Storage efficiency is becoming a practical requirement for keeping performance, density and cost in check as organizations build out larger, faster data systems.

Rising demands on compute and networking are putting new pressure on the storage layer, making speed and responsiveness essential across modern deployments. That shift is showing up clearly in customer conversations, according to Avi Shetty (pictured, right), senior director of AI enablement and partnerships at Solidigm, a trademark of SK Hynix NAND Products Solutions Corp.

“I think last year when I spoke to you guys, GPU and [High Bandwidth Memory] were kind of the favorite childs,” Shetty said. “Over the last year, we’ve seen storage kind of put itself in its place where you’ve seen usages and certain solutions which have exposed the need for having high performing, reliant, scalable, high-dense storage solutions. That’s exactly what we’ve been doing at Solidigm.”

Shetty and Isaiah Weiner (left), head of product management, core software, at WekaIO Inc., spoke with theCUBE’s Dave Vellante and Savannah Peterson at SC25, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They explored how storage efficiency is becoming a central force in scaling modern AI workloads across training, inference and high-performance infrastructure. (* Disclosure below.)

Storage efficiency shapes next-generation AI design

As AI workloads expand across training clusters, inference endpoints and distributed data pipelines, storage efficiency is becoming a defining architectural requirement. Organizations want predictable responsiveness, even as GPU cores multiply and PCIe and networking systems ramp up bandwidth. The trend is visible across deployments where balanced systems outperform raw compute alone, Shetty noted.

“Two trends have emerged,” he said. “When it comes to storage, you need low latency, fast super performing storage. On the other end you need high-dense cost-effective storage solutions. A lot [is] happening in the storage world for us at Solidigm. We are excited to work with Weka to bring all of this storage goodness to our customers in an easy, scalable solution way.”

This new generation of SSD design is directly influencing how AI systems scale. Higher-performance tiers feed GPUs with precision, while high-density tiers support global inference footprints where cost-per-bit and longevity matter as much as throughput. Both are becoming essential as customers balance power constraints, model iteration cycles and long-term operating costs, according to Weiner.

“We’ve been working a long time together,” he said, referring to Weka’s partnership with Solidigm. “They were the first QLC drives that we actually had in our hands. This morning, we announced improvements for Weka customers that they can get 90% with QLC, they can get 90% of the performance, but at a massive reduction.”

Inference at scale is sharpening these priorities further. User-facing applications demand seamless responsiveness, forcing organizations to rethink how fast models load, refresh and transition between requests. The economics of token generation and GPU utilization are pushing companies toward storage-centric designs that minimize idle cycles and maximize throughput across memory and flash layers.

“Inference is what makes AI real,” Peterson said. “None of the stuff that we’re talking about in terms of its real-world application and experience exists if inference isn’t optimized.”

Here’s the complete video interview, part of SiliconANGLE’s and theCUBE’s coverage of SC25:

(* Disclosure: Solidigm sponsored this segment of theCUBE. Neither Solidigm nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE