AI 데이터센터용 분산형 메모리 아키텍처 시장 : 시장 점유율 분석, 업계 동향 및 통계, 성장 예측(2026-2031년)

According to Mordor Intelligence, the disaggregated memory architecture market size for AI data centers is projected to be USD 0.89 billion in 2025, USD 1.28 billion in 2026, and reach USD 6.47 billion by 2031, growing at a CAGR of 38.27% from 2026 to 2031.

This report is Segmented by Component (Memory Modules, Switches and Retimers, Controllers and Adapters, and More), Memory Technology (DRAM, and More), Architecture Type (Direct Attached Memory Expansion, and More), Application (AI Training, and More), End User (Cloud Service Providers, and More), and Geography. The Market Forecasts are Provided in Terms of Value (USD).

Global Disaggregated Memory Architecture For AI Data Centers Market Trends and Insights

AI Workload Proliferation and Memory Wall Pressure

Large language models and agentic AI systems have pushed memory capacity into the foreground because the limiting factor in many production environments is no longer raw compute alone. The disaggregated memory architecture for the AI data centers market benefits from this shift, since CXL-based pooling extends usable memory beyond DIMM slot limits while preserving standard load-store behavior for servers that need fast access paths. Vendors have also framed memory pressure as a system-level issue, with newer switch and controller designs positioned specifically to break the AI memory wall through shared and expandable memory pools. Meta's Vistara deployment made the operating case more concrete by showing a 25% reduction in ML inference server counts and a 29% reduction in distributed cache latency when recycled DDR4 was attached over a CXL fabric at hyperscale. That result matters because it shows that the disaggregated memory architecture for the AI data center market is being driven by real production bottlenecks, not by a speculative lab-only use case. As more AI fleets move from pilot clusters to broad deployment, memory efficiency becomes a direct infrastructure issue, which strengthens demand for pooled designs across both hyperscale and cloud environments.

Hyperscale Data Center Transition to Composable Infrastructure

Hyperscalers are gradually shifting from fixed server configurations toward composable designs, where compute, memory, and storage can be scaled with more independence than in traditional racks. That change supports the disaggregated memory architecture for the AI data center market by allowing operators to avoid retiring working memory assets each time a processor platform is refreshed. CXL Consortium modeling presented in 2025 showed that memory costs can fall by 16% to 27% when lower-cost DIMMs are paired with CXL expansion memory, which gives operators a clear financial reason to separate memory planning from CPU refresh cycles. The architectural shift is also evident in public cloud deployments, where Astera Labs' Leo CXL Smart Memory Controllers were used on Microsoft Azure M-series virtual machines to enable more than 1.5 times the memory capacity per server controller. Research published in 2026 further noted that the CXL ecosystem already spans more than 190 vendors across devices and IP, which means the supplier base needed for composable deployment is now broad enough to support production programs. Even so, the next wave of adoption will depend less on hardware discovery and more on software layers that can place, rebalance, and monitor pooled memory without adding heavy operating complexity.

Interoperability and Validation Complexity Across Multi-Vendor CXL Stacks

Interoperability remains a real brake on the disaggregated memory architecture for AI data centers market, because production systems must qualify CPUs, modules, retimers, switches, operating systems, and management layers as one stack. The CXL Consortium's compliance programs provide a useful baseline, but protocol conformance does not eliminate the longer-term work of system-level tuning, workload validation, and failure handling across mixed-vendor combinations. Research from 2026 clearly made this point by showing that, even as the ecosystem expanded to more than 190 vendors, incremental scaling still required careful deployment discipline and practical lessons from real cloud environments. This is one reason the disaggregated memory architecture for AI data centers market remains more accessible to hyperscalers and large cloud providers than to smaller enterprises or colocation operators with thinner validation teams. The qualification burden also stretches purchasing cycles, because buyers are often forced to test several hardware and software combinations before approving a broader rollout. Until multi-vendor interoperability becomes more routine, adoption will continue to move faster in organizations that can absorb multi-quarter validation programs than in buyers that need short and predictable deployment timelines.

