美國HBM市場：市場佔有率分析、產業趨勢與統計、成長預測（2026-2031年）

據 Mordor Intelligence 稱，2025 年美國 HBM 市值為 10.9 億美元，預計到 2031 年將從 2026 年的 14.3 億美元成長至 49.1 億美元，預測期（2026-2031 年）複合年成長率為 27.98%。

本報告按HBM類型（HBM2E及更早版本、HBM3、HBM3E等）、技術領域（1X及以上、1Y、1Z以及1Z以下版本）、終端用戶行業（CSP和超大規模資料中心業者、網際網路平台、企業、電信等）、應用領域（AI訓練、推理、高效能運算類型、市場預測以美元計價。

美國HBM市場的趨勢與洞察

加速人工智慧訓練和推理中的記憶強度

隨著每個新平台的推出，使用的堆疊數量增加，每個堆疊的頻寬也在提升，因此，每個加速器對HBM的需求成長速度超過了加速器數量的成長速度。 NVIDIA宣布Vera Rubin將於2026年5月全面投產，該平台定位為“基於代理的AI工廠”，這表明內存頻寬在系統設計和部署規劃中仍然扮演著核心角色。三星宣布其商用HBM4的傳輸速度已達到11.7 Gbps，每個堆疊的容量高達3.3 TB/s，這表明記憶體藍圖正在穩步推進，以滿足日益苛刻的AI工作負載需求。美光已開始為Vera Rubin量產HBM4，AMD也報告稱，其資料中心銷售額將在2025年達到166億美元，這表明市場對基於HBM的計算平台的需求依然強勁。雖然訓練叢集仍然消耗大量內存，但推理叢集的需求層更為穩定，因為它們會隨著實際用戶流量和服務成長而擴展。這一趨勢支撐了美國 HBM 市場，因為無論是新部署還是持續的平台更新周期，記憶體容量都在增加。

美國資料中心超大規模GPU叢集的擴展

大規模人工智慧資料中心的擴張持續推動HBM出貨量成長，因為加速器的部署計畫與記憶體供應狀況密切相關。據NVIDIA稱，「Vera Rubin」已透過遍布30多個國家的350多家供應鏈合作夥伴，投入七款全新晶片的全面量產。這顯示系統部署規模正在擴大。美光於2026年3月開始為「Vera Rubin」量產HBM4，三星也於2026年2月開始量產商用HBM4。這兩家公司的量產時間均符合主要客戶的部署計畫。 AMD預計2025年資料中心銷售額的成長也預示著加速器需求基礎的擴大。這一點至關重要，因為HBM的需求與平台採用率相關，而非單獨的記憶體採購。隨著越來越多的買家從試點叢集轉向生產環境，緩解供應限制將變得更加困難，前置作業時間仍然是一個敏感問題。這種轉變將使美國HBM市場擁有比單一採購週期更廣泛、更永續的需求基礎。

CoWoS 及類似流程中先進封裝的產能限制。

先進封裝仍是阻礙因素。這是因為美國對尖端人工智慧加速器的需求仍然依賴有限的認證整合流程。據美國國家標準與技術研究院 (NIST) 稱，SK 海力士位於印第安納州的工廠預計要到 2028 年下半年才能開始量產，這意味著國內供應緩解只有在當前需求浪潮消退後才會發生。美光在愛達荷州、紐約州和維吉尼亞的計畫規模龐大，但這些是多年擴張計畫的一部分，而非即時部署封裝產能。因此，一旦封裝供應狀況與加速器啟動計畫出現分歧，美國 HBM 市場短期內將面臨排期壓力。這是一個結構性問題，因為新一代 HBM 不僅需要增加晶圓產量，還需要封裝檢驗和製程調整。在認證產能充分建立之前，即使終端使用者需求強勁，部署時間仍將存在不確定性。

細分市場分析

截至2025年，HBM3E佔據了美國HBM市場71.32%的佔有率，但預計到2031年，HBM4E及後續幾代HBM將以28.94%的複合年成長率成長。這一主導地位歸因於基於Blackwell架構的系統採購週期大規模，這使得HBM3E成為高容量AI加速器的核心記憶體選擇。這一代HBM提供了建構訓練密集基礎設施所需的頻寬，這也是美國HBM市場近期發展階段的特色。包括HBM2E和HBM3在內的前幾代產品仍在傳統高效能運算（HPC）和專業視覺化環境中使用，由於認證週期和部署平台的限制，這些環境難以即時進行遷移。隨著供應商從研發里程碑過渡到客戶交付，HBM4於2026年初開始全面商業部署。

