Kioxia (KXIAY) is the structural low cost producer of NAND flash memory (yes, even compared to China) and is overhated relative to the big three DRAM suppliers. NAND (specifically Kioxia), will morph into an opportunity that is just as attractive as DRAM and its pump is only just beginning. Buckle in and let me take you on a journey.
NAND vs. DRAM:
You must understand that NAND and DRAM are very different in terms of technology, competitive forces, and their place in the AI datacenter stack.
DRAM is well-loved right now because it has consolidated into an oligopoly among the big 3 producers (Samsung, SK Hynix, Micron) and has a scarcity premium for HBM. HBM sits on-chip and the direct integration allows for extremely low latency, higher bandwidth, and lower power consumption, all of which are critical for scaling AI.
NAND has been stigmatized as a datacenter play, as it has 6 producers of scale (Samsung, SK Hynix, Kioxia, Sandisk, Micron, YMTC) and is viewed much more as a commodity with no scarce, premium tier of product and much lower barriers to entry. NAND sits off-chip and is better for mass storage than accessing data at low latency. It is still critical in AI to bridge the gap between storage and processing, but it is much easier to make and efficiency gains have been easy because all you have to do is stack more layers vertically. Each generation of NAND has brought cost-per-bit down \~15% annually.
In the past, both have been viewed as extremely cyclical industries with no supply discipline by the manufacturers. However, if competition between the DRAM suppliers has historically been a knifefight, NAND has been a machete fight and total bloodbath. The competitive structure of NAND has meant that even a 1-point supply surplus crashes prices (NAND ASP crashed in 2022 when supply grew 32% vs. demand of 31%).
Why NAND Dynamics are Rapidly Changing (it's deeper than AI = good):
There are several ongoing and emerging factors that make the current state of NAND different than in the past and will lead to a sustained supply imbalance, keeping it attractive for the foreseeable future. The market is severely discounting the need for more and more NAND as AI inference propagates.
- We are already seeing massive and growing NAND supply shortages. AI training has been all about HBM (DRAM) since every GPU needs it. Despite this, we are already seeing a supply crunch in NAND, which has led NAND prices to increase +246% since Q1 2025 (including +57% and +72% QoQ price increases in Q1 and Q2 2026 alone). 2026 capacity is completely sold out.
- Inference is the key to making this a long-term trend. The coming shift of AI from training to inference is the catalyst for continued NAND shortages. Inference token volume is the fastest growing quantity in all of computing. AI is shifting from one-off prompts from human users to long-running conversations and AI agentic workflows (AI agents generate 100x more tokens than human users).
- Inference has a scalability problem and NAND is the solution. When you hear complaints that there is no cost-benefit to adopting AI, this is largely because almost all AI infrastructure to date has been geared towards training. Inference consumes an astronomical amount of memory and GPU KV cache size limits are very quickly exceeded regardless of on-chip DRAM. Offloading memory to NAND to hold inference context is necessary.
- NAND is Nvidia's solution to the KV cache limit problem, which will sustain exponential demand growth. Nvidia recently announced the Inference Context Memory Storage Platform (ICMSP) to standardize the offload of inference context to SSD (i.e., NAND). NAND will be an integral part of the of