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

According to Mordor Intelligence, the memory fabric market size is projected to expand from USD 3.17 billion in 2025 and USD 3.98 billion in 2026 to USD 12.44 billion by 2031, registering a CAGR of 25.60% between 2026 to 2031.

This report is Segmented by HBM Generation (HBM3, HBM4, and More), Stack Height (8-High, 12-High, and More), Memory Capacity Per Stack (Up To 16 GB, 16 GB To 24 GB, and More), Advanced Packaging Integration Architecture (3D Logic-Memory Integration, and More), Application (AI Training, and More), End User (Hyperscalers and CSPs, and More), and Geography. The Market Forecasts are Provided in Terms of Value (USD).

Global Memory Fabric Market Trends and Insights

Artificial Intelligence Inference Memory Bottlenecks Drive HBM Bandwidth Demand

The shift from model training toward large-scale inference is pushing the memory fabric market into a phase where bandwidth and latency matter as much as accelerator count. Micron reported that agentic AI workloads place sharply higher pressure on key-value cache movement, with KV-cache demand reaching 6.07x baseline at 10,000 concurrent requests, which shows why memory pressure rises faster as concurrency increases. The same paper showed that HBM4 delivers 2.82x higher peak theoretical bandwidth than HBM3E and supports throughput gains of up to 2.63x for agentic AI workloads at fixed compute budgets. Samsung also stated that its commercial HBM4 reached 3.3 TB/s per stack, which confirms that suppliers are already aligning products to this new demand profile. As context lengths and request volumes keep rising together, the memory fabric market is being pulled by a recurring procurement need instead of a one-time refresh. That pattern gives the memory fabric market a demand base that stays active even when compute upgrades alone no longer solve real deployment bottlenecks.

CXL-Based Memory Pooling Reduces Stranded Capacity In Hyperscale Environments

CXL pooling is giving the memory fabric market a separate expansion path because it addresses unused memory capacity that remains locked inside underutilized servers. A 2025 IEEE Transactions on Parallel and Distributed Systems paper noted that memory can represent up to 44% of the total cost of ownership in typical compute servers, and it showed that CXL memory tiering can improve throughput by up to 1.7x for production workloads through dynamic reallocation across co-located containers. That result matters because it changes memory from a fixed local asset into a shared infrastructure resource, which improves utilization without requiring all workloads to sit on the highest-cost local DRAM footprint. Marvell added switch-layer support to this direction with its Structera S 30260, which delivers 4 TB/s aggregate bandwidth and sub-microsecond memory access latency for rack-scale pooling designs. This means HBM demand at the hot tier and CXL pooling at the broader capacity tier can expand together rather than replace one another. As a result, the memory fabric market is increasing not only by faster stacks, but also by wider system architectures that can reuse and redistribute memory more efficiently.

Limited Commercial Availability Of Advanced CXL Generations Constrains Near-Term Adoption

The memory fabric market still faces a timing constraint because the most advanced CXL generations are not yet in full commercial server deployment. The input shows that CXL 3.0 remained in sampling and early qualification through mid-2026, which limited real 2026 revenue mostly to CXL 2.0 single-host memory expansion. Marvell stated that customer sampling for its Structera S 30260 CXL 3.0 switch begins in Q3 2026, which means full rack-scale memory fabrics still depend on broader CPU and memory-expander readiness. The CXL Consortium also continues to run plugfests and conformance programs, and that adds platform-level qualification cycles before broad deployment can move ahead. This timing gap delays the higher-value pooling and sharing cases that would otherwise widen the memory fabric market faster in the short term. It also keeps enterprise adoption behind hyperscaler adoption, because larger operators can absorb qualification delays more easily within ongoing infrastructure programs.

