Phison, the Taiwanese giant in NAND flash memory controllers, has sounded an alarm that could cool the enthusiasm of those planning on-premise deployments: the NAND chip shortage has no end in sight. Orders are already booked into the second quarter of 2027, extending a tight supply situation many hoped would ease by 2025.

The news comes via DIGITIMES, which closely follows the Asian supply chain, and lands amid rising demand for fast storage fueled by data center expansion, artificial intelligence, and edge computing. For teams designing on-premise architectures, where every component is hand-picked and purchased directly, longer lead times are a warning bell: NAND-based SSDs have become a critical resource for accelerating training and inference workloads of language models.

NAND and AI: a deeper link than it seems

Flash memory is not just a warehouse for datasets. In systems built for on-premise AI, NVMe SSDs reduce access bottlenecks during fine-tuning and inference. With models often exceeding hundreds of billions of parameters, read and write latency directly impacts throughput. Vector databases and chunking pipelines for RAG applications also demand always-available low-latency storage. A supply squeeze translates into higher prices and stretched procurement windows, factors that can inflate the TCO of a self-hosted solution.

Planning on-premise in times of scarcity

Companies evaluating bringing inference in-house now face unexpected timelines. Placing an order today may mean receiving hardware only in 2026, risking stalled expansion projects. This forces infrastructure managers to rethink storage capacity, considering aggressive quantization to shrink model size or adopting compression formats that lighten the SSD load. On the hardware side, some are already looking at a mix of SATA drives for cold storage and NVMe for hot data, even though the performance gap can penalize demanding workflows.

Beyond the chip: reflections on the supply chain

Phison’s statement is not an isolated case: other memory makers have reported difficulties keeping up with demand, especially for advanced generations (176 layers and above). For the on-premise AI world, where data sovereignty and direct hardware control remain non-negotiable advantages, the current scenario demands design flexibility that goes beyond GPU selection. Those planning to expand capacity will likely have to book far in advance or consider hybrid approaches where some storage lives in the cloud, balancing component scarcity without fully surrendering data control — a path that, despite appearances, often brings tangible management complexity.

The lack of transparency in the NAND supply chain makes it hard to foresee relief before 2027, signaling a structural shift rather than a temporary spike. Anyone building AI infrastructure would do well to read this as yet another reminder that the hardware maturity cycle is far from over.