The global Disaggregated Memory Architecture for AI Data Centers Market reached USD 2.4 billion in 2025 and is projected to increase from USD 2.8 billion in 2026 to USD 11.9 billion by 2036, registering a compound annual growth rate (CAGR) of 15.6% from 2026 to 2036, according to Fact.MR.

The rapid expansion of artificial intelligence workloads is placing increasing pressure on data-centre memory infrastructure. AI systems require substantial memory capacity and bandwidth, while data-centre operators are looking for ways to allocate memory resources more efficiently across computing infrastructure.

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Disaggregated memory architecture separates memory resources from individual compute systems, allowing memory to be pooled and allocated according to workload requirements. As AI data centers explore new memory tiers and architectures, demand for disaggregated memory solutions is expected to increase.

Why Is the Disaggregated Memory Architecture Market Growing?

Several factors are supporting market expansion:

• Increasing AI workloads across data centers.
• Growing demand for memory capacity and bandwidth.
• Increasing focus on efficient memory resource utilization.
• Development of new memory tiers for AI infrastructure.
• Rising data-center infrastructure requirements.
• Growing need for scalable computing architectures.

AI workloads can create significant memory requirements that vary according to workload type and processing stage. Traditional architectures may leave memory resources underutilized when compute and memory requirements do not remain balanced.

Disaggregated architectures can enable data-center operators to allocate pooled memory resources more dynamically, potentially improving infrastructure utilization and supporting more flexible AI computing environments.

Key Market Numbers

According to Fact.MR:

• Market value in 2025: USD 2.4 billion
• Market value in 2026: USD 2.8 billion
• Forecast market value by 2036: USD 11.9 billion
• Forecast period: 2026–2036
• CAGR: 15.6%

Market Overview

The Disaggregated Memory Architecture for AI Data Centers Market is emerging as AI infrastructure developers explore alternatives to traditional tightly coupled compute and memory architectures.

In a disaggregated environment, memory resources can be separated from compute nodes and shared or allocated across workloads through high-speed interconnects and supporting infrastructure. This architecture can provide greater flexibility in matching memory resources with changing application requirements.

The development of AI models and increasingly memory-intensive workloads is encouraging data-center operators and technology providers to evaluate new memory tiers and resource-allocation approaches.

Growth Opportunities for Memory and Data Center Technology Providers

Market participants can capitalize on the expanding opportunity through several strategies:

• Developing scalable disaggregated memory architectures.
• Supporting multiple memory tiers for AI workloads.
• Improving high-speed connectivity between compute and memory resources.
• Developing efficient memory pooling technologies.
• Enhancing resource allocation and memory management.
• Supporting integration with AI data-center architectures.
• Improving scalability for large AI infrastructure deployments.

Companies that can provide efficient memory pooling, high-speed data movement, reliable resource allocation, and scalable deployment models are positioned to benefit as AI data centers adopt more flexible memory architectures.

Market Segmentation

By Architecture:
The market includes disaggregated and pooled memory architectures designed to separate memory resources from individual compute nodes.

By Memory Type:
Solutions can incorporate different memory technologies and tiers to address varying AI workload requirements for capacity, bandwidth, latency, and cost.

By Application:
Applications include AI model training, inference, high-performance computing, analytics, and other memory-intensive data-center workloads.

By End User:
Demand is associated with cloud service providers, hyperscale data centers, enterprise data centers, AI infrastructure developers, and high-performance computing operators.

Competitive Landscape

The Disaggregated Memory Architecture for AI Data Centers Market is becoming increasingly competitive as semiconductor companies, memory technology providers, data-center infrastructure developers, cloud service providers, and interconnect technology companies develop solutions for flexible AI memory architectures.

Market participants are focusing on:

• Developing scalable memory pooling architectures.
• Supporting multiple memory tiers for AI workloads.
• Improving high-speed connectivity between compute and memory.
• Enhancing memory resource allocation and utilization.
• Developing technologies for dynamic memory provisioning.
• Supporting integration with AI data-center infrastructure.
• Improving scalability for large AI deployments.

Competition is increasingly influenced by memory bandwidth, latency, scalability, interconnect performance, resource utilization, and integration with existing data-center architectures. Providers capable of helping operators allocate memory resources efficiently across AI workloads are positioned to benefit as disaggregated infrastructure gains adoption.

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Frequently Asked Questions

What was the Disaggregated Memory Architecture for AI Data Centers Market size in 2025?

According to Fact.MR, the global Disaggregated Memory Architecture for AI Data Centers Market reached USD 2.4 billion in 2025.

What will the market be worth by 2036?

The market is projected to reach USD 11.9 billion by 2036.

What is the expected growth rate of the market?

The market is forecast to expand at a 15.6% CAGR from 2026 to 2036.

What was the market value in 2026?

The market is projected to reach USD 2.8 billion in 2026.

What is driving demand for disaggregated memory architecture?

Growth is supported by expanding AI workloads, increasing memory requirements, the need for more efficient memory utilization, development of new memory tiers, and growing investment in AI data-center infrastructure.

Why is disaggregated memory important for AI data centers?

Disaggregated memory separates memory resources from individual compute nodes, allowing memory to be pooled and allocated more flexibly. This can help data-center operators better match memory resources with changing AI workload requirements.

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About Fact.MR

Fact.MR is a global market research and consulting company providing market intelligence, industry forecasts, competitive benchmarking, and strategic insights across semiconductors, artificial intelligence, data-center infrastructure, cloud computing, advanced computing, automotive, healthcare, industrial, and other major sectors. Its research helps manufacturers, suppliers, investors, and business leaders identify emerging opportunities and make informed strategic decisions.