Other drivers and restraints analyzed in the detailed report include:

1. Total Cost Of Ownership Pressure In Hyperscale Data Centers Accelerates Adoption
2. Open Standards Adoption Across CPU, Memory, And Switch Vendors Enables Interoperability
3. Integration Complexity Across Hardware, Firmware, And Software Stacks Slows Deployment

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

Segment Analysis

HBM4E and next-generation HBM are projected to grow at the fastest CAGR of 26.46% through 2031, while HBM3E held 50.67% of the memory fabric market share in 2025. That mix reflects a market in transition, where current revenue is still anchored in the installed base, but future demand is already shifting toward faster and denser stacks. The memory fabric market kept HBM3E in a leading position because many training clusters deployed in 2024 and 2025 continue to run on normal 3-5 year refresh cycles. Those systems still meet the needs of many active deployments, especially where procurement has already been locked to HBM3E-based accelerator platforms. At the same time, the memory fabric market is clearly being pulled toward HBM4-class products by next-generation AI accelerator roadmaps.

SK hynix said in June 2026 that it shipped 12-layer HBM4E samples with 16 Gbps per pin, 48 GB capacity, more than 20% better power efficiency, and 17% lower heat resistance than HBM4 through Advanced MR-MUF packaging. Samsung stated that commercial HBM4 shipments began in February 2026, and Micron stated at GTC 2026 that it entered high-volume HBM4 production for NVIDIA Vera Rubin with 36 GB 12-High stacks and bandwidth above 2.8 TB/s. The ISSCC 2026 paper also showed how HBM4 is bringing more calibration and test sophistication into the die itself, which points to a deeper technical jump than a routine bandwidth lift. Older HBM3, HBM2E, and earlier generations remain in legacy HPC, professional visualization, and other lower-bandwidth environments, but their role inside the memory fabric industry is narrowing as platform roadmaps move upward. The manufacturing dependency is also changing, because HBM4 uses a 4 nm logic base die, which ties production readiness more closely to advanced foundry capacity than earlier generations did.

The 16-High and Above segment is projected to grow at a 26.79% CAGR through 2031, while the 8-High configuration held 42.37% share in 2025. The 8-High format stayed dominant because it aligned with the HBM3E 24 GB standard that powered a large part of the installed AI accelerator base through 2025. That gave the memory fabric market a stable commercial geometry with established yields, packaging familiarity, and broad deployment history. The 12-High tier now represents the main HBM4 production sweet spot, especially for 36 GB stacks entering commercial volumes in 2026 and 2027. Even so, platform requirements are moving beyond what 12-High can deliver when customers want 48 GB and larger capacities per stack.

Samsung stated that it shipped 48 GB 16-High HBM4 samples in 2026, and Micron also shipped 48 GB 16-High HBM4 samples, which confirms that the format has moved from the roadmap stage into customer validation. Applied Materials added that its Producer Avila 2 PECVD system was designed to support reliable stacking at 12-High, 16-High, and future high-layer-count structures, which shows how yield support is moving in step with stack height ambition. The 4-High and Below tier still has a place in edge AI inference and cost-sensitive deployments, but its share is shrinking as mainstream accelerator designs demand more capacity. The memory fabric market is therefore shifting from a geometry led by commercial comfort toward one led by platform need. That change matters because higher stacks increase both revenue opportunity and process difficulty across the supply chain.

Complete Report Scope:

• By HBM Generation
  • HBM2E and Earlier Generations
  • HBM3
  • HBM3E
  • HBM4
  • HBM4E and Next-Generation HBM
• By Stack Height
  • 4-High and Below
  • 8-High
  • 12-High
  • 16-High and Above
• By Memory Capacity per Stack
  • Up to 16 GB
  • 16 GB to 24 GB
  • 24 GB to 36 GB
  • Above 36 GB
• By Advanced Packaging Integration Architecture
  • 2.5D Silicon-Interposer-Based Integration
  • 2.5D Bridge or RDL-Based Advanced Packaging
  • 3D Logic-Memory Integration
  • Other Advanced Heterogeneous Integration Architectures
• By Application
  • AI Training
  • AI Inference
  • High-Performance Computing and Supercomputing
  • Data Center Analytics and In-Memory Computing
  • Networking and Telecommunications Infrastructure
  • Professional Graphics and Visualization
• By End User
  • Hyperscalers and Cloud Service Providers
  • AI Cloud and GPU-as-a-Service Providers
  • Data Center and Enterprise Infrastructure Operators
  • Research Institutions and Supercomputing Centers
  • Semiconductor and Accelerator Manufacturers
  • Telecommunications and Networking Equipment Providers
  • Government, Defense, and Aerospace Organizations
• By Geography
  • North America
    • United States
    • Canada
    • Mexico
  • Europe
    • Germany
    • United Kingdom
    • France
    • Italy
    • Rest of Europe
  • Asia-Pacific
    • China
    • Japan
    • South Korea
    • India
    • Southeast Asia
    • Rest of Asia-Pacific
  • South America
  • Middle East and Africa

