# HBM Related Articles

HTX News Center provides the latest articles and in-depth analysis on "HBM", covering market trends, project updates, tech developments, and regulatory policies in the crypto industry.

The Underlying Logic of Bottleneck Propagation in the AI Computing Power Industry Chain

The article analyzes the evolving bottleneck progression within the AI compute supply chain. Initially constrained by GPU chip and advanced packaging capacity (2022-2024), the primary bottleneck shifted to HBM memory (2024-2025) due to massive model parameter growth. As cluster scale expands, physical limits of copper interconnects are making optical interconnect technologies the next critical phase (2025-2026). The ultimate, emerging constraint is power delivery and advanced liquid cooling (from 2026 onward), driven by skyrocketing rack power densities exceeding traditional infrastructure limits. The core thesis is that AI compute demand follows a "Leontief" production function where solving one bottleneck immediately exposes the next in the sequence: Compute (GPU) → Memory (HBM) → Interconnect (Optics) → Power & Cooling. Each shift reallocates value and investment across the semiconductor and infrastructure landscape.

marsbit05/22 12:25

The Underlying Logic of Bottleneck Propagation in the AI Computing Power Industry Chain

marsbit05/22 12:25

From Token Explosion to Physical Bottlenecks: The Storage Bull Market Driven by Agentic AI

**From Token Explosion to Physical Bottlenecks: The Agentic AI-Driven Storage Bull Market** The AI semiconductor narrative is shifting from training to inference, which now accounts for 66% of AI compute. In the inference "Decode" phase (autoregressive token generation), GPU performance is bottlenecked by memory bandwidth and capacity, not raw compute (FLOPS). The key constraints are **HBM (High Bandwidth Memory) bandwidth** (determining token generation speed) and **HBM capacity** (determining how many requests/models can be served simultaneously). This creates a core economics equation: Token cost is proportional to (GPU + power cost) divided by Tokens/sec, which is fundamentally limited by HBM specs. This drives unprecedented demand for advanced storage. **HBM**, a 3D-stacked DRAM, is critical for AI accelerators. Its complex production consumes 3-4x more wafer capacity than standard DRAM, squeezing supply for traditional memory (DDR) and causing severe shortages. **HBF (High Bandwidth Flash)**, an emerging high-bandwidth NAND, aims to bridge the gap between HBM speed and SSD capacity for AI model weights. The market is experiencing a historic, structurally driven super-cycle. Demand is fueled by a triple engine: 1) AI training (parameter arms race), 2) AI inference explosion (especially Agentic AI with long contexts), and 3) general data center expansion. Supply is constrained by the HBM产能挤压 effect and the 2-3 year lead time for new fab capacity. Analysts project a DRAM supply deficit of ~5% in 2026. Inventory across the supply chain is at historically low levels, with OEMs securing long-term agreements (LTAs) locking in future supply. Current indicators (Q2 2026) suggest the cycle is in its mid-phase, not peaking. While spot prices have corrected from highs, contract prices are forecast to rise sharply (e.g., +70-75% QoQ for NAND). Capacity utilization remains high, and inventory days are still low. The cycle is expected to peak around mid-2027. The storage landscape is stratified, with key players in HBM (SK Hynix, Samsung, Micron), NAND/SSD/HBF (Samsung, Kioxia/WD, SanDisk), and NOR Flash (Winbond, GigaDevice) well-positioned for this AI-driven era.

marsbit05/22 03:41

From Token Explosion to Physical Bottlenecks: The Storage Bull Market Driven by Agentic AI

marsbit05/22 03:41

Samsung Bets on Mobile HBM: AI Moves from Cloud to Palm, a New Frontier in Semiconductor Investment?

Samsung is betting on bringing high-bandwidth memory (HBM) technology from servers to mobile devices, aiming to enable powerful on-device AI features in smartphones and tablets. This move is driven by the booming AI market, where HBM demand from data centers has fueled Samsung's record profits, with HBM4 already in mass production. By integrating mobile HBM, Samsung seeks to transform user AI experiences—making tasks like image generation and real-time translation faster, seamless, and more private by processing data locally. Strategically, this allows Samsung to leverage its vertical integration in memory, advanced packaging, and Exynos processors to differentiate its Galaxy devices against competitors like Apple and Qualcomm. It also opens a new consumer growth avenue, reducing reliance on volatile server HBM demand alone. The initiative is expected to benefit the broader supply chain, boosting demand for advanced packaging materials, thermal solutions, and other components. While promising, risks include potential delays in mobile HBM mass production beyond 2027, high initial costs, and the cyclical nature of the memory market. Nonetheless, Samsung's push signals a broader industry shift toward hybrid cloud-edge AI computing, positioning it as a key player in defining the future of AI-powered devices and presenting a potential long-term investment theme in semiconductors.

marsbit05/19 14:49

Samsung Bets on Mobile HBM: AI Moves from Cloud to Palm, a New Frontier in Semiconductor Investment?

marsbit05/19 14:49

“Why Didn’t You Buy 2x Long SK Hynix?”

