Hyperscale Data, Inc. (NYSE American: GPUS) is a technology infrastructure company specializing in high-performance computing solutions. The company designs, builds and operates energy-efficient data centers optimized for GPU-intensive workloads. By focusing on scalable, modular facilities, Hyperscale Data delivers colocation, managed hosting and turnkey deployment services for clients in artificial intelligence, machine learning, scientific research and financial analytics.
Its core offerings include GPU cloud services, where customers can provision NVIDIA and AMD GPU instances on demand, and private rack solutions that provide dedicated power and cooling for sustained heavy compute tasks. In addition, Hyperscale Data integrates advanced networking and storage systems to support large-scale model training, simulations and data analytics pipelines. The company emphasizes sustainability, leveraging renewable energy sources and innovative cooling techniques to reduce the carbon footprint of its operations.
Founded in 2020 and headquartered in Dallas, Texas, Hyperscale Data has rapidly expanded its footprint across North America and Europe. Its flagship facilities in Texas, Ireland and Northern Virginia serve as hubs for hyperscale deployments, while additional sites in Scandinavia and Asia-Pacific are under development to meet growing demand. The company’s global network enables low-latency access and high availability for multinational enterprises and research institutions.
Led by CEO Jane Tran, a veteran of the data center and cloud computing sectors, Hyperscale Data’s executive team combines deep expertise in technology, finance and sustainable infrastructure. The company maintains strategic partnerships with major hardware vendors and software providers to ensure seamless integration of the latest GPU architectures and AI frameworks. Looking ahead, Hyperscale Data aims to strengthen its position as a leading provider of next-generation compute capacity for mission-critical applications.
AI Generated. May Contain Errors.