HBM4 시장 : 점유율 분석, 업계 동향 및 통계, 성장 예측(2026-2031년)

According to Mordor Intelligence, the HBM4 market size is expected to increase from USD 0.15 billion in 2025 to USD 0.28 billion in 2026 and reach USD 6.17 billion by 2031, growing at a CAGR of 85.80% over 2026-2031.

This report is Segmented by Memory Capacity Per Stack (16 GB, 24 GB, 32 GB, and 64 GB and Above), Processor Interface (GPU, CPU, AI Accelerator and ASIC, FPGA, and More), Application (AI Training Servers, AI Inference Servers, High-Performance Computing (HPC) Servers, and More), End Use Industry (Cloud Service Providers, Enterprise IT, and More), and Geography. The Market Forecasts are Provided in Terms of Value (USD).

Global HBM4 Market Trends and Insights

Accelerating AI Accelerator Bandwidth Demand

The HBM4 market is being driven by AI model scaling, which now requires far more memory bandwidth within a practical power envelope than prior generations could deliver. Samsung shipped commercial HBM4 in February 2026 with a processing speed of 11.7 Gbps and up to 3.3 TB/s of bandwidth per stack, marking a major performance step over HBM3E and giving system designers a direct path to denser AI memory subsystems. Micron also positioned HBM4 above 11.0 Gbps with a 2,048-pin bus and more than 2.8 TB/s per stack, indicating that the HBM4 market is scaling around a shared bandwidth target rather than isolated vendor claims. JEDEC formalized the 2,048-bit interface and 32 independent channels per stack in April 2025, which made the bandwidth jump durable at the standards level and reduced uncertainty for long-cycle platform developers. That matters because training clusters, long-context inference, and mixture-of-experts workloads are using memory bandwidth more aggressively, so the HBM4 market is benefiting from a performance need that sits closer to the architecture than to a short product cycle. As a result, buyers are not treating HBM4 as an optional premium part, but as a practical requirement for top-tier AI systems that need sustained throughput at scale.

HBM4 Qualification Tied to Next-Gen GPU Launch Cycles

The HBM4 market behaves like a launch-synchronized ecosystem, because qualification status determines whether a supplier can participate in a new accelerator generation at volume. When platform requirements exceeded 11 Gbps during the 2025 qualification cycle, suppliers had to resubmit samples and adjust their timelines, underscoring how a single specification change could reset commercial windows across the HBM4 market. Samsung met that higher threshold in mass production at 11.7 Gbps, while Micron's current HBM4 product also exceeded 11.0 Gbps, narrowing the field to suppliers that could meet stricter platform demands on schedule. SK hynix completed HBM4 development in September 2025, achieving performance above JEDEC's 8 Gbps baseline, giving it an early qualification position and strengthening its role in first-wave allocation. The HBM4 market, therefore, rewards suppliers that secure qualification early, because qualification can lock in multi-year revenue visibility before the broader supply base reaches the same yield and speed levels. It also means customers increasingly plan memory procurement alongside platform milestones, rather than treating memory sourcing as a later-stage component decision.

Limited Advanced Packaging Capacity

The HBM4 market is most directly constrained by the availability of advanced packaging, especially CoWoS-class interposer integration and hybrid bonding capacity. TSMC's own materials show how central CoWoS has become to high-performance packaging, and the draft makes clear that booking queues have stretched well beyond normal planning windows for accelerator programs. That bottleneck matters because the HBM4 market does not scale through memory wafer starts alone, since base dies, silicon interposers, bonding tools, backend assembly, and final test all have to expand together. Micron's March 2026 acquisition of PSMC's Tongluo site was one direct response, because it added manufacturing infrastructure and strengthened its production footprint in Taiwan. Even so, additions at this stage usually take time to influence usable throughput, which means near-term customer demand can still outrun the part of the chain that converts dies into deployable HBM4 stacks. The result is that smaller AI chip customers face delayed integration windows even when their end applications remain attractive and commercially ready.

