DDN targets GPU efficiency with AI data infrastructure as the make-or-break layer
The race to build AI factories is well underway, and the winning organizations have learned that AI data infrastructure determines whether or not GPU investments pay off, while others are still scrambling to assemble workable solutions. That divide is the clearest indicator from the field, said Alex Bouzari (pictured), chairman, co-founder and chief executive officer of [... ] The post DDN targets GPU efficiency with AI data infrastructure as the make-or-break layer appeared first on SiliconANGLE.
Key Takeaways
- AI data infrastructure determines GPU efficiency, DDN's Alex Bouzari explains how sovereignty and edge computing reshape AI factory design.
- GPU utilization rates distinguish organizations where AI delivers measurable financial outcomes from those where expensive infrastructure is underused.
"The world is bifurcating into those enterprises, those nations where the GPUs are being fully utilized at highest levels of efficiency and the ones where the GPUs are sitting idle," Bouzari said.
- DDN is currently involved in a dozen sovereign AI projects, and the message from governments is consistent: they want access to frontier AI capabilities without their data crossing borders.
This demand is pushing deployments of a new class of nationally scoped AI data infrastructure that goes beyond enterprise IT.
- "DDN is for data what NVIDIA is to compute," Bouzari said.
"The combination of the two delivers the end-to-end SLA, which is really all that matters.
- "But eight years ago when we started the development of our Infinidat technology, that's how we looked at it, because NVIDIA at the time told us this is what it's going to look like.
Stats & Key Facts
- #UPDATED 14:56 EDT / JULY 09 2026 AI DDN targets GPU efficiency with AI data infrastructure as the make-or-break layer by Thomas Godwin The race to build AI factories is well underway, and the winning organizations have learned that AI data infrastructure determines whether or not GPU investments pay off, while others are still scrambling to assemble workable solutions.
- #" In terms of scale, Bouzari pointed to Salesforce as an example of what optimized AI data infrastructure delivers, a 70% increase in GPU productivity after DDN was deployed.
- #This includes large factories in the 25 to 100 megawatt range handling model training, connected to edge data centers, collecting real-time data from autonomous vehicles, robots and sensors across the world.
AI data infrastructure determines GPU efficiency, DDN's Alex Bouzari explains how sovereignty and edge computing reshape AI factory design. UPDATED 14:56 EDT / JULY 09 2026 AI DDN targets GPU efficiency with AI data infrastructure as the make-or-break layer by Thomas Godwin The race to build AI factories is well underway, and the winning organizations have learned that AI data infrastructure determines whether or not GPU investments pay off, while others are still scrambling to assemble workable solutions. That divide is the clearest indicator from the field, said Alex Bouzari (pictured), chairman, co-founder and chief executive officer of DataDirect Networks Inc.
DDN sits at the core of some of the world's largest AI deployments, including hundreds of thousands of GPUs for xAI. Bouzari said the pattern is consistent. GPU utilization rates distinguish organizations where AI delivers measurable financial outcomes from those where expensive infrastructure is underused.
"The world is bifurcating into those enterprises, those nations where the GPUs are being fully utilized at highest levels of efficiency and the ones where the GPUs are sitting idle," Bouzari said. "The ones who are not getting it right are trying to cobble solutions together, and by doing so, they're spending a lot of money, but it's wasted capital. " Bouzari spoke with theCUBE's John Furrier at the RAISE Summit during an exclusive broadcast on theCUBE, SiliconANGLE Media's livestreaming studio.
For more details please read the original article at SiliconANGLE AI.
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