Global AI Storage Class Memory Market Size, Share, Trends and Forecasts 2031

Key Findings

• The AI storage class memory market focuses on next-generation non-volatile memory technologies positioned between DRAM and NAND to accelerate AI data access and processing.

• Storage class memory enables low-latency, high-endurance data storage critical for AI training and inference workloads.

• AI-driven data growth is increasing demand for memory architectures that reduce bottlenecks between compute and storage.

• SCM technologies support persistent memory use cases in data centers, edge AI systems, and high-performance computing.

• Performance-per-watt advantages are becoming a key differentiator for AI memory adoption.

• Integration of SCM with CPUs, GPUs, and AI accelerators is expanding system-level value.

• Data center operators are exploring SCM to optimize total cost of ownership.

• Emerging AI workloads require higher memory bandwidth and faster data persistence.

• Software ecosystem maturity remains critical for broad SCM adoption.

• The market is characterized by rapid innovation and evolving standards.

AI Storage Class Memory Market Size and Forecast

The global AI storage class memory market was valued at USD 5.4 billion in 2025 and is projected to reach USD 18.6 billion by 2031, growing at a CAGR of 23.1%. Growth is driven by exponential increases in AI training data volumes and latency-sensitive inference workloads. Storage class memory bridges the performance gap between DRAM and NAND, enabling faster data access. Adoption is strongest in hyperscale data centers and AI-focused HPC systems. Cost optimization and endurance advantages support long-term scalability. Market expansion is reinforced by AI infrastructure investments.

Market Overview

AI storage class memory refers to high-speed, non-volatile memory technologies designed to provide near-DRAM performance with persistent storage characteristics. These memories reduce data movement between storage and compute, improving AI workload efficiency.

SCM supports use cases such as in-memory databases, model checkpointing, and fast restart of AI pipelines. Compared to traditional storage, SCM offers lower latency and higher endurance. Integration into AI systems enhances throughput and energy efficiency. The market serves cloud providers, enterprises, and edge AI deployments seeking performance optimization.

AI Storage Class Memory Value Chain & Margin Distribution

AI Storage Class Memory Market By Technology Type

AI Storage Class Memory – Adoption Readiness & Risk Matrix

Future Outlook

The AI storage class memory market is expected to grow rapidly as AI workloads become increasingly data intensive. Future developments will focus on improving cost efficiency and endurance while scaling capacity. Closer integration with AI accelerators and CPUs will enhance system performance. Software frameworks will mature to fully exploit SCM capabilities. Edge AI and real-time inference will create additional demand. Long-term growth is tied to architectural shifts toward memory-centric computing.

AI Storage Class Memory Market Trends

• Growing Adoption Of Memory-Centric AI Architectures
  AI systems are shifting toward architectures that minimize data movement. SCM enables persistent memory close to compute. This reduces latency and energy consumption. Memory-centric designs improve AI training efficiency. Adoption is increasing in HPC environments. This trend reshapes system architecture decisions.

• Rising Use Of Persistent Memory In AI Data Pipelines
  Persistent memory accelerates checkpointing and recovery. AI workflows benefit from fast restart capabilities. SCM reduces downtime during training interruptions. Data persistence improves pipeline resilience. Adoption improves operational efficiency. This trend supports enterprise AI workloads.

• Integration Of SCM With AI Accelerators
  SCM is increasingly integrated alongside GPUs and AI accelerators. Tight coupling reduces memory bottlenecks. Accelerator utilization improves significantly. System throughput increases for inference workloads. Integration complexity rises. This trend drives co-design efforts.

• Expansion Of SCM In Edge AI Applications
  Edge AI requires fast and power-efficient memory. SCM supports real-time data processing. Persistence enhances reliability in disconnected environments. Energy efficiency is critical for edge deployments. Adoption is growing in industrial and automotive AI. This trend expands market scope.

• Advancements In SCM Controller And Interface Technologies
  Controller innovation reduces latency and improves endurance. Advanced interfaces enhance compatibility with existing systems. Performance tuning increases adoption confidence. Standardization efforts are progressing. Controller IP becomes a differentiator. This trend strengthens ecosystem maturity.

Market Growth Drivers

• Explosive Growth Of AI Training And Inference Data
  AI models increasingly rely on massive, continuously growing datasets. Data movement between storage and compute creates latency bottlenecks. SCM enables faster access to frequently used data. Reduced latency improves model training throughput. Inference workloads benefit from deterministic response times. Persistent data access improves pipeline stability. Memory proximity reduces system overhead. Data growth structurally drives SCM adoption.