Other drivers and restraints analyzed in the detailed report include:

1. Hybrid Bonding And Advanced Packaging Upscaling
2. Rising Memory Intensity in Sovereign AI Data Centers
3. TSV Yield and Stacking Complexity at Higher Layer Counts

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

Segment Analysis

The 32 GB tier accounted for 44.31% of the HBM4 market in 2025, making it the largest commercial capacity segment at the start of the forecast period. Its position reflected the balance between usable yield, bandwidth gains, and launch timing, since 12-high stacks offered a practical path into early commercial deployment without waiting for 16-high yield stability. Samsung's first commercial HBM4 shipments in February 2026 were built around 24 GB to 36 GB configurations on its 1c DRAM process, confirming that the HBM4 market opened with products concentrated around capacity points ready for high-volume qualification. Micron's HBM4 product for current platform demand also ships as a 36 GB 12-high stack, which further shows that the initial HBM4 market favors configurations with a lower production learning burden than taller stacks. This capacity band, therefore, anchors current revenue because it is close enough to the performance frontier to win AI allocations, while still fitting the manufacturing envelope suppliers can support at scale.

The 64 GB and above segment is projected to grow at an 86.78% CAGR through 2031, and that pace is tied directly to the commercial readiness of 16-high stacking. JEDEC supports up to 64 GB per stack in a 16-high structure, which means the roadmap is defined, but the HBM4 market still depends on thinner dies and tighter process control before that roadmap translates into broad shipment volume. SK hynix presented a 48 GB 16-layer HBM4 device in early 2026, which places the 48 GB tier in a bridge position between current 12-high volume ramps and higher-density future products. Smaller 16 GB and 24 GB offerings remain relevant for networking, telecom, and edge AI workloads where bandwidth needs are lower and cost discipline matters more. The 48 GB tier is likely to benefit as tooling broadens and customers want more memory per package before the full 64 GB class reaches steady commercial yield. That leaves the HBM4 market with a clear density ladder, where current revenue is led by practical launch capacities and future upside sits in taller stacks that are still moving through the yield curve.

GPU-attached products accounted for 76.83% of processor interface revenue in 2025, making GPU the dominant interface in the HBM4 market at launch. That share reflected the scale of the existing accelerator ecosystem and the way platform transitions in AI compute pull memory demand into concentrated, high-volume procurement waves. The HBM4 market remains tied closely to GPU programs because commercial qualification, allocation priority, and system-level design all begin with the highest-volume accelerator platforms. At the same time, the interface mix already shows that memory suppliers are preparing for a broader demand structure than a pure GPU-led model. CPU-attached products continue to serve scientific simulation and high-performance compute workloads, while FPGA demand remains relevant in specialized signal processing and routing environments.

AI Accelerator and ASIC are projected to grow at an 86.71% CAGR through 2031, making it the fastest-growing processor interface segment in the HBM4 market. This rise reflects hyperscaler efforts to use custom silicon to reduce dependence on standard GPU pricing and to tune systems more closely to inference economics. The HBM4 market becomes more strategically complex under this shift, as custom ASIC programs require base-die and controller alignment earlier in the design cycle than standard GPU programs typically do. Siemens EDA stated in April 2026 that HBM4 controller architecture breaks backward compatibility with HBM3 and HBM3E, so non-GPU developers face full controller redesign rather than a simple upgrade path. That extends design-in timelines, but it also increases the value of suppliers that can support earlier technical engagement and more tailored memory integration. Over time, this will make the HBM4 market less dependent on one interface even if GPU remains the near-term revenue anchor.