Other drivers and restraints analyzed in the detailed report include:

1. Tight Coupling of CXL Ecosystem Support Across CPUs, Memory, and Switches
2. Rising Demand for Memory Utilization Optimization and Lower TCO
3. Immature Software Orchestration and Memory Tiering Tooling

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

Segment Analysis

Memory Modules accounted for 44.13% of component revenue in 2025, indicating that most deployments still begin with direct memory expansion before buyers move into more complex switching and fabric designs. That position was supported by the fact that validated CXL memory modules were already moving into customer programs, with SK hynix completing validation of a 96GB CXL 2.0-based CMM-DDR5 product and progressing work on a 128GB version. In the disaggregated memory architecture for the AI data centers market, this entry point makes sense because module-led expansion is easier to qualify than rack-level pooling and requires fewer changes to the current server design. It also helps explain why the hardware revenue base still skews toward modules, even though switches, controllers, and software are attracting growing attention. Switches and retimers matter because they create the path from simple expansion to shared memory pools, where larger efficiency gains begin to emerge.

Controllers and adapters sit in the middle of that transition because they determine how reliably memory can be expanded, monitored, and mapped across different host environments. Integration and Support Services are also becoming a more visible part of the disaggregated memory architecture for AI data centers market, since qualification, tuning, and workload testing often require engineering support beyond standard hardware fulfillment. Software and Management Platforms is projected to grow at a 39.18% CAGR through 2031, which shows that value is beginning to migrate toward the control layer as basic CXL hardware becomes more widely available. Astera Labs' COSMOS suite reflects that direction by offering link visibility, fleet-level management, and reliability telemetry around its memory controller portfolio. As the disaggregated memory architecture for AI data centers industry matures, buyers in regulated and large-scale environments are likely to switch hardware components faster than they switch orchestration and diagnostic tools, which makes software the stickier layer of the stack.

DRAM held 61.76% of the memory technology segment in 2025, and that dominance reflects its role as the only broad production-ready option for CXL direct-attached expansion with latency that still fits CPU load-store access. In the disaggregated memory architecture for the AI data centers market, DRAM remains the practical first choice because it can expand capacity without forcing applications to shift immediately toward storage-like access patterns. HBM remains important in the wider AI hardware stack, but its near-GPU placement and high cost per bit make it less suited to shared rack-scale disaggregation than to private accelerator memory. Persistent Memory still has a narrower role, mainly in use cases where byte-addressable durability matters more than DRAM-class speed, such as journaling and checkpoint-heavy workflows. That means the segment structure today still reflects operational readiness more than long-run architectural preference.

Tiered Memory, which combines DRAM and NAND, is projected to grow at a 38.97% CAGR through 2031, as it offers a more affordable path to larger, more effective memory pools. Research published in 2026 showed that CXL-hybrid memory systems can expose SSD-backed capacity as direct-access expansion through a DMA-based approach that masks part of NVMe latency, which supports much larger inference state hosting than all-DRAM designs can economically deliver. That matters for the disaggregated memory architecture for AI data centers market because many context-length-sensitive workloads cannot justify an all-DRAM footprint at production scale. It also means software policy will determine segment growth, since tiered memory only works well when page placement, hot data handling, and fallback behavior are managed with discipline. Over time, the segment is likely to broaden not because DRAM loses relevance, but because operators need more than one economic tier inside the same memory hierarchy. The disaggregated memory architecture for AI data centers market therefore keeps DRAM at the core while gradually opening more room for mixed memory classes that balance latency, capacity, and cost.