Geography Analysis

Asia-Pacific led with 42.33% of the memory fabric market share in 2025 and is also projected to grow at a 27.44% CAGR through 2031. This dual lead reflects the fact that production capability and demand growth are concentrated in the same regional system. South Korea remains central because Samsung and SK Hynix anchor a large share of global HBM supply, and both companies advanced their HBM4 and HBM4E roadmaps in 2026. Taiwan also strengthens Asia-Pacific's position because advanced packaging capacity remains essential to the commercial coupling of HBM and AI accelerators. China adds strong demand through hyperscaler and cloud deployment activity, even though advanced equipment controls continue to limit domestic HBM supply expansion within the period covered by the draft.

North America remained the second-largest regional block in the memory fabric market because it combines hyperscaler spending, processor platform control, and CXL ecosystem development. The United States also anchors platform standards and system design through companies active in CPUs, controllers, switches, and memory products, including Intel, Micron, Marvell, and other infrastructure vendors. Native CXL support in Intel Xeon 6 and active commercialization of switch-layer products support North America's position as the main design and deployment center for disaggregated memory systems. The region, therefore, continues to shape technical direction even when a large share of manufacturing sits in Asia-Pacific.

Europe holds a strategic role in the memory fabric market through supercomputing, enterprise adoption, and standards-based server upgrades. EuroHPC-backed deployments and other institutional HPC programs help sustain demand for HBM-equipped accelerators, especially where performance targets stay close to exascale-class requirements. South America, the Middle East, and Africa remain early-stage regions in this market, with growth coming from cloud buildouts and data center expansion rather than from local HBM supply depth. Their absolute value is expected to rise through 2031, but the larger share of frontier AI infrastructure investment will remain concentrated in Asia-Pacific and North America.

1. Intel Corporation
2. Advanced Micro Devices, Inc.
3. Samsung Electronics Co., Ltd.
4. SK hynix Inc.
5. Micron Technology, Inc.
6. Marvell Technology, Inc.
7. Astera Labs, Inc.
8. Montage Technology Co., Ltd.
9. Credo Technology Group Holding Ltd.
10. Rambus Inc.
11. Microchip Technology Incorporated
12. Teledyne Technologies Incorporated
13. Dell Technologies Inc.
14. Lenovo Group Limited
15. Super Micro Computer, Inc.
16. SMART Modular Technologies, Inc.
17. Innodisk Corporation
18. ScaleFlux, Inc.
19. Enfabrica, Inc.
20. MemVerge, Inc.
21. H3 Platform, Inc.
22. XConn Technologies, Inc.
23. Penguin Solutions, 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 Artificial Intelligence Inference Memory Bottlenecks
  • 4.2.2 CXL-Based Memory Pooling Reduces Stranded Capacity
  • 4.2.3 Total Cost of Ownership Pressure in Hyperscale Data Centers
  • 4.2.4 Open Standards Adoption Across CPU, Memory, and Switch Vendors
  • 4.2.5 Brownfield Server Refresh Needs for Memory Expansion
  • 4.2.6 Emergence of Memory-As-A-Service Architectures
• 4.3 Market Restraints
  • 4.3.1 Limited Commercial Availability of Advanced CXL Generations
  • 4.3.2 Integration Complexity Across Hardware, Firmware, and Software Stacks
  • 4.3.3 Validation and Interoperability Risk Across Multi-Vendor Ecosystems
  • 4.3.4 High Dependency on Data Center Refresh Cycles and Capex Approval
• 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 Industry Rivalry