Complete Report Scope:

• By Memory Capacity Per Stack
  • 16 GB
  • 24 GB
  • 32 GB
  • 48 GB
  • 64 GB and Above
• By Processor Interface
  • GPU
  • CPU
  • AI Accelerator and ASIC
  • FPGA
  • Other Processor Interfaces
• By Application
  • AI Training Servers
  • AI Inference Servers
  • High-Performance Computing (HPC) Servers
  • Networking and Telecom
  • Automotive and Edge AI
  • Other Applications
• By End Use Industry
  • Cloud Service Providers
  • Enterprise IT
  • Telecommunications
  • Automotive
  • Aerospace and Defense
  • Other End Use Industries
• By Geography
  • North America
    • United States
    • Canada
    • Mexico
  • Europe
    • Germany
    • United Kingdom
    • France
    • Italy
    • Rest of Europe
  • Asia-Pacific
    • China
    • Japan
    • South Korea
    • Taiwan
    • India
    • Rest of Asia-Pacific
  • South America
  • Middle East and Africa

Geography Analysis

North America accounted for 48.33% of the HBM4 market in 2025, making it the largest regional revenue contributor at the start of the forecast period. That position reflected the concentration of hyperscale cloud operators, the central role of U.S. accelerator platforms, and the region's influence on allocation priorities across the HBM4 market. The region also shapes trade behavior, because BIS export controls introduced under ECCN 3A090 in December 2024 brought advanced HBM products into a tighter compliance framework. BIS issued further enforcement guidance in May 2026, which clarified that license requirements also apply based on the headquarters status of certain entities, regardless of transaction location. Canada is also adding support to the regional demand base through investments in cloud and AI infrastructure, which helps broaden North American pull beyond the United States.

Asia-Pacific is projected to expand at a 86.79% CAGR through 2031, making it the fastest-growing geography in the HBM4 market. The region is unique because it combines the largest supply role with rising domestic demand, especially in South Korea and Taiwan. South Korea hosts SK hynix and Samsung Electronics, while Taiwan anchors advanced packaging through TSMC and now also carries added importance through Micron's Tongluo acquisition. South Korea's large AI data center plans strengthen the region's demand profile, because a major producer is also preparing to absorb more HBM4-equipped compute domestically. This dual role gives Asia-Pacific a structurally different position in the HBM4 market than other regions, since supply expansion and consumption growth are reinforcing each other within the same geography.

Europe started from a smaller base in 2025, but the HBM4 market is gaining strategic weight there as sovereign AI programs move into active deployment. Deutsche Telekom's Munich AI factory and SoftBank Group's 1 GW project in France show that large-scale AI infrastructure is now being built with institutional and strategic backing. South America remained early in adoption during 2025 and 2026, while the Middle East and Africa moved faster through sovereign AI investment and large planned data center capacity. That leaves the HBM4 market geographically concentrated today, but with a wider future demand map that is being shaped by policy-led compute programs and not only commercial cloud expansion.

1. Hewlett Packard Enterprise Company
2. Samsung Electronics Co., Ltd.
3. SK hynix Inc.
4. Micron Technology, Inc.
5. NVIDIA Corporation
6. Advanced Micro Devices, Inc.
7. Taiwan Semiconductor Manufacturing Company Limited
8. Amkor Technology, Inc.
9. ASE Technology Holding Co., Ltd.
10. Intel Corporation
11. Broadcom Inc.
12. Marvell Technology, Inc.
13. Rambus Inc.
14. Cadence Design Systems, Inc.
15. Synopsys, Inc.
16. Hanmi Semiconductor Co., Ltd.
17. JCET Group Co., Ltd.
18. Advantest Corporation
19. Tokyo Electron Limited
20. Lam Research Corporation

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 Accelerator Bandwidth Demand
  • 4.2.2 HBM4 Qualification Tied To Next-Gen GPU Launch Cycles
  • 4.2.3 Hybrid Bonding And Advanced Packaging Upscaling
  • 4.2.4 Rising Memory Intensity In Sovereign AI Data Centers
  • 4.2.5 Defense And High-Reliability Compute Adoption
  • 4.2.6 Thermal And Power-Efficiency Advantage Versus GDDR And HBM3E
• 4.3 Market Restraints
  • 4.3.1 Limited Advanced Packaging Capacity
  • 4.3.2 TSV Yield And Stacking Complexity At Higher Layer Counts
  • 4.3.3 Qualification Risk And Design-In Delays
  • 4.3.4 Export Controls And Supply Chain Fragmentation
• 4.4 Industry Value Chain Analysis
• 4.5 Technological Outlook
• 4.6 Impact of Macroeconomic Factors on the Market
• 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 Industry Rivalry

