Memory concept stocks heat up: How does the computing power race reshape the logic of the storage chip industry?

Artificial intelligence model training and crypto asset mining dependencies on computing power have established a clear industry consensus. Building computing infrastructure not only requires core processing units like GPUs but also relies heavily on high-bandwidth, low-latency storage chips. As model parameter scales advance from hundreds of billions to trillions, the bandwidth and capacity bottlenecks of traditional DRAM become increasingly apparent.

High Bandwidth Memory (HBM), through stacking technology and Through-Silicon Via (TSV) processes, achieves data transfer rates far exceeding those of traditional memory. This makes HBM a standard component in AI accelerators and high-performance computing clusters. Meanwhile, hash computations in crypto mining also require frequent read/write of temporary data, continuously raising performance demands on storage subsystems. The essence of the computing power race is gradually shifting from a focus solely on computational capability to a collaborative optimization of compute and storage.

How HBM Technology Is Changing the Storage Chip Industry Landscape

HBM is not merely an upgrade of traditional DRAM but a systemic overhaul of packaging architecture and circuit design. It stacks multiple layers of DRAM die vertically and connects them via silicon interposers with logic chips, significantly reducing data path lengths. This technological approach imposes extremely high requirements on manufacturing processes: controlling die thickness, bonding precision, heat dissipation, and testing yield all present substantial barriers.

Currently, only a few leading memory manufacturers are capable of mass-producing HBM at scale. This high concentration of technology shifts profit distribution within the industry chain. Upstream components such as packaging substrates, TSV equipment, and testing machinery also benefit from HBM capacity expansion. The rising technical barriers are reshaping the competitive landscape of the entire storage chip industry.

Where Are the Bottlenecks in the Memory Supply Chain?

Mass deployment of HBM faces multiple physical constraints. First, wafer capacity: high-performance DRAM chips used in HBM require advanced process lines, which have long expansion cycles. Second, packaging: TSV processes involve deep etching, insulation layer deposition, electroplating, and other precise steps, where yield fluctuations can impact final output.

Testing efficiency is also a hidden bottleneck. After stacking, HBM modules require complex warp detection, thermal cycling tests, and high-speed signal integrity analysis—testing times are much longer than traditional memory. Additionally, the supply of silicon interposers is limited by backend substrate capacity. These interconnected steps mean that bottlenecks in any single stage can delay overall delivery. The fragility of the supply chain is a core reason behind ongoing discussions about memory concept stocks.

How Capital and Power Are Reallocating in the Storage Industry Chain

From capital market performance, funds are reallocating along the HBM value chain. Companies with advanced packaging capabilities command premiums, and the valuation centers of substrate suppliers are rising, while the cyclical fluctuations in the traditional spot DRAM market are somewhat mitigated. This shift in capital flow reflects a change in industry logic: technological scarcity is increasingly becoming the main determinant of pricing, replacing capacity scale.

The shift in power dynamics is also evident in downstream customer behavior. AI compute cluster builders are increasingly involved in the storage supply chain, locking in HBM capacity through long-term agreements or joint R&D. This closer upstream-downstream relationship alters the previous model of storage industry reliance solely on spot market transactions. Bargaining power is gradually shifting from capacity holders to technological innovators.

What Are the Core Disagreements in the Market Regarding Memory Concept Stocks?

There are two main viewpoints on the sustainability of memory concept stocks. The optimistic view believes that deployment demands during AI inference will far exceed those during training, and since inference tasks also require high storage bandwidth, HBM demand has not yet peaked. Additionally, the proliferation of edge computing devices could generate more advanced storage needs.

The cautious camp focuses on supply-side rapid expansion. Several memory manufacturers have announced HBM capacity increase plans, and if new capacity is concentrated between 2026 and 2027, the supply-demand balance could temporarily reverse. Furthermore, emerging in-memory computing or near-memory computing architectures might reduce dependence on HBM at the architectural level. The collision of these perspectives constitutes the core tension in current market discussions.

What Are Other Evolutionary Directions for Memory Technology?

HBM is currently in an iterative development phase, with each generation increasing stacking layers or pin speeds to expand bandwidth. However, physical stacking layers have limits; too many layers can cause heat dissipation and signal integrity issues. Industry players are exploring alternatives, including integrating logic processing units more tightly with memory or even adopting optical interconnects to replace some electrical connections.

Another path involves material innovations in memory itself. Technologies like Ferroelectric RAM (FeRAM), Magnetoresistive RAM (MRAM), and Resistive RAM (RRAM) offer advantages in power consumption and speed. While these are not yet capable of replacing DRAM in large-capacity scenarios economically, they are beginning to find applications in embedded and in-memory computing contexts. The diversification of technical routes provides investors with richer perspectives for long-term investment.

How Should Investors Assess the Risk and Return of Memory Concept Stocks?

When evaluating related stocks, it is essential to consider them within the broader context of compute infrastructure rather than in isolation. First, distinguish between short-term capacity cycles and long-term technological trends: capacity shortages may ease within 12-18 months, but HBM’s role as a high-end compute standard is likely to persist for a long time. Second, pay attention to the ability of companies to evolve technologically; each generation of HBM involves increasing R&D investment and production difficulty, so only companies with continuous innovation can maintain market share.

It is also important to consider downstream demand risks. If AI model algorithms become more efficient, reducing the required compute power for the same tasks, storage demand could be suppressed. Geopolitical policies restricting semiconductor equipment exports also introduce uncertainties. Investors should build multi-dimensional analysis frameworks rather than simply chasing capacity shortages.

Summary

The core driver for memory concept stocks stems from the rigid demand for storage bandwidth driven by AI and high-performance computing. As the most advanced solution, HBM’s technological barriers and capacity bottlenecks are reshaping the value of the storage industry chain. Market concerns about supply release pace and alternative technological routes create reasonable disagreements, which in turn provide space for ongoing discussion and iterative analysis. Future focus should be on three key indicators: the yield ramp-up speed of new HBM production lines, the actual scale of downstream compute deployment, and the commercial progress of new storage technologies.

FAQ

Q: What is the core difference between HBM and traditional DRAM?

HBM uses multi-layer stacking and TSV processes to achieve data transfer bandwidth far exceeding traditional DRAM, but at significantly higher costs and manufacturing complexity. Traditional DRAM is suitable for general computing, while HBM mainly supports AI accelerators and high-performance computing clusters.

Q: Can the prosperity of memory concept stocks last until 2027?

It depends on the interplay between demand and supply. Demand is driven by AI deployment scale, while supply depends on capacity expansion pace. Several manufacturers have announced capacity increases; if these are realized and AI growth slows, the supply-demand balance could change. No definitive conclusion can be made now.

Q: Besides HBM, what other storage technologies are worth watching?

Emerging storage technologies like MRAM and FeRAM have advantages in low power and high-speed writing, mainly used in embedded and in-memory computing. While they are not yet direct substitutes for HBM in large-capacity scenarios, their long-term evolution is worth tracking.

Q: How significant is the impact of crypto mining’s compute demand on the storage market?

Crypto mining’s demand for storage bandwidth is lower than AI training, but the large number of mining machines still creates stable storage procurement needs. Additionally, some PoW algorithm evolutions may increase memory capacity or bandwidth requirements, which are variables requiring dynamic assessment.