Compute-in-Memory AI Market Size, Share & Growth Forecast (2026-2031)

The compute-in-memory AI market is expected to grow from approximately USD 0.12 billion in 2026 to USD 1.32 billion by 2031 at a CAGR of 61.5%.

Commercial products include digital SRAM compute-in-memory accelerators, analog in-memory processors, licensed CIM semiconductor IP and emerging compute-enabled memory modules. Buyers seek architectures that perform neural operations close to stored weights, reducing repeated data movement and improving energy efficiency for supported workloads.

The shift toward commercial implementation is visible in d-Matrix’s Corsair platform, which entered full production in June 2026, while TetraMem has validated RRAM-based silicon and Mythic has secured embedded-memory technology for future analog inference processors. The participating technologies differ materially in mathematical precision, software compatibility, manufacturing maturity and customer application, creating a market where supplier qualification remains as important as headline performance-per-watt claims.

Market Overview

AI inference requires repeated access to model weights, and data movement between processors and memory can consume significant power even when the arithmetic itself is efficient. Compute-in-memory addresses this problem by performing matrix operations where weights are stored, using digital SRAM logic, resistive-memory crossbars, embedded flash arrays or charge-domain circuits. Its economic value depends on a complete usable accelerator, including conversion circuits, control logic and supporting software, rather than the memory array alone.

Digital and analog approaches serve different buyers. d-Matrix’s SRAM-based Corsair accelerator is designed for low-latency inference in servers and rack systems, with compute chiplets and a dedicated compiler environment. Analog suppliers such as Mythic, TetraMem, EnCharge AI and Sagence emphasize power savings for dense matrix operations, but must also manage precision, analog-to-digital conversion, device variation and software portability. Not all these technologies are shipping at the same commercial scale.

The memory supply chain adds a third development path. Samsung has demonstrated processing-in-memory technologies and presented LPDDR5X-PIM in its 2026 memory roadmap, while SK hynix has collaborated with TetraMem on resistive-memory computation. These initiatives establish credible technology development, although ordinary HBM shipments, memory roadmap presentations and research prototypes do not themselves represent sales of AI compute-in-memory products.

Market Trends

• Inference Platforms Move from Silicon Demonstrations to Deployed Hardware

Commercial data-center buyers increasingly evaluate specialist inference chips by delivered response latency, repeatable throughput and integration into their server fleet. Memory-local matrix operations are valuable when they accelerate the portions of a language-model inference workload that repeatedly access stored parameters. The relevant purchasing decision covers the complete working platform, including firmware, compilation, networking and application support.

d-Matrix announced that Corsair entered full production in June 2026 and later introduced Squadron configurations for customers seeking multi-rack inference systems. Its separate agreement with Gimlet Labs demonstrates practical deployment alongside GPUs, rather than requiring the entire AI system to be replaced. These steps make actual delivery and supported software a more useful indicator of market maturity than isolated laboratory efficiency results.

• Analog Arrays Target Low-Power Edge Inference

Analog compute-in-memory architectures carry out selected multiply-accumulate operations inside resistive, flash or capacitive memory structures. Edge equipment manufacturers are interested because keeping neural-network weights close to the computation reduces transfers that would otherwise consume power and generate heat. This is attractive for always-on industrial sensing, embedded vision and mobile robotics where cooling and battery capacity are tightly constrained.

TetraMem completed initial silicon validation of its 22 nm MLX200 RRAM device in May 2026, while Mythic selected Microchip’s memBrain embedded-memory technology for its next generation of analog processing units. These are different commercialization stages: validated samples and licensed IP support future adoption but do not have the same revenue certainty as a confirmed production shipment.

• Hybrid GPU and Memory-Compute Deployments Expand the Addressable Base

• Specialist in-memory accelerators are increasingly positioned alongside GPUs, CPUs and conventional server memory rather than as universal replacements. Large language models contain attention, control and matrix-compute stages with different memory-access patterns, so splitting work between accelerator types can improve system efficiency when the software can manage each stage reliably.

d-Matrix announced an NVIDIA NVLink Fusion collaboration in September 2026 for future Raptor products and common rack infrastructure. The announcement supports long-term integration but the Raptor availability timetable remains beyond the 2026 base market. Buyers will need evidence of workload portability, networking overhead and model compatibility before committing to mixed-accelerator systems.

