, HPC, and hyperscale data centers.
LiquidStack, a Trane Technologies company, has announced the commercial availability of its GigaModular™ CDU platform. The platform’s modular, pay-as-you-grow architecture, designed to meet NVIDIA Vera Rubin specifications, enables AI data center operators to deploy liquid cooling infrastructure with greater speed, flexibility and operational simplicity through multi-MW building blocks that scale up to 14 MW.
The GigaModular™ CDU platform supports the demands of both merchant silicon and hyperscale custom silicon heat profiles across application temperature ranges, giving operators greater flexibility as compute and thermal requirements evolve.
Designed for AI, HPC, and hyperscale data centers, the platform offers:
First announced in June 2025, the GigaModular™ CDU has completed extensive multi-module system integration and full-load testing, validating performance of deployments up to 14 MW and achieving ETL certification. The platform is now commercially available, with early customer orders reflecting market demand for scalable liquid cooling infrastructure.
“Purpose-built for high-density AI environments, GigaModular™ helps operators expand cooling capacity without requiring significant infrastructure redesigns or over-provisioning upfront,” said Joe Capes, Vice President at Trane Technologies and General Manager of LiquidStack™. “Its flexible deployment and distribution architecture enables phased buildouts aligned to AI compute demand, while centralized system-level controls help simplify operations, improve efficiency and optimize capital planning.”
LiquidStack™ will showcase GigaModular™ at Datacloud Global Congress, June 2–4 at the Palais des Festivals in Cannes, France, including live VR demonstrations of the platform. Attendees can visit LiquidStack at Booth #109.
More information about the GigaModular™ CDU platform from LiquidStack