5 MARKET SIZE AND GROWTH FORECASTS (VALUE)

• 5.1 By Memory Capacity Per Stack
  • 5.1.1 16 GB
  • 5.1.2 24 GB
  • 5.1.3 32 GB
  • 5.1.4 48 GB
  • 5.1.5 64 GB and Above
• 5.2 By Processor Interface
  • 5.2.1 GPU
  • 5.2.2 CPU
  • 5.2.3 AI Accelerator and ASIC
  • 5.2.4 FPGA
  • 5.2.5 Other Processor Interfaces
• 5.3 By Application
  • 5.3.1 AI Training Servers
  • 5.3.2 AI Inference Servers
  • 5.3.3 High-Performance Computing (HPC) Servers
  • 5.3.4 Networking and Telecom
  • 5.3.5 Automotive and Edge AI
  • 5.3.6 Other Applications
• 5.4 By End Use Industry
  • 5.4.1 Cloud Service Providers
  • 5.4.2 Enterprise IT
  • 5.4.3 Telecommunications
  • 5.4.4 Automotive
  • 5.4.5 Aerospace and Defense
  • 5.4.6 Other End Use Industries
• 5.5 By Geography
  • 5.5.1 North America
    • 5.5.1.1 United States
    • 5.5.1.2 Canada
    • 5.5.1.3 Mexico
  • 5.5.2 Europe
    • 5.5.2.1 Germany
    • 5.5.2.2 United Kingdom
    • 5.5.2.3 France
    • 5.5.2.4 Italy
    • 5.5.2.5 Rest of Europe
  • 5.5.3 Asia-Pacific
    • 5.5.3.1 China
    • 5.5.3.2 Japan
    • 5.5.3.3 South Korea
    • 5.5.3.4 Taiwan
    • 5.5.3.5 India
    • 5.5.3.6 Rest of Asia-Pacific
  • 5.5.4 South America
  • 5.5.5 Middle East and Africa

6 COMPETITIVE LANDSCAPE

• 6.1 Market Concentration
• 6.2 Strategic Moves
• 6.3 Vendor Positioning Analysis
• 6.4 Company Profiles (includes Global Level Overview, Market Level Overview, Core Segments, Financials as available, Strategic Information, Market Rank/Share, Products and Services, Recent Developments)
  • 6.4.1 Hewlett Packard Enterprise Company
  • 6.4.2 Samsung Electronics Co., Ltd.
  • 6.4.3 SK hynix Inc.
  • 6.4.4 Micron Technology, Inc.
  • 6.4.5 NVIDIA Corporation
  • 6.4.6 Advanced Micro Devices, Inc.
  • 6.4.7 Taiwan Semiconductor Manufacturing Company Limited
  • 6.4.8 Amkor Technology, Inc.
  • 6.4.9 ASE Technology Holding Co., Ltd.
  • 6.4.10 Intel Corporation
  • 6.4.11 Broadcom Inc.
  • 6.4.12 Marvell Technology, Inc.
  • 6.4.13 Rambus Inc.
  • 6.4.14 Cadence Design Systems, Inc.
  • 6.4.15 Synopsys, Inc.
  • 6.4.16 Hanmi Semiconductor Co., Ltd.
  • 6.4.17 JCET Group Co., Ltd.
  • 6.4.18 Advantest Corporation
  • 6.4.19 Tokyo Electron Limited
  • 6.4.20 Lam Research Corporation

7 MARKET OPPORTUNITIES AND FUTURE OUTLOOK

• 7.1 White-Space and Unmet-Need Assessment
• 7.2 Gradual Transition Toward HBM4E and Future HBM5 Platforms