The article discusses the immense popularity of the "2x Long SK Hynix ETF" (07709.HK) in Hong Kong, which became the world's largest single-stock leveraged ETF by May 2026. Launched in October 2025, the ETF's net value soared over 1000% in seven months, significantly outperforming the 324% gain of SK Hynix's underlying stock, driven by the AI boom and a critical shift in industry demand from computing power to memory. It highlights the mechanics and risks of daily-rebalanced leveraged ETFs. In a smooth bullish market, they generate amplified returns, but during volatile periods—exemplified by market swings during geopolitical tensions in the Strait of Hormuz in March-April 2026—they suffer severe "volatility decay," where choppy price action can cause losses far exceeding twice the drop of the underlying asset. The piece frames SK Hynix, as NVIDIA's primary HBM supplier, within the classic cycle of the memory chip industry—a commoditized sector prone to boom-and-bust cycles of shortage, price hikes, overcapacity, and crashes. While current AI-driven demand and high margins (Q1 2026毛利率~79%) create a "super cycle," the article questions its sustainability. It warns that extreme profits will inevitably tempt competitors like Samsung and Micron to ramp up HBM production, potentially eroding scarcity. Furthermore, the entire narrative remains tethered to the massive AI capital expenditure of tech giants. In conclusion, the ETF's trajectory symbolizes the accelerated, all-in nature of the current AI revolution, where timeframes are compressed and market moves are extreme. However, it also underscores that while industry trends define ultimate returns, macro-geopolitical risks dictate the volatile and uncertain path to get there.

marsbit05/16 05:06

marsbit05/16 05:06

One Article to Understand the Profit Pools and Industry Landscape of the AI Storage Hierarchy

**Deciphering the Profit Pools and Industry Landscape of the AI Storage Hierarchy** AI storage architecture can be divided into six distinct layers based on proximity to computing units: 1) On-chip SRAM, 2) HBM, 3) Motherboard DRAM, 4) CXL pooling layer, 5) Enterprise SSD, and 6) NAS & Cloud Object Storage. In 2025, the total market for these layers (excluding embedded SRAM value) was approximately $229 billion, with DRAM constituting half, HBM 15%, and SSD 11%. The profit landscape is highly concentrated, with over 90% market share in the top three layers for key players. These profit pools are categorized into three types: 1) High-margin, oligopolistic silicon layers (HBM, embedded SRAM, QLC SSD), 2) High-margin, emerging interconnect layers (CXL), and 3) Scalable, recurring-revenue service layers (NAS, Cloud Object Storage). **Key Layers Analysis:** * **On-chip SRAM:** Profits accrue primarily to TSMC via advanced wafer sales for AI chips. * **HBM:** The largest AI-era profit pool, driven by AI accelerator demand. SK Hynix (57-62% share), Samsung, and Micron dominate. HBM boasts exceptionally high margins (e.g., SK Hynix's 72% operating margin in Q1 2026) and is projected to grow at a ~40% CAGR to $100 billion by 2028. * **Motherboard DRAM:** The largest market by revenue ($121.8B in 2025), controlled by Samsung, SK Hynix, and Micron. High profitability is sustained as capacity shifts to HBM. * **CXL Pooling Layer:** Enables rack-level memory sharing for AI workloads. The market is forecast to grow from $1.6B in 2024 to $23.7B by 2033. While memory giants lead, companies like Astera Labs (holding ~55% share in retimers/controllers) achieve very high margins (~76%). * **Enterprise SSD:** A major beneficiary of the AI inference era, especially QLC SSDs, with the market expected to reach $76B by 2030. Samsung, SK Hynix (including Solidigm), and Micron are key players. * **NAS & Cloud Object Storage:** The outermost data lake layer, growing steadily (CAGR ~16-17%). Profit derives from long-term data hosting, egress fees, and ecosystem lock-in, led by vendors like NetApp, Dell, and cloud providers (AWS, Azure, Google Cloud). **Summary:** Profitability correlates strongly with proximity to compute: layers like HBM and CXL components command the highest margins (60%+ and 76%+, respectively) despite smaller market sizes, while DRAM has the largest revenue base. The primary growth vectors are HBM (CAGR ~28%), Enterprise SSD (CAGR ~24%), and CXL pooling (CAGR ~37%). Barriers vary by layer, encompassing advanced manufacturing (HBM), IP/certification (CXL), and high switching costs (service layers).