• Software and Manufacturing Readiness Determine Adoption Speed

Compute-in-memory silicon introduces nonstandard compilation, precision and memory-mapping requirements. Even efficient chips can remain limited to demonstrations if developers must rewrite models or cannot obtain a stable supply of parts. Analog designs also require calibration against device variability and conversion overhead, making reproducible system performance a central procurement criterion.

d-Matrix describes established fabrication and packaging partners for Corsair, while TetraMem’s collaboration with SK hynix illustrates the work needed to translate promising resistive-memory research into complete AI systems. Samsung’s memory roadmap shows a potential manufacturing path for processing-in-memory, but large memory-vendor production volumes cannot be attributed to CIM without a qualifying compute-enabled SKU.

Segment Analysis

By Commercial Offering: Data Center Digital CIM Accelerators

Data center digital compute-in-memory accelerator products represent approximately USD 76 million, or 63.3% of the market in 2026, and are projected to reach USD 940 million by 2031. These accelerator cards and closely related compute systems address low-latency AI inference where conventional processing must move large quantities of model parameters between memory and arithmetic units. The segment is supported by d-Matrix Corsair production activity, with procurement shaped by software tools, server compatibility and sustained token performance. Growth will depend on qualified fleet deployments rather than the number of announced hardware designs.

By Compute Architecture: Digital SRAM Compute-in-Memory

Digital SRAM compute-in-memory accounts for an estimated 59% of 2026 revenue, reflecting the commercial position of digital accelerator architectures that place arithmetic in memory-local compute structures. The approach offers more familiar precision and digital design controls than many analog crossbar implementations. Analog non-volatile arrays represent about 23%, targeting efficient matrix operations through flash or resistive cells. Buyers distinguish the two approaches by supported operators, accuracy, calibration demands and the energy consumed by peripheral conversion circuits; the fastest isolated array is not necessarily the most efficient complete accelerator.

By Memory Technology: Embedded SRAM Arrays

Embedded SRAM arrays account for about 59% of estimated 2026 revenue because digital compute-in-memory products have the clearest near-term production path. Embedded flash and SuperFlash represent around 15%, supported by Mythic's nonvolatile analog computation and Microchip's licensing activity. Resistive RAM contributes a smaller share but gains relevance as TetraMem validates multi-level analog arrays. Compute-enabled DRAM and HBM products face demanding qualification and economic hurdles before broader deployment.

By Hardware Delivery Format: PCIe Accelerator Cards

Data-center accelerator cards form an estimated 47% of 2026 market value, providing an upgrade path for operators that want specialized inference acceleration in existing servers. Integrated XPU systems contribute around 17%, while edge chips and modules account for 20%. Cards and integrated platforms must not be summed as separate purchases when a system incorporates the same board. Procurement trends favor formats with available device drivers, compiler support and thermal qualification, particularly because organizations increasingly expect memory-compute devices to work alongside existing GPUs.

By Primary AI Workload: Interactive Generative AI

Interactive generative AI and language-model inference account for around 45% of 2026 revenue and are projected to reach about 61% by 2031. Low-latency generation, coding assistants and voice-enabled services benefit when frequently used model weights remain close to computation. Vision inference represents roughly 25% of the initial market and remains important for embedded cameras and industrial systems. Workload economics vary sharply: token latency and throughput matter in cloud AI, while sustained power consumption and sensor processing often determine embedded-device purchasing decisions.

By Deployment Environment: Hyperscale and Neocloud Data Centers

Hyperscale and neocloud data centers contribute approximately 43% of revenue in 2026, rising to an estimated 57% by 2031 as specialist inference devices move toward larger production clusters. Enterprise and sovereign data centers account for around 23%, reflecting on-premises model serving and controlled deployments. Industrial and automotive edge environments represent a further 19%, supported by vision and sensing requirements. Each installation is allocated once based on the destination of the qualifying CIM hardware, rather than the locations of chip design, fabrication and final customer.

