Kioxia to Phase Out Legacy NAND: SLC and MLC Lines Discontinued by 2026

Kioxia, a leading player in the NAND flash memory market, has announced a strategic shift that will see the discontinuation of its SLC (Single-Level Cell) and MLC (Multi-Level Cell) NAND product lines by 2026. This decision, reported by DIGITIMES, marks a turning point for flash memory technologies that have long been the standard for reliability and endurance in numerous industrial and enterprise sectors. The market is rapidly moving towards higher-density solutions, and Kioxia's move reflects a broader industry trend.

This transition will have significant repercussions for companies that have built their infrastructures, including on-premise AI deployments, on these types of NAND. The need to plan for hardware upgrades and evaluate alternatives will become a priority to ensure operational continuity and the robustness of storage systems.

Technical Detail: The Implications of SLC and MLC NAND

SLC and MLC NAND memories are distinguished by their ability to store a single bit (SLC) or two bits (MLC) per cell, respectively. This characteristic provides them with high endurance to write/erase cycles (P/E cycles) and greater reliability compared to subsequent generations such as TLC (Triple-Level Cell) and QLC (Quad-Level Cell), which store three and four bits per cell. SLC, in particular, has long been the preferred choice for mission-critical applications, embedded systems, and industrial storage, where data longevity and integrity are fundamental parameters.

However, the downside is the cost per bit and storage density, which are significantly less advantageous than newer technologies. While TLC and QLC offer much higher density and a lower cost per gigabyte, they come with lower endurance and speed. Kioxia's decision highlights a clear market preference for cost and capacity optimization, even at the expense of the extreme endurance offered by legacy NAND.

Context and Implications for On-Premise AI

For organizations managing on-premise AI and Large Language Models (LLM) workloads, the discontinuation of SLC and MLC NAND brings crucial strategic considerations. AI infrastructures often require robust and high-performance storage solutions for storing massive training datasets, model checkpoints, embeddings, and inference logs. Although SLC and MLC NAND were not the dominant choice for mass storage in data centers due to their cost, they found use in niches where long-term reliability was essential, such as in certain caching systems or for critical data persistence in air-gapped environments.

The transition compels CTOs, DevOps leads, and infrastructure architects to re-evaluate their storage strategies. It will be necessary to consider alternatives based on TLC or QLC NAND, while simultaneously implementing advanced wear leveling techniques, over-provisioning, and more sophisticated memory management systems to mitigate lower endurance. The Total Cost of Ownership (TCO) analysis must include not only the initial hardware cost but also the expected lifespan, maintenance costs, and potential impacts on the long-term performance and reliability of AI workloads.

Final Outlook: Towards New Storage Strategies

Kioxia's move is a clear indicator of the evolving flash memory market, driven by demand for higher capacity and reduced costs. While this trend favors the adoption of more economical storage solutions for high volumes of data, it also poses challenges for applications requiring maximum endurance and reliability. Companies will need to adapt by exploring new storage architectures that balance performance, cost, and longevity.

For those evaluating on-premise deployments of LLMs and other AI applications, careful planning is essential. It will be necessary to consider not only the hardware specifications of GPUs and servers but also the entire storage pipeline, from system drives to data lakes. The adoption of TLC and QLC NAND will require greater attention to data lifecycle management and the implementation of resilient backup and recovery strategies. The market will continue to innovate, with the introduction of new technologies like PLC (Penta-Level Cell) further pushing the limits of density, making storage selection an increasingly critical element in designing efficient and sustainable AI infrastructures.