marsbit05/14 04:03

One Article to Understand the Profit Pools and Industry Landscape of the AI Storage Hierarchy

marsbit05/14 04:03

SK Hynix Becomes the 'Capitalist Traitor', Samsung Employees Launch Major Strike

A major labor dispute is escalating at Samsung Electronics, with its union threatening an 18-day strike from May 21st after mediation failed. The core conflict is over profit-sharing, not the wage increase amount. The union, emboldened by a precedent set at rival SK Hynix, demands a permanent, formula-based annual bonus tied to 13% of the semiconductor division's operating profit. Management offered a large one-time payout instead, refusing to institutionalize an annual split, fearing it would set a costly precedent across Samsung's diverse business groups and the wider Korean conglomerate system. SK Hynix's deal, guaranteeing employees 10% of annual operating profit for a decade, has already delivered substantial bonuses due to its dominance in the lucrative HBM memory market for AI chips. This has triggered rare employee movement from Samsung to SK Hynix. The strike, potentially involving tens of thousands, risks impacting global DRAM and NAND production. Analysts see this as the start of a broader recalibration within the AI supply chain, where workers in critical, scarce roles are beginning to renegotiate their share of the massive profits generated, moving beyond traditional models like stock options towards more direct, transparent cash profit-sharing agreements.

marsbit05/13 05:03

SK Hynix Becomes the 'Capitalist Traitor', Samsung Employees Launch Major Strike

marsbit05/13 05:03

The AI Investment Landscape Is Being Reshaped: Beyond the 'Magnificent Seven', What Opportunities Lie in the Semiconductor Supply Chain?

AI Investment Map is Reshaping: Opportunities Beyond the 'Magnificent Seven' Since ChatGPT ignited the AI wave, investment initially focused on the "Magnificent Seven" tech giants dominating cloud infrastructure. However, the rise of DeepSeek and debates on AI capital expenditure effectiveness are shifting this dynamic. Investors now recognize opportunities deeper in the supply chain—the companies providing the essential "picks and shovels." Early concerns about an AI investment "arms race" and potential low returns were partly alleviated by strong Q1 earnings from cloud providers, validating robust compute demand. This has highlighted a more certain investment thesis: regardless of which AI applications ultimately win, massive capital expenditure will first fuel demand for semiconductors and related components. This "pick-and-shovel" logic has driven semiconductor ETFs to record highs. Key beneficiaries include: * **Memory Chipmakers (e.g., SK Hynix, Samsung, Micron)**: High Bandwidth Memory (HBM) is a critical bottleneck for AI training. * **Photonics Companies**: Crucial for high-speed data transfer within AI data centers. * **The Broader "AI-11" Semiconductor Ecosystem**: This encompasses foundries & lithography (TSMC, ASML), logic & custom chips (AMD, Broadcom, Intel, Marvell), and enterprise storage (SanDisk, Western Digital). Every dollar of AI infrastructure spending flows through this chain. While the "Magnificent Seven" remain dominant in market size, their earnings growth premium over the rest of the S&P 500 ("S&P 493") is narrowing. Market attention and marginal investment are shifting towards the expanding semiconductor supply chain. The investment narrative is evolving from "betting on the ultimate AI winner" to "investing in the certainty of the infrastructure build-out." Understanding this shift from the demand side to the supply side is key to identifying future AI investment opportunities.

marsbit05/12 08:06

The AI Investment Landscape Is Being Reshaped: Beyond the 'Magnificent Seven', What Opportunities Lie in the Semiconductor Supply Chain?

marsbit05/12 08:06

Morgan Stanley 2026 Semiconductor Report: Buy Packaging, Buy Testing, Buy China Chips, Avoid Traditional Tracks

Morgan Stanley 2026 Semiconductor Report: Buy Packaging, Buy Testing, Buy Chinese Chips; Avoid Traditional Segments. The core theme is the shift in AI compute supply from NVIDIA dominance to a three-track system of GPU + ASIC + China-local chips. The key opportunity is capturing share in this expansion, while non-AI semiconductors face marginalization due to resource reallocation to AI. Key investment conclusions, in order of priority: 1. **Advanced Packaging (CoWoS/SoIC) - Highest Conviction**: TSMC is the primary beneficiary of explosive demand, driven by massive cloud capex. Its pricing power and AI revenue share are rising significantly. 2. **Test Equipment - Undervalued & High-Growth Certainty**: Chip complexity is causing test times to double generationally, structurally driving handler/socket/probe card demand. Companies like Hon Hai Precision (Foxconn), WinWay, and MPI offer compelling value. 3. **China AI Chips (GPU/ASIC) - Long-Term Irreversible Trend**: Export controls are accelerating domestic substitution. Companies like Cambricon, with firm customer orders and SMIC's 7nm capacity support, are positioned to benefit from lower TCO (30-60% vs NVIDIA) and growing local cloud demand. 4. **Avoid Non-AI Semiconductors (Consumer/Auto/Industrial)**: These segments face a weak, structurally hindered recovery due to AI's resource "crowding-out" effect on capacity and supply chains. 5. **Memory - Severe Internal Divergence**: Strongly favor HBM (Hynix primary beneficiary) and NOR Flash (Macronix). Be cautious on interpreting price rises in DDR4/NAND as true demand recovery. The report emphasizes a 2026-2027 time window, stating the AI capital expenditure cycle is far from over. Key macro variables include persistent export controls and AI's systemic "crowding-out" effect on traditional semiconductor supply chains.

