DDN, a global leader in AI and data intelligence, has officially introduced the DDN Enterprise AI HyperPOD, a turnkey AI data platform developed in collaboration with Supermicro and accelerated by NVIDIA. The new platform is purpose-built to revolutionize how enterprises and sovereign organizations deploy, manage, and scale artificial intelligence at every level.

A Unified AI Powerhouse

The DDN Enterprise AI HyperPOD combines DDN’s Infinia Data Intelligence Platform, Supermicro’s AI-optimized infrastructure, and NVIDIA’s cutting-edge technologies, including RTX PRO 6000 Blackwell Server Edition GPUs, BlueField-3 DPUs, and NVIDIA AI Enterprise software featuring NIM and NeMo Retriever microservices. This integration delivers exceptional efficiency, scalability, and density for AI inferencing from entry-level deployments to exascale applications. Built on the NVIDIA AI Data Platform reference design, the solution serves a range of industries such as healthcare, life sciences, manufacturing, financial services, automotive, and sovereign AI environments.

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Key Business Advantages

Enterprises can deploy the DDN HyperPOD on day one through its modular configurations, cutting deployment times from months to minutes. Additionally, it boosts return on investment by accelerating data ingestion by 22x and the inference KV Cache stage by 18x compared to alternative solutions. The system also ensures sustainability with 10x power savings and unmatched density of 2PB in just 1U, making it both energy-efficient and ESG-compliant.

Furthermore, the HyperPOD is hybrid and future-ready, offering seamless integration with Oracle Cloud Infrastructure (OCI), Google Managed Lustre, and upcoming NVIDIA technologies, empowering organizations to innovate today and stay adaptable tomorrow.

Executive Insights

“AI success isn’t about how many GPUs you buy, it’s about how well you use them,” said Sven Oehme, CTO at DDN. “Enterprises are tired of wasted capacity and spiraling costs. With DDN Enterprise AI HyperPOD, built on Supermicro Edition, accelerated by NVIDIA, we’re delivering a turnkey platform that keeps GPUs 95% busy, transforms infrastructure into outcomes, and scales from edge deployments to sovereign AI factories. This is about giving CEOs and CIOs the confidence to invest in AI with measurable business returns.”

“Organizations need a new class of AI-native storage to gain real-time insights from unstructured data,” added Justin Boitano, vice president of enterprise AI products at NVIDIA. “The DDN Enterprise AI HyperPOD, which integrates the full NVIDIA AI stack on Supermicro systems, gives enterprises a proven, secure, and high-performance foundation to harness the full potential of AI data at scale.”

Vik Malyala, President & Managing Director EMEA SVP, Technology & AI at Supermicro, emphasized the turnkey value: “Supermicro’s integration of all of the technologies and software, validation, and support enables customers to receive a fully unified solution for their AI workflow.”

Customer Momentum and Configurations

Global AI cloud leader Singtel has already adopted the DDN solution to power its next-generation AI services. “Our goal is to support enterprises across industries in accelerating the adoption of agentic AI and large-scale inference with the right balance of performance, economics, security, and compliance,” said Manoj Prasana Kumar, CTO at Singtel Digital Infraco.

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To meet diverse enterprise needs, DDN offers configurations across four scales:

  • XS (4 GPUs, 0.5+PB): Ideal for inference and edge AI.
  • Small (32 GPUs, 1+PB): Optimized for cost-effective enterprise scaling.
  • Medium (64 GPUs, 3+PB): Designed for full-scale AI factories.
  • Large (256 GPUs, 12+PB): Built for exascale, sovereign-grade AI.

Each model integrates Supermicro hardware, NVIDIA AI Enterprise software, and DDN Infinia 2.3 with the NVIDIA Dynamo engine, achieving 100x faster metadata queries and enabling smooth transitions from pilot to production environments.

Redefining AI Infrastructure Efficiency

Ultimately, DDN’s Enterprise AI HyperPOD tackles one of enterprise AI’s biggest challenges wasted infrastructure. By aligning compute, networking, and data intelligence, it sustains near-perfect GPU utilization and transforms AI infrastructure from a cost center into a growth engine for scalable, measurable outcomes.

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