미국의 HBM 시장 : 점유율 분석, 업계 동향 및 통계, 성장 예측(2026-2031년)

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