此類別中成長最快的細分市場是HBM4E及後續幾代HBM。這是因為客戶現在要求在相同的封裝尺寸內實現更高的吞吐量、更大的單堆疊容量和更高的能源效率。三星宣布其商用HBM4的吞吐量高達3.3 TB/s，能源效率比HBM3E提升40%，為該細分市場的未來成長奠定了堅實的效能基礎。三星也進一步推進了2026年5月的藍圖，開始交付頻寬高達3.6 TB/s、容量高達48 GB的HBM4E樣品。 SK海力士於2025年9月完成了HBM4的研發，美光於2026年3月開始為「Vera Rubin」晶片量產HBM4。這表示下一個週期已從規劃階段進入執行階段。這項變化對美國HBM產業具有重大意義。這是因為需求成長不再取決於 HBM4 的可用性，而是取決於供應商能夠在多快的時間內將 HBM4 部署到其客戶的整個專案中。

到2025年，1Z以下的先進製程節點將占美國HBM市場規模的49.94%，預計到2031年將以28.69%的複合年成長率成長。值得注意的是，同一工藝節點類別既推動了當前的收入成長，也推動了未來的成長，這表明先進工藝技術不再是未來的選擇，而已成為當前的必需品。 1Z製程節點支撐了第一波HBM3E產品，而現在1Z以下的製程節點正在支援向HBM4和HBM4E產品的過渡。 SK海力士表示，其HBM4採用了10奈米製程和先進的MR-MUF工藝，顯示下一代HBM的性能與更先進的製造技術直接相關。三星表示，其商用HBM4結合了4奈米邏輯晶片和1c DRAM工藝，這支持了其他主要供應商所看到的「同節點」趨勢。

由於共封裝記憶體必須持續支援系統級更快的AI加速器、增加堆疊數量以及更嚴格的功耗限制，先進製程節點變得至關重要。美光公司為「Vera Rubin」專案量產HBM4表明，1Z以下的製程技術不再只是研發基準，而是實際商業供應的一部分。在國防、科學研究和認證等領域，對包括1X、1Y和1Z在內的舊製程節點系列仍有需求，因為在這些領域，持續性與尖峰性能同等重要，而且製程需求也相對寬鬆。即使專案生命週期長且平台遷移管理得當，這些舊製程節點仍然有用。儘管如此，到2025年，1Z以下的先進製程節點將佔據美國HBM市場49.94%的佔有率。這是因為在美國HBM市場，每瓦頻寬和整合效率的重要性已經超過了其他技術權衡。

其他好處：

• Excel格式的市場預測（ME）表
• 3個月的分析師支持

目錄

第1章：引言

• 研究假設和市場定義
• 調查範圍

第2章：調查方法

第3章執行摘要

第4章 市場狀況

• 市場概覽
• 市場促進因素
  • 加速人工智慧訓練和推理中的記憶強度
  • 美國資料中心超大規模GPU叢集的擴展
  • 在先進封裝和晶片組架構中採用HBM
  • 鼓勵將國內記憶體供應鏈遷回日本的獎勵以及《晶片法》提供的支持
  • 政府主導的人工智慧、國防和安全運算計畫正在推動對人腦記憶體的需求。
  • 下一代加速器和客製化晶片的HBM認證
• 市場限制因素
  • CoWoS 及類似流程中先進封裝的產能限制。
  • 多晶片高堆疊HBM製造製程中良率損失風險較高
  • 高功率密度人工智慧系統中溫度控管的局限性
  • 合格供應商集中，且合格審查週期較長
• 供應鏈分析
• 監理情勢
• 技術展望
• 波特五力分析

第5章 市場規模與成長預測

• HBM 型
  • HBM2E 及更早世代
  • HBM3
  • HBM3E
  • HBM4
  • HBM4E 及後續幾代 HBM
• 按技術領域
  • 1X 以上版本和傳統節點
  • 1Y節點
  • 1Z 節點
  • 小於 1Z 的高階節點
• 按包裝類型
  • 採用 2.5D 中介層的封裝
  • 3D堆疊
  • 扇出高級封裝
• 按最終用途行業分類
  • 雲端服務供應商和超大規模資料中心業者雲端服務商
  • 網際網路平台和人工智慧模型開發公司
  • 政府、國防、研究和學術機構
  • 企業資料中心
  • 通訊業者和網路設備供應商
  • 其他商業領域
• 透過使用
  • 訓練人工智慧模型
  • AI模型推理
  • 高效能運算和科學運算
  • 專業圖形、渲染和視覺化
  • 網路與通訊處理
  • 其他高頻寬運算工作負載