marsbit05/12 01:30

Morgan Stanley 2026 Semiconductor Report: Buy Packaging, Buy Testing, Buy China Chips, Avoid Traditional Tracks

marsbit05/12 01:30

The King of Blind Date Attire in Korea: How SK Hynix Made a Comeback Against Samsung?

In South Korea's dating scene, SK Hynix employees are now highly sought after, a status shift fueled by the company's astronomical profits and employee bonuses, projected to reach up to 6.1 million RMB per person by 2027. This marks a dramatic reversal for the long-time second-place player in memory semiconductors, which has now surpassed its rival Samsung in annual operating profit. The turnaround story began in 2008 when a struggling Hynix, emerging from bankruptcy restructuring, took a risky bet by agreeing to develop High Bandwidth Memory (HBM) with AMD. At the time, HBM had no clear market beyond high-end graphics cards and was a costly, complex technology. Major players like Samsung, pursuing its own HMC technology, declined. For Hynix, with only memory as its core business, it was a gamble born of necessity. The pivotal moment came in 2012 when SK Group Chairman Chey Tae-won acquired Hynix. Defying industry downturns, he invested heavily in R&D and fabrication, sustaining the HBM project through over a decade of commercial uncertainty and internal challenges. A key break occurred around 2016-2017 when Samsung faced production issues supplying HBM2 for Google's TPU, allowing SK Hynix to gain a crucial foothold in the data center market. The AI explosion post-ChatGPT in 2022 was the catalyst, turning HBM into a critical bottleneck for AI accelerators like NVIDIA's GPUs. By 2025, SK Hynix captured 62% of the global HBM market, leaving Samsung at 17%. For the first time, its annual operating profit exceeded Samsung's. Analysts point to the "innovator's dilemma" to explain Samsung's miss: its vast, successful business portfolio made it risk-averse, preventing an all-in bet on the initially niche HBM technology. In contrast, SK Hynix, as a challenger with its back against the wall, had no choice but to commit fully. The story highlights how Korea's chaebol system allows for ultra-long-term bets beyond quarterly pressures. However, SK Hynix's lead isn't guaranteed. Samsung is aggressively catching up on HBM4, and challenges like customer concentration (heavy reliance on NVIDIA) and technical hurdles in advanced packaging remain. The narrative underscores a market truth: the greatest alpha often comes from betting on uncertain, long-term directions others dismiss, much like HBM in 2008.

marsbit05/11 11:08

The King of Blind Date Attire in Korea: How SK Hynix Made a Comeback Against Samsung?

marsbit05/11 11:08

An In-Depth Analysis of the Reasons Behind the Recent Surge in the Memory Storage Industry

"Understanding the Recent Boom in the Memory Industry" summarizes the key structural shift in the memory sector driven by AI. Traditionally, the memory industry (DRAM, NAND) has been highly cyclical, with prices driven by inventory cycles and consumer electronics demand, which can be deferred. However, AI has fundamentally changed the demand structure. AI workloads create continuous, non-deferrable demand for high-bandwidth memory (HBM) due to GPU memory bottlenecks. This high-margin HBM production is prioritized by major manufacturers like Samsung, SK hynix, and Micron, structurally reducing the supply of traditional DRAM and NAND. Consequently, the market is transitioning from a spot-based model to a contract-based allocation system. HBM supply is reportedly booked through 2026, leading to spot shortages and sharp price increases for other memory types. Delivery lead times have also extended dramatically. Pricing power is shifting from the open market to those with long-term contracts. Primary beneficiaries are HBM-leading suppliers and large hyperscalers (Microsoft, AWS, Google) that can lock in future supply. Key risks to this "AI supercycle" include a potential slowdown in AI capital expenditure, disruptive new memory architectures, or a return to aggressive industry capacity expansion, which could reintroduce cyclicality.

marsbit05/11 10:30

An In-Depth Analysis of the Reasons Behind the Recent Surge in the Memory Storage Industry