AI infrastructure conversations often start with compute. But they don’t end there.
As deployments move from experimentation to scale, a different constraint is becoming harder to ignore: how data is stored, moved, and accessed. Training models get talked about a lot, but it’s the underlying data layer that ultimately determines how far and how fast those systems can go. At scale, the reality is straightforward: without high-density NAND, there’s no practical path to scalable AI.
That shift is shaping a new set of questions. What does it take to keep GPUs consistently fed with data? How do organizations manage the volume of datasets, checkpoints, and inference workloads now in play? And what happens as AI extends beyond centralized environments into enterprise systems and edge devices?
In a recent Q&A, I explore these dynamics through the lens of NAND innovation. The discussion looks at why NAND flash has become foundational to AI, and why SSDs -compared to traditional HDDs - deliver the latency, throughput, and power efficiency modern AI systems require.
I also take a look at the fact that, as AI use cases diversify, storage demands are evolving along two axes at once: higher density and faster performance. Advances like our KIOXIA BiCS FLASH™ 3D flash memory, including innovations such as CMOS directly Bonded to Array (CBA), are designed to increase capacity while improving performance and power efficiency. At the same time, QLC-based solutions are helping scale storage more economically across data centers and emerging AI environments.
As inference expands across cloud, enterprise, and edge - and as more AI functionality moves closer to the user - demand for high-density, power-efficient storage will only accelerate.
For a deeper look at how KIOXIA is approaching these challenges, read the full Q&A: “How KIOXIA Is Powering the Future of AI with NAND Innovation.”
Notes:
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Disclaimer
The views and opinions expressed in this blog are those of the author(s) and do not necessarily reflect those of KIOXIA America, Inc.