VDURA has launched Data Platform V12 to help AI factories and GPU cloud providers reduce storage-related bottlenecks. The platform adds multi-tenant controls, automation, and dynamic movement of data between flas really readh and hard disks. 

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PiecesAs AI infrastructure scales through Middle East and globally, there is an increase in bottlenecks regarding data storage. GPU capacity may be attracting the most capital, but storage architecture is equally important to keep production at efficient levels. VDURA is targeting that pressure point with the general availability of its Data Platform V12, designed to help AI factories and GPU cloud providers manage multiple customers on shared infrastructure while reducing the risk of GPUs sitting idle waiting for data.  

What is VDURA? 

VDURA, a high-performance data storage company serving AI factories and GPU cloud providers. fuses the PanFS core foundation with a modern SDS business model so AI and HPC leaders gain true parallel performance, software-defined services, and scale-anywhere economics without compromising resilience. 

V12 Data Platform 

V12 lets operators serve multiple customers from a shared storage system, automate how storage is provisioned and move data between flash and hard disks as workloads change. VDURA will demonstrate the platform at Ai Everything Abu Dhabi in October 

AI factories use large clusters of graphics processing units, or GPUs, to train models and run AI services. Those processors need a continuous supply of data. Delays in loading a model, saving a training checkpoint or retrieving data for an inference request can leave computing capacity idle. For providers renting GPU capacity to several customers, storage must also keep each customer’s data and performance requirements separate. 

V12 addresses both demands through VDURA’s HYDRA architecture. Operators can assign quality of service, namespaces, encryption keys and network isolation to individual customers within a shared storage environment. This allows a provider to manage separate customer services without building and operating a dedicated storage system for each one. 

The platform also introduces REST APIs, Kubernetes CSI support and infrastructure-as-code provisioning. These capabilities let operators create and manage storage through the same automated processes used for other parts of their GPU cloud. As demand grows, they can add capacity and provision new customer environments without relying on manual storage configuration for each deployment. 

“Neocloud and AI factory operators told us exactly what they need from storage: keep the GPUs fed, isolate the tenants, automate everything, expand capacity for cold data without a second system, and move that data back to flash the moment it warms up for extended context,” said Ken Claffey, CEO of VDURA. “V12 is that list, shipped.”

This launch reflects a broader change taking place across AI infrastructure. As GPU clouds evolve from single-purpose clusters into shared environments serving multiple customers and workloads, operators will need storage systems that can scale, isolate tenants, and respond dynamically to changing data demands. 

For the region, that is becoming particularly relevant as investment in AI factories, sovereign compute and high-performance infrastructure accelerates. The next phase of the AI buildout will not be defined by GPUs alone, but by how effectively the infrastructure surrounding them can keep those processors working. 

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