By End-User Industry: Cloud Infrastructure and AI Laboratories

Cloud infrastructure operators and AI laboratories represent about 44% of 2026 market value and are expected to account for 56% by 2031. These buyers can justify investment in specialized silicon when lower latency or better tokens per watt improves the economics of high-volume model serving. Enterprise software providers, manufacturing and robotics companies, and automotive developers are important secondary users. Smaller customers may purchase integrated solutions through OEM channels rather than buy bare chips, making technical support, warranty terms and operating software essential elements of supplier competitiveness.

Market Drivers

• Increasing Cost of Moving Model Weights

Large AI models repeatedly read parameters during inference, creating memory-access and energy costs that conventional processor architectures cannot eliminate through arithmetic improvements alone. Moving selected operations into SRAM, resistive or flash memory arrays can reduce this burden and improve performance per watt for suitable matrix-heavy workloads.

• Growth of Real-Time Interactive AI Inference

Coding assistants, speech interfaces and real-time recommendation systems place growing value on short response times. Digital in-memory accelerators are gaining attention where latency sensitivity supports specialized hardware purchases, especially in heterogeneous server configurations that retain GPUs for other model stages.

• Expanding Edge AI and On-Device Processing

Robotics, industrial sensors and embedded vision devices need local inference under strict space, thermal and power limits. Analog in-memory chips offer a potential route to efficient always-on processing, provided the device can deliver adequate accuracy and survive manufacturing variation.

• Commercial Partnerships and Semiconductor Licensing

Supplier agreements are helping new architectures reach actual systems. d-Matrix is working with production and rack-integration partners, while Microchip has licensed embedded-memory IP to Mythic for analog computing designs. Such partnerships improve design readiness without implying that every announced future chip is already shipping.

• Energy and Infrastructure Constraints in AI Deployment

Data-center operators and device manufacturers face rising power and cooling requirements. Compute-in-memory can reduce the energy spent moving weights, creating an economic incentive to deploy specialized accelerators where end-to-end efficiency is measured and where workload characteristics justify the software integration effort.

Market Restraints

• Limited Commercial Production Across Suppliers

Only a small number of companies have publicly demonstrated production or customer-shipment milestones for AI compute-in-memory platforms. Many analog concepts remain in sampling, customer evaluation or research stages, creating supply risk and a relatively narrow qualified purchasing base.

• Analog Precision and Device Variation

Resistive, flash and charge-domain arrays must manage noise, variation, drift and conversion overhead. These issues can restrict usable accuracy for demanding models and increase the engineering required to reproduce performance across chips and operating temperatures.

• Nonstandard Compiler and Model Support

A purpose-built in-memory accelerator requires software to map supported neural operations to its memory arrays. Missing operator coverage, immature debugging tools or model-conversion work can offset hardware efficiency benefits and slow enterprise qualification.

• Competition from Established GPUs and Inference ASICs

Conventional accelerators offer mature toolchains, established purchasing channels and increasingly efficient memory hierarchies. A compute-in-memory supplier must demonstrate a clear workload-specific advantage after system integration, not only a favorable isolated chip benchmark.

• Fabrication, Packaging and Qualification Complexity

Integrating computational memory with logic, peripheral circuits and high-bandwidth packaging creates manufacturing challenges. Small suppliers must secure foundry capacity, repeatable yields and OEM design approvals, while large memory-vendor roadmaps do not guarantee prompt availability of commercially qualified CIM products.

Regional Outlook

North America

North America is the largest compute-in-memory AI market, estimated at USD 55.2 million in 2026 and projected to reach USD 554.4 million by 2031. The United States combines specialist semiconductor designers, hyperscale cloud buyers and venture-backed AI infrastructure companies, creating an early environment for testing and deploying unconventional inference architectures. d-Matrix, Mythic, EnCharge AI, TetraMem and Sagence contribute to the regional technology base, although their deployment maturity differs.

Demand is expected to be strongest among AI model-serving operators that can evaluate real system performance and integrate devices with established GPU clusters. Design wins will depend on application support, predictable supply and total infrastructure economics. Canadian and Mexican opportunities are smaller, with a larger share of adoption expected through broader North American hardware and enterprise technology supply chains.