Complete Report Scope:

• By Component
  • Memory Modules
  • Switches and Retimers
  • Controllers and Adapters
  • Software and Management Platforms
  • Integration and Support Services
• By Memory Technology
  • DRAM
  • HBM
  • Persistent Memory
  • Tiered Memory (DRAM + NAND)
• By Architecture Type
  • Direct Attached Memory Expansion
  • Switched Memory Pooling
  • Rack-Scale Memory Disaggregation
  • Fabric Attached Memory
• By Application
  • AI Training
  • AI Inference
  • High Performance Computing
  • In-Memory Databases And Analytics
  • Large Language Model Serving
  • Enterprise Virtualization
• By End User
  • Hyperscalers
  • Cloud Service Providers
  • Enterprise Data Centers
  • Colocation Providers
  • Research and Supercomputing Institutions
• 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 46.28% of the disaggregated memory architecture market share in 2025, reflecting the region's concentration of hyperscale campuses, semiconductor design firms, and advanced qualification capacity. The region benefits from close proximity among CPU platform developers, memory controller specialists, switch vendors, and some of the world's largest AI infrastructure operators, which shortens deployment feedback loops. Astera Labs expanded its ecosystem reach in June 2026 by expanding its Taiwan operations and establishing a cloud-scale interoperability laboratory to strengthen its work with Asian system manufacturers and AI platform providers. For North American operators, the 16% to 27% memory cost savings modeled by the CXL Consortium remain especially relevant because rising power costs and mature data center corridors place greater emphasis on efficiency gains than on simple hardware scale. Canada is also emerging as a secondary node through AI-oriented data center investment, while Mexico remains more closely tied to edge and supporting infrastructure than to full-scale pooled memory deployment.

Europe remains smaller in current revenue, but the region is moving forward on a different logic than North America. Data residency requirements and compliance expectations make software-definable infrastructure more attractive, because buyers want visibility into how resources are assigned and governed. Germany and the United Kingdom are leading adoption through a mix of hyperscale presence and enterprise demand from finance, manufacturing, and simulation-heavy workloads. France and Italy are still earlier in the cycle, but national AI and research infrastructure programs are helping create an initial buyer base for more advanced memory topologies. Across the rest of Europe, renewable power availability and continued hyperscaler expansion into Nordic and Eastern European locations are supporting the conditions needed for later-stage adoption.

Asia-Pacific is projected to expand at a 39.09% CAGR through 2031, making it the fastest-growing regional part of the disaggregated memory architecture for AI data centers market. Taiwan continues to anchor the supply chain as the foundry base for leading CXL controllers and switch silicon, which gives the region production depth as well as demand potential. China is building domestic memory capability that can feed state-linked AI infrastructure, while India is still in an earlier capacity-building phase where hyperscaler and cloud investment lay the groundwork for future adoption. South America and the Middle East and Africa are likely to remain behind the global frontier in the near term because lower hyperscale density and higher integration costs make rack-scale CXL deployments harder to justify early.

1. Samsung Electronics Co., Ltd.
2. SK hynix Inc.
3. Micron Technology, Inc.
4. Intel Corporation
5. Advanced Micro Devices, Inc.
6. Broadcom Inc.
7. Marvell Technology, Inc.
8. Astera Labs, Inc.
9. Rambus Inc.
10. Montage Technology Co., Ltd.
11. Super Micro Computer, Inc.
12. Dell Technologies Inc.
13. Hewlett Packard Enterprise Company
14. Lenovo Group Limited
15. GigaIO, Inc.
16. MemVerge, Inc.
17. Liqid, Inc.
18. Quanta Computer 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 AI Workload Proliferation and Memory Wall Pressure
  • 4.2.2 Hyperscale Data Center Transition to Composable Infrastructure
  • 4.2.3 Tight Coupling of CXL Ecosystem Support Across CPUs, Memory, and Switches
  • 4.2.4 Rising Demand for Memory Utilization Optimization and Lower TCO
  • 4.2.5 Emerging Need for Rack-Scale Resource Pooling In Multi-Tenant AI Clusters
  • 4.2.6 Faster Qualification of CXL 3.x Platforms for Production Deployment
• 4.3 Market Restraints
  • 4.3.1 Interoperability and Validation Complexity Across Multi-Vendor CXL Stacks
  • 4.3.2 Immature Software Orchestration and Memory Tiering Tooling
  • 4.3.3 High Integration Cost for Rack-Scale Fabric, Switch, and Memory Pooling Hardware
  • 4.3.4 Risk of Delayed Adoption Where HBM and DDR Roadmaps Still Meet Near-Term Needs
• 4.4 Industry Value Chain Analysis
• 4.5 Regulatory Landscape
• 4.6 Technological Outlook
• 4.7 Porter's Five Forces Analysis
  • 4.7.1 Bargaining Power of Buyers
  • 4.7.2 Bargaining Power of Suppliers
  • 4.7.3 Threat of New Entrants
  • 4.7.4 Threat of Substitutes
  • 4.7.5 Intensity of Competitive Rivalry
• 4.8 Impact of Macroeconomic Factors on the Market

5 MARKET SIZE AND GROWTH FORECASTS (VALUE)

• 5.1 By Component
  • 5.1.1 Memory Modules
  • 5.1.2 Switches and Retimers
  • 5.1.3 Controllers and Adapters
  • 5.1.4 Software and Management Platforms
  • 5.1.5 Integration and Support Services
• 5.2 By Memory Technology
  • 5.2.1 DRAM
  • 5.2.2 HBM
  • 5.2.3 Persistent Memory
  • 5.2.4 Tiered Memory (DRAM + NAND)
• 5.3 By Architecture Type
  • 5.3.1 Direct Attached Memory Expansion
  • 5.3.2 Switched Memory Pooling
  • 5.3.3 Rack-Scale Memory Disaggregation
  • 5.3.4 Fabric Attached Memory
• 5.4 By Application
  • 5.4.1 AI Training
  • 5.4.2 AI Inference
  • 5.4.3 High Performance Computing
  • 5.4.4 In-Memory Databases And Analytics
  • 5.4.5 Large Language Model Serving
  • 5.4.6 Enterprise Virtualization
• 5.5 By End User
  • 5.5.1 Hyperscalers
  • 5.5.2 Cloud Service Providers
  • 5.5.3 Enterprise Data Centers
  • 5.5.4 Colocation Providers
  • 5.5.5 Research and Supercomputing Institutions
• 5.6 By Geography
  • 5.6.1 North America
    • 5.6.1.1 United States
    • 5.6.1.2 Canada
    • 5.6.1.3 Mexico
  • 5.6.2 Europe
    • 5.6.2.1 Germany
    • 5.6.2.2 United Kingdom
    • 5.6.2.3 France
    • 5.6.2.4 Italy
    • 5.6.2.5 Rest of Europe
  • 5.6.3 Asia-Pacific
    • 5.6.3.1 China
    • 5.6.3.2 Japan
    • 5.6.3.3 South Korea
    • 5.6.3.4 Taiwan
    • 5.6.3.5 India
    • 5.6.3.6 Rest of Asia-Pacific
  • 5.6.4 South America
  • 5.6.5 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 Samsung Electronics Co., Ltd.
  • 6.4.2 SK hynix Inc.
  • 6.4.3 Micron Technology, Inc.
  • 6.4.4 Intel Corporation
  • 6.4.5 Advanced Micro Devices, Inc.
  • 6.4.6 Broadcom Inc.
  • 6.4.7 Marvell Technology, Inc.
  • 6.4.8 Astera Labs, Inc.
  • 6.4.9 Rambus Inc.
  • 6.4.10 Montage Technology Co., Ltd.
  • 6.4.11 Super Micro Computer, Inc.
  • 6.4.12 Dell Technologies Inc.
  • 6.4.13 Hewlett Packard Enterprise Company
  • 6.4.14 Lenovo Group Limited
  • 6.4.15 GigaIO, Inc.
  • 6.4.16 MemVerge, Inc.
  • 6.4.17 Liqid, Inc.
  • 6.4.18 Quanta Computer Inc.

7 MARKET OPPORTUNITIES AND FUTURE OUTLOOK

• 7.1 White Space and Unmet Need Assessment