第6章 競爭情勢

• 市場集中度
• 策略趨勢
• 市佔率分析
• 公司簡介
  • SK Hynix Inc.
  • Samsung Electronics Co., Ltd.
  • Micron Technology, Inc.
• Other Ecosystem Players
  • NVIDIA Corporation
  • Advanced Micro Devices, Inc.
  • Intel Corporation
  • Broadcom Inc.
  • Marvell Technology, Inc.
  • Taiwan Semiconductor Manufacturing Company Limited
  • Amkor Technology, Inc.
  • ASE Technology Holding Co., Ltd.
  • Powertech Technology Inc.
  • Siliconware Precision Industries Co., Ltd.
  • GlobalFoundries Inc.
  • Applied Materials, Inc.
  • Cadence Design Systems, Inc.
  • Synopsys, Inc.
  • Rambus Inc.
  • Qualcomm Incorporated
  • Texas Instruments Incorporated

第7章 市場機會與未來展望

According to Mordor Intelligence, the United States HBM market size was valued at USD 1.09 billion in 2025 and estimated to grow from USD 1.43 billion in 2026 to reach USD 4.91 billion by 2031, at a CAGR of 27.98% during the forecast period (2026-2031).

This report is Segmented by HBM Type (HBM2E and Earlier, HBM3, HBM3E, and More), Technology Node (1X and Above, 1Y, 1Z, and Below 1Z), End Use Industry (CSPs and Hyperscalers, Internet Platforms, Enterprise, Telecom, and More), Application (AI Training, Inference, HPC, and Graphics), and Packaging Type (2. 5D Interposer, 3D Stacking, and Fan-Out). The Market Forecasts are Provided in Terms of Value (USD).

United States HBM Market Trends and Insights

Accelerating AI Training and Inference Memory Intensity

Per-accelerator HBM demand is rising faster than accelerator unit growth because each new platform uses more stacks and higher bandwidth per stack. NVIDIA stated that Vera Rubin ramped into full production in May 2026 and positioned the platform for agentic AI factories, which keeps memory bandwidth central to system design and deployment planning. Samsung said its commercial HBM4 reached 11.7 Gbps transfer speed and up to 3.3 TB/s per stack, which shows how memory roadmaps are being pushed to match more demanding AI workloads. Micron entered high-volume HBM4 production for Vera Rubin, and AMD reported USD 16.6 billion in 2025 data center revenue, pointing to sustained demand for HBM-equipped compute platforms. Training clusters still consume large amounts of memory, but inference fleets add a steadier demand layer as they expand with live user traffic and service growth. That pattern supports the United States HBM market because memory content now rises with both new installations and ongoing platform refresh cycles.

Hyperscale GPU Cluster Expansion in United States Data Centers

Large AI data center build-outs keep pulling HBM volumes forward because accelerator deployment schedules are closely tied to memory availability. NVIDIA said Vera Rubin entered full production with 7 new chips across more than 350 supply chain partners in 30 countries, which signals the scale of systems moving toward deployment. Micron moved into high-volume HBM4 production for Vera Rubin in March 2026, and Samsung began mass production of commercial HBM4 in February 2026, both aligned with large customer rollouts. AMD's 2025 data center revenue growth also points to a broader accelerator demand base, which matters because HBM demand follows platform adoption rather than stand-alone memory purchasing. As more buyers move from pilot clusters to production environments, supply allocations become harder to loosen and lead times remain sensitive. That shift gives the United States HBM market a broader and more durable demand base than a single procurement cycle would suggest.

Advanced Packaging Capacity Constraints Across CoWoS And Similar Flows

Advanced packaging remains a restraint because the United States demand still depends on a narrow set of qualified integration flows for frontier AI accelerators. NIST said SK hynix's Indiana facility is expected to begin mass production only in the second half of 2028, meaning domestic relief will arrive after the current demand wave. Micron's Idaho, New York, and Virginia plans are large, but they are part of a multi-year build-out rather than an immediate release of packaging capacity. This leaves the near-term United States HBM market exposed to schedule pressure whenever packaging availability and accelerator launch plans fall out of sync. The problem is structural because each new HBM generation also requires packaging validation and process tuning, not just more wafer output. Until more qualified capacity is in place, deployment timing will remain vulnerable even when end demand stays strong.

Other drivers and restraints analyzed in the detailed report include:

1. HBM Adoption in Advanced Packaging and Chiplet Architectures
2. Domestic Memory Supply Chain Reshoring Incentives and CHIPS Act Support
3. High Yield Loss Risk In Multi-Die, High-Stack HBM Manufacturing

For complete list of drivers and restraints, kindly check the Table Of Contents.

