DDN targets GPU efficiency with AI data infrastructure as the make-or-break layer

2026-07-09 · Source: AI – SiliconANGLE · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure, Robotics & Autonomous Systems · Depth: Intermediate, extended

Summary

DataDirect Networks Inc. (DDN) asserts that AI data infrastructure is the critical factor determining the return on investment for GPU deployments in the ongoing race to build AI factories. Alex Bouzari, DDN's CEO, highlighted at RAISE Summit 2026 that organizations with high GPU utilization rates achieve measurable financial outcomes, while others waste capital on underused infrastructure. DDN, which supports hundreds of thousands of GPUs for xAI, has demonstrated a 70% increase in GPU productivity for Salesforce and is internally used by NVIDIA for 8 years. The company is also involved in a dozen sovereign AI projects, addressing the demand for nationally controlled AI capabilities. DDN's Infinidat platform, designed eight years ago, supports a globally distributed hierarchy of AI nodes, connecting large AI factories (25-100 MW) with smaller edge data centers (5-10 MW) for agentic workloads.

Key takeaway

For AI Architects and MLOps Engineers building or optimizing AI factories, recognizing data infrastructure as the "make-or-break" layer is crucial. Your GPU investments' profitability directly hinges on maximizing utilization, which DDN's experience with xAI and NVIDIA demonstrates. Prioritize integrated, high-performance data solutions like DDN to ensure end-to-end SLAs, prevent wasted capital on idle GPUs, and support distributed edge-to-core AI pipelines, especially for sovereign data requirements.

Key insights

Effective AI data infrastructure is paramount for maximizing GPU utilization and achieving tangible financial returns from AI investments.

Principles

• GPU utilization directly correlates with AI investment ROI.
• Data sovereignty drives demand for nationally scoped AI infrastructure.
• End-to-end SLAs are crucial for AI factory performance.

Method

DDN's Infinidat platform connects globally distributed edge data centers (5-10 MW) to monolithic AI factories (25-100 MW) and multi-cloud environments, optimizing data flow for AI pipelines and agentic workloads.

In practice

• Implement robust AI data infrastructure to increase GPU productivity, as seen with Salesforce's 70% gain.
• Prioritize end-to-end SLAs for AI deployments to ensure consistent GPU operation and ROI.
• Design distributed edge architectures for real-time data collection and localized processing.

Topics

• AI Data Infrastructure
• GPU Efficiency
• Data Sovereignty
• AI Factories
• Distributed AI
• DDN Infinidat

Best for: CTO, VP of Engineering/Data, Executive, AI Architect, Director of AI/ML, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by AI – SiliconANGLE.