Asia Pacific

Asia Pacific is projected to expand from approximately USD 32.4 million in 2026 to USD 448.8 million in 2031, representing the fastest estimated regional growth. Semiconductor design, fabrication and memory development in South Korea, Taiwan, China and Japan support opportunities for both analog and digital CIM components. Samsung and SK hynix have established research and memory-technology programs, while regional OEMs supply potential customers for compact edge inference solutions.

The commercial outlook depends on converting memory demonstrations into supported production products and gaining qualification in industrial, automotive and cloud AI systems. Research partnerships such as SK hynix and TetraMem show technical progress, but adoption will be determined by verified customer shipments and software compatibility rather than the number of published test chips.

Competitive Landscape

The compute-in-memory AI market includes a small group of specialist semiconductor developers and larger memory or embedded-technology companies. d-Matrix is differentiated by a production digital SRAM platform and data-center deployment focus. Mythic addresses analog inference using embedded nonvolatile memory, whereas TetraMem focuses on resistive-memory arrays for low-power AI. EnCharge AI brings charge-domain analog design expertise, and Sagence promotes multilevel nonvolatile in-cell computing. These approaches do not compete on identical precision, model capacity or delivery schedules.

Microchip Technology participates through licensable embedded-memory technology rather than selling the same finished accelerator as its customers. Samsung and SK hynix have influential memory development programs, but their ordinary memory sales should not be confused with compute-enabled product revenue. Competition increasingly depends on manufacturable system designs, supported model operators, available software frameworks and customer qualification. Announced peak efficiency figures should be compared using the same model, precision and system boundary.

Partnerships are becoming central to technology adoption. d-Matrix has combined its accelerator roadmap with existing networking and rack infrastructure, while TetraMem’s work with SK hynix supports memory-array research. Buyers are likely to prefer suppliers that deliver working boards or integrated solutions and can document sustained workload performance, supply commitments and software lifecycle support.

Recent Developments

12 March 2026: d-Matrix and Gimlet Labs announced the use of Corsair accelerators alongside GPUs in Gimlet Cloud to accelerate low-latency agentic AI inference workloads.

17 March 2026: Microchip Technology announced that Mythic had selected its memBrain embedded-memory intellectual property for next-generation analog processing units.

19 May 2026: TetraMem reported successful initial silicon validation of its MLX200 22 nm resistive-memory analog in-memory AI computing system-on-chip.

9 June 2026: d-Matrix announced full production of the Corsair inference platform and plans for volume delivery to priority customers.

4 August 2026: Samsung outlined its future AI memory roadmap at FMS 2026, including LPDDR5X-PIM, while presenting additional memory architecture concepts.

12 October 2026: d-Matrix introduced Squadron multi-rack inference systems and reported priority-customer shipments using its memory-centric accelerator architecture.

Market Outlook

Compute-in-memory AI hardware is expected to advance from a limited set of commercially deployed digital accelerators toward a broader portfolio of analog edge products, embedded-memory IP and compute-enabled memory devices. Data-center adoption will remain closely tied to the economics of low-latency language-model inference, while edge applications will prioritize reliable accuracy and power savings within compact product designs.

Technology maturity will vary substantially across suppliers and memory technologies. Production-capable accelerator companies are positioned to capture near-term orders, while analog RRAM, flash and charge-domain designs have a longer qualification path. As larger customers adopt heterogeneous computing, procurement will focus increasingly on supported software, service commitments and measurable system-level efficiency rather than theoretical reductions in data movement.

North America is expected to maintain the leading revenue position because of concentrated AI infrastructure purchasing and specialist semiconductor development. Asia Pacific is forecast to grow faster through 2031 as chip fabrication, memory technology and edge electronics production provide additional routes to commercialization. Companies that can translate memory-array innovation into reliable production systems are likely to capture the largest opportunities.

Companies Profiled

d-Matrix Corporation

Mythic Inc.

TetraMem Inc.

EnCharge AI, Inc.

Sagence AI, Inc.

Samsung Electronics Co., Ltd.

SK hynix Inc.

Microchip Technology Incorporated

AnalogAI Inc.

Compute-in-Memory AI Market Scope: