Near Memory Compute (NMC) In-SSD Processor Market to Reach USD 1.28 Billion by 2036 as AI Inference
Author : Semicon Insights Semicon Insights | Published On : 02 Nov 2026
The rapid expansion of artificial intelligence, cloud computing and enterprise data infrastructure is increasing the amount of information that data centers must process and retrieve. As storage workloads become more demanding, conventional architectures that repeatedly transfer large datasets between storage devices, system memory and host processors can create performance and energy-efficiency limitations. Against this backdrop, Near Memory Compute (NMC) in-SSD processors are gaining attention as an emerging approach to bringing selected computing operations closer to stored data.
According to SemiconductorInsight, the global Near Memory Compute (NMC) In-SSD Processor Market stood at USD 211 million in 2026 and is projected to reach USD 1,280 million by 2036, expanding at a CAGR of 19.8% during the 2027–2036 forecast period. North America accounted for approximately 43% of base-year revenue, while Asia-Pacific represents an important manufacturing center because of its NAND flash and SSD production ecosystem. NMC processors integrate processing capabilities within SSD controllers, storage devices or closely connected processing units, allowing selected operations to be performed near the data instead of repeatedly transferring information to the host CPU.
Explore the complete Near Memory Compute (NMC) In-SSD Processor Market analysis: https://semiconductorinsight.com/report/near-memory-compute-nmc-in-ssd-processor-market/
Why Is Near Memory Compute Becoming Important in Data Storage?
Traditional data-storage architectures rely on transferring information from SSDs to system memory and host processors before computational operations can be performed. As datasets become larger and applications require faster processing, this movement of information can consume significant bandwidth, processing resources and energy.
Near-memory compute addresses this challenge by placing selected processing capabilities inside an SSD, within its controller or in an associated storage-processing device. This architecture allows certain operations to take place closer to where information is stored.
Computational storage devices can support functions such as data filtering, compression, encryption, analytics and selected inference operations. By reducing unnecessary data transfers, these systems can potentially improve storage efficiency and reduce the workload placed on host processors.
The technology is particularly relevant to enterprise data centers and hyperscale cloud environments where large datasets must be processed continuously. However, the commercial value depends on demonstrating measurable improvements under real workloads rather than relying solely on theoretical performance advantages.
AI and Machine Learning Inference Are Supporting Market Growth
One of the major trends influencing the Near Memory Compute In-SSD Processor Market is the expansion of AI and machine-learning inference workloads.
Artificial intelligence systems frequently access large datasets, vector indexes, embeddings and contextual information. These operations can create repeated data movement between storage devices and processing systems, increasing the importance of efficient data-access architectures.
SemiconductorInsight identifies AI and machine-learning inference as the largest application category, representing an estimated 36% of 2026 market revenue. Inference workloads can benefit from storage-side filtering and data processing when these operations reduce unnecessary information transfers and host-processing requirements.
Data analytics and business intelligence represent another important application area. Database systems performing large-scale scans and aggregation operations can potentially benefit from executing selected functions closer to storage.
The adoption of computational storage therefore reflects a broader transition toward specialized data-processing architectures designed to improve efficiency across AI infrastructure and enterprise computing environments.
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Computational Storage Devices Lead the Product Category
Computational Storage Devices represent the largest type segment within the Near Memory Compute In-SSD Processor Market, accounting for an estimated 44% of 2026 revenue.
These devices combine NAND storage with fixed-function or programmable processing capabilities within an SSD-oriented product architecture. Their familiar drive form factors can make procurement, qualification and replacement easier for enterprise storage operators compared with more complex standalone computing solutions.
Samsung SmartSSD demonstrates a device-level computational-storage approach, while ScaleFlux offers SSD products emphasizing transparent data reduction and compression.
SSD controller-embedded processors represent another important product category, accounting for approximately 31% of 2026 revenue. These processors integrate programmable computing resources into the SSD controller data path to support selected operations such as compression, encryption and data placement.
Near-storage processing units represent approximately 16% of the market, while NAND flash-integrated compute engines account for approximately 9%. These technologies provide alternative approaches to positioning computation closer to stored information, although their commercial adoption depends on integration complexity, software compatibility and demonstrated performance benefits.
Data Analytics and Database Processing Create Additional Opportunities
The market extends beyond AI inference into several enterprise computing applications.
Data analytics and business intelligence represent an important demand category because organizations increasingly process large volumes of stored information. Moving selected filtering and aggregation operations closer to storage can reduce the amount of information transferred through host systems.
Database query processing provides another application opportunity. Near-storage processing can support selected scans, filters and data-placement operations, although database integration and transaction consistency remain important considerations.
Data compression and encryption also create commercial opportunities. Transparent processing functions can potentially reduce write amplification, data movement and host CPU usage without requiring major changes to existing application workflows.
Other applications include genomic data preprocessing, telemetry reduction, media processing and selected scientific computing workloads. These specialized uses depend on whether the processing requirements can be supported within the available drive hardware and software architecture.
SNIA and NVMe Standards Are Supporting Technology Development
Interoperability is an important factor influencing the commercial development of near-memory compute solutions.
Different computational-storage products require software interfaces that allow applications and operating systems to discover, manage and execute supported processing functions. Without standardized interfaces, enterprise customers may face integration difficulties when deploying products from different manufacturers.
SemiconductorInsight highlights the development of SNIA computational-storage specifications as an important factor supporting market adoption. The Computational Storage API version 1.1 was published in May 2025, while the Architecture and Programming Model version 1.2 was published in April 2026.
NVM Express also released NVMe 2.4 specifications in August 2026, including the Computational Programs Command Set. These developments provide a stronger foundation for device discovery, processing execution and interoperability.
However, standards maturity does not automatically guarantee commercial adoption. Enterprise operators still need to evaluate application compatibility, security isolation, device monitoring, firmware recovery and measurable improvements in operating costs.
Data Movement Reduction Is a Major Market Driver
One of the principal advantages of near-memory compute is its potential to reduce unnecessary data movement.
When applications repeatedly transfer large datasets between storage devices and host processors, the resulting workload can increase CPU utilization, system power consumption and data-transfer requirements.
By performing selected operations closer to storage, NMC processors can reduce the amount of information that must be transferred through the broader computing architecture.
The benefits depend on application characteristics. Filtering and compression may provide relatively straightforward opportunities, while programmable analytics and AI inference require more advanced software integration.
For enterprise buyers, the main consideration is whether the reduction in data movement translates into measurable improvements in throughput, energy efficiency, latency or total cost of ownership.
Software Integration and Security Remain Key Challenges
Despite its potential advantages, the Near Memory Compute In-SSD Processor Market faces several technical and commercial challenges.
Software portability is an important restraint because applications must be able to use the processing capabilities exposed by the storage device. Different programming models and controller architectures can create compatibility problems.
Workload variability also affects adoption. A function that performs effectively during a database scan may provide limited benefits for random transactional workloads or data that cannot be compressed efficiently.
Data security and execution isolation represent additional considerations. Enterprise operators must ensure that processing inside storage devices does not compromise data integrity, access controls or system reliability.
Thermal constraints, firmware management, application recovery and long-term product support also influence purchasing decisions. Customers require predictable device behavior and reliable maintenance procedures before deploying specialized SSD processors across large storage fleets.
The report identifies software portability, workload variability, thermal constraints and data security among the principal deployment barriers.
Where Are the Biggest Opportunities Emerging?
AI and machine-learning inference represents one of the most important opportunities for near-memory compute manufacturers. The growth of vector databases, retrieval-augmented generation and AI context storage is creating new workloads that repeatedly access large amounts of stored information.
Enterprise analytics provides another opportunity because data filtering, aggregation and selected database operations can benefit from processing near storage.
Cloud service providers and hyperscale operators represent important potential customers because they operate large storage fleets and continuously evaluate methods to reduce infrastructure costs.
Telecommunications and network operators also provide opportunities through distributed storage and data-processing requirements. Research institutions and academic organizations can contribute through experimentation with specialized computational-storage architectures.
The expansion of interoperable software standards and improved SSD controller capabilities could further support commercialization as vendors demonstrate repeatable performance advantages across enterprise workloads.
Near Memory Compute (NMC) In-SSD Processor Market Segmentation
The report evaluates the market across processor architecture, application, end-user category, interface standard and deployment mode.
By type, the market is divided into:
NAND Flash-Integrated Compute Engines
Near-Storage Processing Units
Computational Storage Devices
SSD Controller-Embedded Processors
By application, the market covers:
Data Analytics and Business Intelligence
AI and Machine Learning Inference
Database Query Processing
Data Compression and Encryption
Others
By end user, the market includes:
Cloud Service Providers and Hyperscalers
Enterprise Data Centers
Telecommunications and Network Operators
Research and Academic Institutions
By interface standard, the market covers:
NVMe-Based NMC Processors
PCIe-Attached Computational Storage
SATA-Based Near-Storage Compute
By deployment mode, the market includes:
On-Premises Data Center
Cloud-Native
Edge and Distributed
This segmentation highlights the different approaches to integrating computing capabilities with storage devices. Computational storage devices currently represent the largest product category, while AI inference is the leading application area.