• Need To Reduce Memory And Storage Bottlenecks
  Traditional memory hierarchies struggle to scale with AI workloads. DRAM capacity limits and NAND latency create inefficiencies. SCM bridges this performance gap effectively. Reduced I/O overhead accelerates end-to-end processing. System utilization improves significantly. Bottleneck reduction enhances hardware ROI. Architectural redesign favors SCM integration. This need fuels sustained market growth.

• Demand For Energy-Efficient AI Infrastructure
  Power consumption is a dominant constraint in AI data centers. SCM offers lower energy per operation than conventional storage. Reduced power draw improves operating margins. Energy-efficient memory supports sustainability goals. Hyperscalers prioritize efficiency improvements. Thermal load reduction improves system density. Energy constraints accelerate SCM adoption. Efficiency remains a critical driver.

• Rising Investment In AI Data Center Infrastructure
  Global investment in AI-focused data centers is accelerating. Memory performance directly impacts AI infrastructure ROI. SCM aligns with next-generation data center architectures. Capital spending supports advanced memory deployments. Infrastructure scaling increases addressable demand. Cloud providers experiment with SCM tiers. AI infrastructure growth sustains long-term demand. Investment momentum drives market expansion.

• Emergence Of Real-Time And Edge AI Use Cases
  Real-time AI requires predictable, ultra-low-latency data access. SCM supports deterministic memory behavior. Edge systems benefit from non-volatility and fast startup. Mission-critical applications demand reliability. Edge AI expands deployment environments. New use cases broaden market scope. Persistent memory improves resilience. Edge adoption diversifies demand.

Challenges in the Market

• High Cost Relative To Conventional Memory Technologies
  SCM remains more expensive than DRAM and NAND on a per-bit basis. Cost sensitivity limits widespread deployment. Hyperscalers evaluate ROI cautiously. Economies of scale are still emerging. Manufacturing yields affect pricing. Cost parity has not yet been achieved. Pricing pressure slows volume adoption. Cost remains a major barrier.

• Limited Software And Application Ecosystem Support
  SCM requires software optimization to unlock full benefits. Legacy applications may not be SCM-aware. Developer familiarity remains limited. Integration increases deployment complexity. Software tooling is still maturing. Ecosystem fragmentation affects portability. Adoption depends on middleware support. Software readiness constrains growth.

• Technology Maturity And Reliability Concerns
  Some SCM technologies face endurance and retention challenges. Long-term reliability data is limited. Qualification cycles are extended for enterprise use. Risk perception affects purchasing decisions. Reliability varies across technology types. Early failures impact confidence. Conservative buyers delay adoption. Maturity concerns limit scaling.

• Standardization And Interoperability Challenges
  Interface and protocol fragmentation complicates system integration. Lack of universal standards increases vendor lock-in risk. Interoperability testing adds cost. Platform compatibility varies across systems. Standard adoption is still evolving. Fragmentation slows ecosystem growth. Buyers hesitate without clarity. Standards gaps remain a challenge.

• Supply Chain And Manufacturing Scalability Issues
  Novel materials complicate fabrication processes. Yield variability impacts supply consistency. Scaling production is capital intensive. Manufacturing learning curves are steep. Supply volatility affects pricing. Capacity expansion requires long lead times. Consistency issues limit adoption confidence. Scalability remains uncertain.

AI Storage Class Memory Market Segmentation

By Technology Type

• Phase Change Memory

• Resistive RAM

• Magnetoresistive RAM

• Ferroelectric RAM

• Hybrid Storage Class Memory

By Application

• AI Training Systems

• AI Inference Engines

• In-Memory Databases

• Edge AI Platforms

By End User

• Hyperscale Data Centers

• Enterprises

• Research Institutions

By Region

• North America

• Europe

• Asia-Pacific

Leading Key Players

• Intel Corporation

• Samsung Electronics Co., Ltd.

• Micron Technology, Inc.

• SK hynix Inc.

• Western Digital Corporation

• Kioxia Holdings Corporation

• IBM Corporation

• Toshiba Corporation

• Everspin Technologies, Inc.

• Crossbar Inc.

Recent Developments

• Samsung Electronics advanced next-generation SCM prototypes targeting AI workloads.

• Micron Technology expanded persistent memory solutions for data center AI applications.

• Intel enhanced software support for AI persistent memory use cases.

• IBM demonstrated SCM-enabled memory-centric computing architectures.

• SK hynix invested in advanced non-volatile memory R&D for AI systems.

This Market Report Will Answer the Following Questions

• What is the projected size of the AI storage class memory market through 2031?

• Which SCM technologies best support AI workloads?

• How does SCM reduce AI memory bottlenecks?

• What role does software ecosystem maturity play?

• Which regions are leading SCM adoption?

• How does energy efficiency influence buying decisions?

• What challenges limit large-scale deployment?

• Who are the leading SCM vendors?

• How does SCM integrate with AI accelerators?

• What innovations will shape the SCM market?