According to the latest data from market research firm Counterpoint Research, China's NAND storage giant, YMTC, is rapidly expanding its share of the global NAND flash memory market. In the second quarter of 2026, based on shipment volume, it rose to third place globally, trailing only South Korea's two dominant storage chip behemoths, Samsung Electronics and SK Hynix.
The recent strong earnings reports from storage chip leaders like Samsung, SK Hynix, Seagate, SanDisk, and Western Digital, coupled with a more than 20% surge in South Korea's stock market (where these two giants hold a 50% weight) from its July 30 low—entering technical bull market territory—underscore the ongoing super cycle in HBM, DRAM, and NAND storage driven by the AI infrastructure boom. NAND flash technology is evolving from its traditional role as a "cold data/bulk storage" asset into an "extended quasi-memory layer" for the AI inference era, potentially becoming the most important frontier technology trend in the storage chip industry after HBM.
The emergence of the HBF (High Bandwidth Flash) technology route, constructed from NAND flash, further reinforces this assessment. Kioxia, SanDisk, SK Hynix, and Samsung have explicitly defined HBF as a new NAND form factor designed to address the AI "memory wall." Its goal is to provide greater capacity for AI inference, claiming that HBF can achieve a comprehensive transmission performance close to "unlimited capacity HBM" in relevant inference tests, while significantly boosting usable memory capacity. SanDisk and SK Hynix have already released the first OCP technical specification for HBF, aiming to place high-capacity, persistent NAND closer to AI accelerators. This is intended to alleviate the "memory wall" during inference, offering much higher capacity than HBM while improving bandwidth and reducing the total cost of Token service. It is not a short-term replacement for HBM but rather creates a new near-compute storage layer positioned between HBM and traditional SSDs.
From Domestic Substitution to Global Third Place: YMTC Shipments Surpass Kioxia and Micron, Chinese Power Enters the NAND Supply Chain
Counterpoint statistics show that YMTC captured a 14% market share of global NAND shipments in the second quarter, slightly exceeding Japan's NAND flash leader Kioxia and ahead of US-based DRAM/NAND storage chip giant Micron Technology (which held a 13% share). South Korea's storage giant Samsung Electronics maintained its global top position with a 25% market share, while SK Hynix ranked second with a 22% share.
The latest demand dynamics data from the storage supply chain and the broader AI computing power chain collectively indicate that the expansion of storage chip demand driven by AI computing needs is rapidly spilling over from HBM/DRAM to NAND. Massive AI inference workloads are becoming the core engine for this structural change in NAND demand. The unprecedented surge in storage demand driven by the explosive expansion of AI computing power, the extreme complexity of HBM manufacturing and packaging processes, and the long-term inelastic supply of general-purpose DRAM/NAND have jointly catalyzed a "super supply shortage cycle" in the current storage chip field.
The accelerated migration of production capacity to advanced manufacturing and the more complex packaging and testing of HBM storage systems at Samsung Electronics and SK Hynix has led to persistent capacity constraints. Furthermore, Samsung Electronics is also focusing more capacity resources on higher-margin enterprise server DRAM storage components. Collectively, these factors have limited Samsung's NAND output, yet it still holds the top market share due to years of capacity deployment and brand influence. SK Hynix's performance was strongly boosted by its subsidiary Solidigm, whose Bit shipments grew by a massive 40% quarter-over-quarter.
Meanwhile, YMTC's shipments grew by 22% year-over-year and 5% quarter-over-quarter, benefiting from the widespread global supply shortage. The company accelerated its NAND storage product line shipments to domestic OEMs and achieved mass production of 267-layer 3D NAND. It is also advancing 300+ layer NAND flash technology based on its Xtacking architecture. Kioxia, which ranked third in the previous quarter's report, saw over 30% of its shipments go to the server market. However, the sharp price increases in AI server clusters hampered customer procurement, causing its NAND capacity growth to lag behind YMTC. Micron Technology ranked fifth in shipments but still holds a higher overall revenue ranking than YMTC. It is noteworthy that while YMTC ranks third globally by NAND shipment volume, it ranks fifth by total revenue, trailing Micron Technology, Kioxia, Samsung, and SK Hynix. Counterpoint's research report indicates that this lower revenue ranking reflects a product portfolio still heavily focused on consumer-grade NAND applications, with a smaller share of higher-priced data center enterprise SSDs (eSSD) compared to other NAND giants.
