
US and Hong Kong Storage Industry
Explore the US and Hong Kong storage industry, including the growing demand created by artificial intelligence, cloud computing, and data centers. This topic covers HBM, DRAM, NAND, SSD, HDD, and related technologies, along with the products, financial performance, industry cycles, competition, and valuations of relevant US- and Hong Kong-listed companies. Company analysis, earnings coverage, and industry guides help readers understand the storage supply chain, its major participants, and the opportunities and risks associated with the sector.
Related Blogs

What Is the Roundhill DRAM ETF? Buying the AI Memory Supply Chain with One ETF
The Roundhill DRAM ETF is an actively managed memory-themed fund focused on Samsung Electronics, SK hynix, Micron, and companies across NAND, SSD, and HDD storage. Learn about its holdings, 0.65% expense ratio, AI memory growth logic, differences from SOXX and SMH, key risks, and suitable investor profile.

Will NAND Prices Keep Rising? Enterprise SSD Demand and the Memory Cycle
Will NAND prices continue to rise? This analysis looks at enterprise SSD demand, AI data centers, NAND contract prices, inventory, bit supply, capital expenditure, and memory stock valuations.

HBM Investment Theme: Samsung, SK Hynix, or Micron—Which One Deserves the Most Attention?
HBM has become a core variable in the AI chip supply chain. Compare Samsung, SK Hynix, and Micron across HBM3E, HBM4, customer qualification, margins, trading access, and cycle risk to understand which AI memory stock deserves closer attention.

What Is HBM? Why AI Servers Depend on High-Bandwidth Memory
HBM is the key high-bandwidth memory that connects GPUs with data in AI servers. This article explains HBM from its structure, the memory wall, AI training and inference, DDR/GDDR comparison, manufacturing bottlenecks, and the evolution from HBM3E to HBM4.

What Is the Relationship Between HBM and GPUs? Why NVIDIA Shipments Depend on High-Bandwidth Memory
HBM is the high-speed memory foundation of AI GPUs. Learn how HBM and GPUs work together, why NVIDIA H100, H200, B200, and Rubin keep upgrading memory capacity and bandwidth, how CoWoS packaging affects supply, and why HBM4 matters for the AI chip supply chain.

How Much Longer Can HBM Keep Rising? A Cycle View from Supply, Pricing, and AI GPU Demand
HBM demand is still being driven by AI GPUs, AI ASICs, and inference servers, but the cycle has moved from broad shortage to structural divergence. Analyze HBM’s outlook through supply, pricing, HBM4, AI GPU demand, and inventory signals.

Has the DRAM Price Cycle Entered an Upswing? Key Indicators for Memory Stock Investing
Has the DRAM price cycle entered an upswing? This analysis explains how investors can evaluate contract prices, inventory, HBM capacity shifts, server demand, capital expenditure, and memory stock valuations.

Why Does AI Compute Growth Drive Storage Demand? From Training and Inference to Cold Data Archiving
AI compute growth increases demand for training data, model checkpoints, inference cache, vector databases, and cold data archives. Understand how GPUs, enterprise SSDs, HDDs, and object storage connect across the AI storage value chain.

Why Do AI Data Centers Need Enterprise SSDs? Which NAND and SSD Stocks Benefit?
AI data centers need enterprise SSDs not only to store training data, but also to support model training, inference, RAG, KV cache, and GPU data pipelines. This article explains the NAND and enterprise SSD supply chain, and compares the potential beneficiaries among Micron, Samsung, SK hynix, Kioxia, Sandisk, and related stocks.




