Nvidia Corp.‘s (NASDAQ:NVDA) AI chips may be powering today’s artificial intelligence boom, but Seagate Technology Holdings PLC (NASDAQ:STX) thinks the next leap in performance won’t come from more compute alone.
Instead, the data storage company argues that smarter storage architecture can help AI systems get more work out of the same expensive GPUs—a shift that could lower infrastructure costs while boosting productivity.
AI Needs More Than Faster Chips
The argument stems from a recent white paper Seagate published with SK hynix Inc. (NASDAQ:SKHY), which examines how inference and agentic AI workloads are changing the way data is managed. Rather than repeatedly generating the same information, modern AI applications increasingly rely on retaining context that can be reused across interactions.
“Our recent white paper with SK hynix illustrates the importance of tiered storage for inference and agentic AI workloads, which show a direct benefit to hard drive storage,” CEO Dave Mosley said on the company’s fiscal fourth-quarter earnings call.
At the center of that approach is key-value, or KV, cache, which stores previously generated context so AI models can retrieve it instead of recreating it each time. “Key-value, or KV cache, is used to retain and reuse that context efficiently,” Mosley said.
How Storage Unlocks Nvidia GPUs
According to Seagate, that seemingly simple change has an outsized impact on AI economics. By moving context across memory, solid-state drives and hard drives instead of forcing GPUs to recompute it, AI infrastructure can make better use of its most expensive hardware.
“This drives the need for increased hard drive storage and reduces GPU usage during the most compute-intensive phases of an agentic application. As a result, GPU resources are available for additional revenue-generating workloads,” Mosley said.
The message isn’t that GPUs become less important. Rather, Seagate argues that storage is becoming a bigger contributor to AI performance as inference workloads expand and models retain more context over time. That makes storage architecture an increasingly important part of the AI stack alongside compute and memory.
The Next Winner In AI Infrastructure
The comments also reinforce Seagate’s broader investment thesis that AI is creating structural demand for high-capacity storage. Management said cloud data centers now account for roughly 90% of the company’s exabyte shipments, while customers continue extending long-term supply commitments into 2029 and beyond as AI infrastructure scales.
For investors, the takeaway is that the next phase of the AI race may not be won solely by building bigger GPU clusters.
As companies look to squeeze more value out of every Nvidia accelerator they buy, the biggest upgrade could come from the storage systems working quietly behind the scenes.
Photo: Samuel Boivin / Shutterstock