AI Inference Propels NAND from 'Storage Warehouse' to the Frontline of Computing: eSSD Captures Nearly Half of Global Shipments, HBF Could Be the Next Major Storage Super Cycle After HBM
Counterpoint's just-released NAND shipment data report for the second quarter of 2026 is particularly crucial for investors focused on the AI computing theme. The data shows that server enterprise SSDs (eSSD) now account for 48% of global NAND Bit shipments, nearly double the 26% from the same period last year, and are projected to exceed 50% by year-end. Global NAND industry revenue has also increased approximately five-fold year-over-year. Earlier data from TrendForce indicated that overall Q1 enterprise SSD revenue surged 86.1% quarter-over-quarter to $18.46 billion. Supplier inventories fell to historic lows, and eSSD contract prices rose by approximately 80% in a single quarter.
Large AI training sets, model weights, checkpoints, vector databases, RAG corpora, multimodal data, logs, and inference results all require long-term residency in high-capacity storage. During training, massive datasets need to be continuously fed into GPU clusters from object storage and local NVMe SSDs. The inference era further generates vast amounts of KV Cache, long context, agent states, and retrieval data. HBM handles the highest-bandwidth "hot data layer," DRAM handles system working memory, and enterprise NAND SSDs handle the much larger, lower-cost "warm data and persistence layer" compared to HBM. Therefore, NAND is not replacing HBM but is expanding capacity alongside HBM and DRAM within the AI server's storage hierarchy. The AI era demands not only powerful compute models but also the continuous movement, storage, and low-latency retrieval of massive state data. HBM and DRAM handle the highest bandwidth working set near the GPU, but model weights, training datasets, checkpoints, vector databases, RAG knowledge bases, inference logs, agent long-term memory, and parts of the KV cache cannot all reside permanently in the expensive and limited-capacity HBM. Enterprise NVMe SSDs thus serve as the high-performance persistent layer between HBM/DRAM and HDDs or object storage.
HBF (High Bandwidth Flash) is very likely to become one of the largest sources of structural demand growth for NAND in the next cycle. Some analysts even emphasize that the latest HBF technology roadmap and outlook suggest "NAND is trying to replicate HBM's historical moment." SanDisk and SK Hynix just released the first open HBF technical specification in August. It is based on 3D NAND but is no longer just a remote SSD. By using advanced packaging and a UCIe interface positioned close to CPUs/GPUs/xPUs, a single package can achieve up to 512GB capacity with 0.4–3.0 TB/s bandwidth. SanDisk plans to provide first HBF samples in the second half of 2026, with the first AI inference devices equipped with HBF expected to enter the sample stage in early 2027. The truly revolutionary aspect of HBF is its potential to elevate NAND from a traditional "Storage Tier" to a high-capacity Memory Tier near AI accelerators. Model weights, RAG vector databases, long-term context, and some inference data no longer need to be crowded entirely into expensive HBM but can be massively migrated to NAND with higher capacity density and lower cost per Bit. If this architecture is widely adopted by major GPU/xPU makers and cloud hyperscalers, the NAND content per AI server could undergo a quantum leap. This is the immense "super catalyst" potential of HBF.
Regarding the bullish sentiment for storage chip stocks, Morgan Stanley senior analyst Shawn Kim, formerly a vociferous bear, has turned, suggesting the correction is nearing its end. This is a significant market signal. However, a more accurate understanding is not that "he suddenly believes storage prices will rise forever," but rather that he believes the market has overreacted to the "second derivative of price decline." Morgan Stanley's research report indicates that Q3 NAND contract prices are expected to rise significantly by 20% quarter-over-quarter on top of the high base from the previous quarter. The industry is also continuing to shift capacity from consumer applications to eSSD. Meanwhile, Kim has shifted the next-stage stock price driver from pure ASP growth to "LTA long-term supply agreements + FCF trajectory + strong capital returns." From an AI systems engineering perspective, the biggest difference in this storage super cycle is that HBM, server DRAM, and enterprise SSDs are being upgraded from standard cyclical commodities to physical bottlenecks for AI computing systems. Elon Musk, in his rare commentary on the storage market during a SpaceX earnings call, stated that storage supply grows by about 20% annually, but demand growth is 200% or even higher. This severe supply-demand imbalance makes price increases a basic economic law. SK Hynix CEO Kwak Noh-Jung also stated in a July conference call that 2027 could be the tightest supply year in the history of the global storage industry, and the situation where customer demand exceeds the company's supply capacity may persist until after 2030.
Comments