5 MARKET SIZE AND GROWTH FORECASTS (VALUE)

• 5.1 By HBM Generation
  • 5.1.1 HBM2E and Earlier Generations
  • 5.1.2 HBM3
  • 5.1.3 HBM3E
  • 5.1.4 HBM4
  • 5.1.5 HBM4E and Next-Generation HBM
• 5.2 By Stack Height
  • 5.2.1 4-High and Below
  • 5.2.2 8-High
  • 5.2.3 12-High
  • 5.2.4 16-High and Above
• 5.3 By Memory Capacity per Stack
  • 5.3.1 Up to 16 GB
  • 5.3.2 16 GB to 24 GB
  • 5.3.3 24 GB to 36 GB
  • 5.3.4 Above 36 GB
• 5.4 By Advanced Packaging Integration Architecture
  • 5.4.1 2.5D Silicon-Interposer-Based Integration
  • 5.4.2 2.5D Bridge or RDL-Based Advanced Packaging
  • 5.4.3 3D Logic-Memory Integration
  • 5.4.4 Other Advanced Heterogeneous Integration Architectures
• 5.5 By Application
  • 5.5.1 AI Training
  • 5.5.2 AI Inference
  • 5.5.3 High-Performance Computing and Supercomputing
  • 5.5.4 Data Center Analytics and In-Memory Computing
  • 5.5.5 Networking and Telecommunications Infrastructure
  • 5.5.6 Professional Graphics and Visualization
• 5.6 By End User
  • 5.6.1 Hyperscalers and Cloud Service Providers
  • 5.6.2 AI Cloud and GPU-as-a-Service Providers
  • 5.6.3 Data Center and Enterprise Infrastructure Operators
  • 5.6.4 Research Institutions and Supercomputing Centers
  • 5.6.5 Semiconductor and Accelerator Manufacturers
  • 5.6.6 Telecommunications and Networking Equipment Providers
  • 5.6.7 Government, Defense, and Aerospace Organizations
• 5.7 By Geography
  • 5.7.1 North America
    • 5.7.1.1 United States
    • 5.7.1.2 Canada
    • 5.7.1.3 Mexico
  • 5.7.2 Europe
    • 5.7.2.1 Germany
    • 5.7.2.2 United Kingdom
    • 5.7.2.3 France
    • 5.7.2.4 Italy
    • 5.7.2.5 Rest of Europe
  • 5.7.3 Asia-Pacific
    • 5.7.3.1 China
    • 5.7.3.2 Japan
    • 5.7.3.3 South Korea
    • 5.7.3.4 India
    • 5.7.3.5 Southeast Asia
    • 5.7.3.6 Rest of Asia-Pacific
  • 5.7.4 South America
  • 5.7.5 Middle East and Africa

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, Market Rank/Share, Products and Services, Recent Developments)
  • 6.4.1 Intel Corporation
  • 6.4.2 Advanced Micro Devices, Inc.
  • 6.4.3 Samsung Electronics Co., Ltd.
  • 6.4.4 SK hynix Inc.
  • 6.4.5 Micron Technology, Inc.
  • 6.4.6 Marvell Technology, Inc.
  • 6.4.7 Astera Labs, Inc.
  • 6.4.8 Montage Technology Co., Ltd.
  • 6.4.9 Credo Technology Group Holding Ltd.
  • 6.4.10 Rambus Inc.
  • 6.4.11 Microchip Technology Incorporated
  • 6.4.12 Teledyne Technologies Incorporated
  • 6.4.13 Dell Technologies Inc.
  • 6.4.14 Lenovo Group Limited
  • 6.4.15 Super Micro Computer, Inc.
  • 6.4.16 SMART Modular Technologies, Inc.
  • 6.4.17 Innodisk Corporation
  • 6.4.18 ScaleFlux, Inc.
  • 6.4.19 Enfabrica, Inc.
  • 6.4.20 MemVerge, Inc.
  • 6.4.21 H3 Platform, Inc.
  • 6.4.22 XConn Technologies, Inc.
  • 6.4.23 Penguin Solutions, Inc.

7 MARKET OPPORTUNITIES AND FUTURE OUTLOOK

• 7.1 White-Space and Unmet-Need Assessment