Segment Analysis

HBM3E held 71.32% of the United States HBM market share in 2025, while HBM4E and later-generation HBM are projected to expand at a 28.94% CAGR through 2031. That lead came from the large procurement cycle tied to Blackwell-based systems, which made HBM3E the core memory choice for high-volume AI accelerator deployments. The generation delivered the bandwidth needed for the training-heavy build-out that defined the recent phase of the United States HBM market. Earlier generations, including HBM2E and HBM3, still served legacy HPC and professional visualization deployments where qualification cycles and installed platforms limited immediate migration. HBM4 entered meaningful commercial activity in early 2026 as suppliers shifted from development milestones to customer shipments.

The fastest-growing part of this category is HBM4E and later-generation HBM, as customers now want higher throughput, greater capacity per stack, and stronger power efficiency within the same package footprint. Samsung said its commercial HBM4 reached up to 3.3 TB/s and improved power efficiency by 40% compared with HBM3E, giving the segment a clear performance case for future growth. Samsung also began shipping HBM4E samples in May 2026, with up to 3.6 TB/s bandwidth and 48 GB capacity, pushing the roadmap forward further that year. SK Hynix completed HBM4 development in September 2025, and Micron began high-volume HBM4 production for Vera Rubin in March 2026, confirming that the next cycle has moved from planning to execution. In the United States HBM industry, that change matters because demand growth now depends less on whether HBM4 arrives and more on how quickly suppliers can scale it across customer programs.

Advanced nodes below 1Z commanded 49.94% of the United States HBM market size in 2025 and are projected to grow at a 28.69% CAGR through 2031. This is notable because the same node class is driving both current revenue and future growth, indicating that advanced process technology has already become a current requirement rather than a future option. The 1Z node underpinned the first HBM3E wave, while nodes below 1Z now support the shift to HBM4 and HBM4E products. SK hynix said its HBM4 uses the 1b nm process and the Advanced MR-MUF process, which ties next-generation HBM performance directly to more advanced manufacturing execution. Samsung said its commercial HBM4 combines a 4 nm logic base die with a 1c DRAM process, reinforcing the same-node trend seen with another leading supplier.

Advanced nodes matter because co-packaged memory must keep pace with faster AI accelerators, larger stack counts, and tighter power envelopes at the system level. Micron's move into high-volume HBM4 production for Vera Rubin shows that below 1Z execution is now part of live commercial supply, not just a development benchmark. Legacy node families, including 1X, 1Y, and 1Z, still have demand in defense, research, and slower-qualification environments where continuity matters as much as peak performance. Those older nodes remain relevant when program lifecycles are long, and platform migration is managed cautiously. Even so, advanced nodes below 1Z accounted for 49.94% of the United States HBM market in 2025, as the United States HBM market now rewards bandwidth per watt and integration efficiency over other technical trade-offs.

Complete Report Scope:

• By HBM Type
  • HBM2E and Earlier Generations
  • HBM3
  • HBM3E
  • HBM4
  • HBM4E and Later-Generation HBM
• By Technology Node
  • 1X And Above Legacy Nodes
  • 1Y Node
  • 1Z Node
  • Advanced Nodes Below 1Z
• By Packaging Type
  • 2.5D Interposer-Based Packaging
  • 3D Stacking
  • Fan-Out Advanced Packaging
• By End Use Industry
  • Cloud Service Providers and Hyperscalers
  • Internet Platforms and AI Model Developers
  • Government, Defense, Research, and Academic Institutions
  • Enterprise Data Centers
  • Telecommunications Operators and Network Equipment Providers
  • Other Enterprise Verticals
• By Application
  • AI Model Training
  • AI Model Inference
  • HPC and Scientific Computing
  • Professional Graphics, Rendering, and Visualization
  • Network and Telecom Processing
  • Other High-Bandwidth Compute Workloads

List of Companies Covered in this Report:

1. SK Hynix Inc.
2. Samsung Electronics Co., Ltd.
3. Micron Technology, Inc.

Additional Benefits:

