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March 16, 2026 – As artificial intelligence moves into production, organizations are discovering that the next frontier of AI infrastructure performance is the data platform itself. GPUs and accelerated computing may power AI models, but the ability to move massive volumes of data quickly and reliably is what ultimately determines how efficiently those systems operate.
Training, fine-tuning, and inference workloads require rapid access to enormous volumes of unstructured data. As a result, object storage is increasingly emerging as the foundational data layer for modern AI architectures.
This milestone reflects a broader shift in enterprise AI infrastructure, where object storage is rapidly becoming the primary data foundation for large-scale AI pipelines and GPU-accelerated computing environments. The certification is achieved under the Foundation level, which validates storage platforms against AI workloads scaling to environments of up to 128 GPUs.
HPE today announced that HPE Alletra Storage MP X10000 has achieved NVIDIA-Certified Storage validation for object-based systems at the Foundation level, becoming the first object storage system to reach this milestone.
Becoming the first object storage platform to achieve NVIDIA-Certified Storage validation underscores the critical role object storage now plays in AI architectures and validates the design of HPE Alletra Storage MP X10000 as a foundation for enterprise AI data platforms.
Jim O’Dorisio
Senior Vice President and General Manager, Storage, HPE
“AI infrastructure performance ultimately depends on how efficiently data can reach accelerated computing environments,” said Jim O’Dorisio, senior vice president and general manager, Storage, at HPE. “Becoming the first object storage platform to achieve NVIDIA-Certified Storage validation underscores the critical role object storage now plays in AI architectures and validates the design of HPE Alletra Storage MP X10000 as a foundation for enterprise AI data platforms.”
Validation for AI infrastructure
The NVIDIA-Certified Storage program validates that storage platforms can meet the demanding performance, reliability, and scalability requirements needed to support AI workloads and integrate with NVIDIA accelerated computing environments. The certification is achieved under the Foundation level, which validates storage platforms against AI workloads scaling to environments of up to 128 GPUs.
With NVIDIA-Certified Storage Foundation certification, HPE Alletra Storage MP X10000 has been validated by NVIDIA to deliver the performance required for these AI environments, with extensive benchmarking and functional testing confirming enterprise-grade availability and reliability. This validation confirms that the platform can efficiently feed data to accelerated computing resources, helping organizations accelerate model training, support lower-latency inference, and improve overall GPU utilization.
Object storage designed for AI data pipelinesHPE Alletra Storage MP X10000 is designed specifically to support large-scale unstructured data environments that power modern AI and analytics workloads.
The platform combines scale-out object storage with integrated data intelligence capabilities that help organizations:
This architecture enables organizations to deploy a data platform capable of keeping pace with the scale and performance requirements of modern AI infrastructure.
Advancing the AI data ecosystemThe certification reflects the continued collaboration between HPE and NVIDIA to accelerate every stage of the AI data lifecycle. By combining HPE Alletra Storage MP X10000 with NVIDIA accelerated computing platforms, organizations can deploy AI infrastructure with a validated storage foundation designed to support the scale and performance demands of production AI workloads.
These capabilities build on the broader NVIDIA AI Computing by HPE portfolio, which integrates HPE infrastructure with NVIDIA accelerated computing, networking, and software to help enterprises deploy, operationalize, and scale AI environments more quickly and reliably.
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