View the complete Near Memory Compute (NMC) In-SSD Processor Market segmentation and forecast: https://semiconductorinsight.com/report/near-memory-compute-nmc-in-ssd-processor-market/
Regional Near Memory Compute (NMC) In-SSD Processor Market Outlook
North America represents the largest regional market, accounting for an estimated 43% of 2026 revenue. The region benefits from its concentration of hyperscale cloud operators, enterprise data centers, storage-software companies and SSD controller development capabilities.
The United States represents the primary regional market because of its established cloud infrastructure and early computational-storage qualification activity. Canada contributes through AI research, enterprise data services and specialized computing applications, while Mexico is developing opportunities through data-center modernization.
Asia-Pacific represents an important manufacturing and technology market, accounting for approximately 32% of 2026 revenue. South Korea benefits from its NAND and SSD manufacturing ecosystem, while Japan contributes through flash-memory research and storage technology development. China provides opportunities through its expanding cloud infrastructure and domestic storage supply chain.
Europe accounts for an estimated 16% of 2026 market revenue. Germany represents an important market through industrial analytics and enterprise cloud applications, while regional demand is influenced by data sovereignty, standards compliance and enterprise infrastructure modernization.
The Middle East and Africa and South America represent smaller regional opportunities. Their development is associated with selective enterprise deployments, cloud infrastructure investment and specialized data-processing requirements.
Competitive Landscape and Key Companies
The Near Memory Compute In-SSD Processor Market includes semiconductor manufacturers, SSD technology developers and companies working on computational-storage hardware and software architectures.
The report discusses technology and ecosystem participants including Samsung, ScaleFlux, Solidigm, Kioxia, Broadcom and NVIDIA in the context of storage processing, controller technologies and related data-center architectures.
Samsung SmartSSD demonstrates programmable computation within an SSD architecture, while ScaleFlux focuses on computational-storage products and transparent data reduction. Solidigm and Kioxia contribute to the broader enterprise SSD and intelligent-storage ecosystem, while Broadcom participates in the controller supply chain.
NVIDIA's development of specialized data-center storage architectures also illustrates the growing interest in advanced data paths for AI infrastructure, although separate DPU and storage-server solutions should not automatically be classified as in-SSD processor products.
Competition is influenced by processing efficiency, software compatibility, data security, endurance, system integration and the ability to demonstrate measurable improvements against conventional NVMe storage.
What Does the Near Memory Compute (NMC) In-SSD Processor Market Report Cover?
The SemiconductorInsight study provides detailed analysis of market size, historical performance, future revenue forecasts, competitive positioning, technology adoption, application trends and regional developments.
The report examines computational storage devices, SSD controller-embedded processors, near-storage processing units and NAND-integrated compute engines. It also evaluates applications across AI inference, data analytics, database query processing, compression and encryption.
Additional coverage includes enterprise data centers, cloud service providers, telecommunications operators and research institutions. The study also examines NVMe and PCIe-related interfaces, deployment models, interoperability standards and software integration requirements.
Regional coverage includes North America, Asia-Pacific, Europe, the Middle East and Africa, and South America. The report evaluates the technological, commercial and operational factors influencing the adoption of near-memory compute architectures.
Key Questions Answered by the Study
The report helps industry participants evaluate questions including:
What is the current Near Memory Compute In-SSD Processor Market size?
How large will the market become by 2036?
What is the expected CAGR during the 2027–2036 forecast period?
What are the different types of near-memory compute processors?
Why do computational storage devices lead the market?
How is AI and machine-learning inference influencing market growth?
What role does data analytics play in computational storage adoption?
How do SSD controller-embedded processors differ from near-storage processing units?
What role do SNIA and NVMe standards play in interoperability?
What are the principal technical challenges affecting enterprise deployment?
Which regions represent the largest market opportunities?
How are Samsung and ScaleFlux contributing to computational-storage development?
What factors determine the return on investment for enterprise NMC deployments?
Explore the full Near Memory Compute (NMC) In-SSD Processor Market forecast and competitive analysis: https://semiconductorinsight.com/report/near-memory-compute-nmc-in-ssd-processor-market/
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