• The market estimate (ME) sheet in Excel format
• 3 months of analyst support

TABLE OF CONTENTS

1 INTRODUCTION

• 1.1 Study Assumptions and Market Definition
• 1.2 Scope of the Study

2 RESEARCH METHODOLOGY

3 EXECUTIVE SUMMARY

4 MARKET LANDSCAPE

• 4.1 Market Overview
• 4.2 Market Drivers
  • 4.2.1 Accelerating AI Training and Inference Memory Intensity
  • 4.2.2 Hyperscale GPU Cluster Expansion in United States Data Centers
  • 4.2.3 HBM Adoption in Advanced Packaging and Chiplet Architectures
  • 4.2.4 Domestic Memory Supply Chain Reshoring Incentives and CHIPS Act Support
  • 4.2.5 HBM Demand Pull From Sovereign AI, Defense, and Secure Compute Programs
  • 4.2.6 HBM Qualification for Next-Generation Accelerators and Custom Silicon
• 4.3 Market Restraints
  • 4.3.1 Advanced Packaging Capacity Constraints Across CoWoS and Similar Flows
  • 4.3.2 High Yield Loss Risk in Multi-Die, High-Stack HBM Manufacturing
  • 4.3.3 Thermal Management Limits in High Power Density AI Systems
  • 4.3.4 Heavy Concentration of Qualified Supply and Long Qualification Cycles
• 4.4 Supply Chain Analysis
• 4.5 Regulatory Landscape
• 4.6 Technological Outlook
• 4.7 Porter's Five Forces Analysis
  • 4.7.1 Bargaining Power of Suppliers
  • 4.7.2 Bargaining Power of Buyers
  • 4.7.3 Threat of New Entrants
  • 4.7.4 Threat of Substitutes
  • 4.7.5 Intensity of Competitive Rivalry

5 MARKET SIZE AND GROWTH FORECASTS (VALUE)

• 5.1 By HBM Type
  • 5.1.1 HBM2E and Earlier Generations
  • 5.1.2 HBM3
  • 5.1.3 HBM3E
  • 5.1.4 HBM4
  • 5.1.5 HBM4E and Later-Generation HBM
• 5.2 By Technology Node
  • 5.2.1 1X And Above Legacy Nodes
  • 5.2.2 1Y Node
  • 5.2.3 1Z Node
  • 5.2.4 Advanced Nodes Below 1Z
• 5.3 By Packaging Type
  • 5.3.1 2.5D Interposer-Based Packaging
  • 5.3.2 3D Stacking
  • 5.3.3 Fan-Out Advanced Packaging
• 5.4 By End Use Industry
  • 5.4.1 Cloud Service Providers and Hyperscalers
  • 5.4.2 Internet Platforms and AI Model Developers
  • 5.4.3 Government, Defense, Research, and Academic Institutions
  • 5.4.4 Enterprise Data Centers
  • 5.4.5 Telecommunications Operators and Network Equipment Providers
  • 5.4.6 Other Enterprise Verticals
• 5.5 By Application
  • 5.5.1 AI Model Training
  • 5.5.2 AI Model Inference
  • 5.5.3 HPC and Scientific Computing
  • 5.5.4 Professional Graphics, Rendering, and Visualization
  • 5.5.5 Network and Telecom Processing
  • 5.5.6 Other High-Bandwidth Compute Workloads

6 COMPETITIVE LANDSCAPE

• 6.1 Market Concentration
• 6.2 Strategic Moves
• 6.3 Market Share Analysis
• 6.4 Company Profiles (includes Global Level Overview, Market Level Overview, Core Segments, Financials as available, Strategic Information, Products and Services, Recent Developments)
  • 6.4.1 SK Hynix Inc.
  • 6.4.2 Samsung Electronics Co., Ltd.
  • 6.4.3 Micron Technology, Inc.
• 6.5 Other Ecosystem Players
  • 6.5.1 NVIDIA Corporation
  • 6.5.2 Advanced Micro Devices, Inc.
  • 6.5.3 Intel Corporation
  • 6.5.4 Broadcom Inc.
  • 6.5.5 Marvell Technology, Inc.
  • 6.5.6 Taiwan Semiconductor Manufacturing Company Limited
  • 6.5.7 Amkor Technology, Inc.
  • 6.5.8 ASE Technology Holding Co., Ltd.
  • 6.5.9 Powertech Technology Inc.
  • 6.5.10 Siliconware Precision Industries Co., Ltd.
  • 6.5.11 GlobalFoundries Inc.
  • 6.5.12 Applied Materials, Inc.
  • 6.5.13 Cadence Design Systems, Inc.
  • 6.5.14 Synopsys, Inc.
  • 6.5.15 Rambus Inc.
  • 6.5.16 Qualcomm Incorporated
  • 6.5.17 Texas Instruments Incorporated

7 MARKET OPPORTUNITIES AND FUTURE OUTLOOK

• 7.1 White-Space and Unmet-Need Assessment