AIメモリ市場シェア分析、業界動向と統計、成長予測 2026-2031年

AIメモリ市場の規模は、2025年の217億2,000万米ドルから2026年には279億3,000万米ドルへと拡大し、2026年から2031年にかけて年平均成長率（CAGR）28.61%で推移し、2031年には982億8,000万米ドルに達するとMordor Intelligenceでは予測しています。モデルの学習、推論処理、コンテキスト（文脈情報）の保存といったすべてのプロセスにおいて、より高い帯域幅と大容量のアクティブメモリプールが求められるようになった結果、メモリはAIシステム設計の中核を担う存在となっています。

また、ハイパースケール（超大規模データセンター事業者）によるAIサーバー群の増強計画に加え、自動車、ロボティクス、産業分野などのエッジシステムがデータセンター外での需要の「第二の波」を生み出していることも、市場拡大を後押ししています。現在、製品ロードマップの策定においては、認定（クオリフィケーション）のスピード、パッケージング技術の準備状況、そして単一プラットフォームで複数のメモリ階層をサポートできるかどうかが重要な要素となっています。こうした状況を受け、サプライヤーの戦略は、長期的なプラットフォームへの適合、生産計画の最適化、そしてエコシステム内でのパートナーシップ強化へと重点を移しつつあります。さらに、CXL（Compute Express Link）ベースのメモリ拡張やAIネイティブなストレージメモリといった新たなメモリ層の導入も進んでおり、これらがAIメモリ市場の商圏をさらに拡大させています。

レポートの主なポイント

• メモリ技術別：2025年時点で、HBM（広帯域幅メモリ）がAIメモリ市場の収益の55.60%を占めました。一方、GDDRは2031年までCAGR 29.44%で拡大すると予測されています。
• AI用途別：2025年時点で、AI学習およびモデル開発が収益の45.50%を占めました。一方、AI推論は2031年までCAGR 29.57%を記録し、最も高い成長率を示すと予測されています。
• コンピューティングプラットフォーム別：2025年時点で、AIサーバーがAIメモリ市場の48.52%のシェアを占めました。一方、エッジAIシステムは2031年までCAGR 29.62%で成長すると予測されています。
• 地域別：2025年時点で、北米が38.41%のシェアを占めました。一方、アジア太平洋地域は2031年までCAGR 29.48%で拡大すると予測されています。

AI用途別動向：収益を牽引する「学習」とメモリスタックを再構築する「推論」

2025年時点において、AI学習およびモデル開発はAIメモリ市場の収益の45.50%を占め、最大の用途分野としての地位を維持しました。この状況は、最先端モデルの開発に伴う膨大なメモリ需要を反映したものです。こうした開発では、数千基のアクセラレータが連携して動作し、大規模なアクティブメモリプールを必要とします。一方、AI推論については、AIサービスが「定期的な学習イベント」から「ユーザー向けに常時稼働するワークロード」へと移行するにつれ、2031年まで年平均成長率（CAGR）29.57%という最も速いペースで拡大すると予測されています。この変化は極めて重要です。なぜなら、推論処理は継続的に行われる上、プロンプト、検索サイクル、コンテキストの更新といった各動作に応じて処理規模が拡大する性質があるからです。

Penguin Solutionsは2026年3月、推論ワークロードの構成比について、演算（コンピュート）主導が30%、メモリ主導が70%であると発表しました。これは、導入計画を策定する上で、メモリ帯域幅やアクセス可能な容量がいかに重要な設計要素となっているかを浮き彫りにしています。また、NVIDIAはエージェンティックAI（自律型AI）の推論向けに「CMXコンテキストメモリ・ストレージ」を導入しました。これは、KVキャッシュや関連するモデルの状態（ステート）を、アクティブなワークロードの近傍に保持する必要性が高まっていることを反映しています。AIメモリ業界において、推論は単なる需要の「上乗せ」要因にとどまりません。推論は、導入済みシステム内部のメモリ階層そのものを変革する要素となっているからです。HPC（ハイパフォーマンス・コンピューティング）および科学技術AIは、依然として第3の用途分野としての地位を占めています。これらの分野は、国立研究所、気候モデリング、創薬といったワークロードに支えられ、安定した需要基盤を提供しています。こうしたワークロードでは、最上位のHBM（広帯域メモリ）層への依存度よりも、サーバー全体のメモリバランスの最適化が重視される傾向にあります。

AI Memory Market Analysis by Mordor Intelligence

The AI memory market size is expected to grow from USD 21.72 billion in 2025 to USD 27.93 billion in 2026 and is forecast to reach USD 98.28 billion by 2031 at 28.61% CAGR over 2026-2031. This pace reflects how memory has moved to the center of AI system design as model training, inference serving, and context storage all depend on higher bandwidth and larger active memory pools. The AI memory market is also expanding because hyperscale buyers are planning larger AI server fleets, while edge systems in automotive, robotics, and industrial settings are creating a second wave of demand outside the data center. Product roadmaps are now being shaped by qualification speed, packaging readiness, and the ability to support multiple memory tiers in one platform. That is making supplier strategy more focused on long-term platform alignment, production planning, and ecosystem partnerships. It is also opening room for newer memory layers such as CXL-based expansion and AI-native storage memory, which widen the commercial scope of the AI memory market.

Key Report Takeaways

• By memory technology, HBM held 55.60% revenue share of the AI memory market in 2025, while GDDR is projected to expand at a 29.44% CAGR through 2031.
• By AI application, AI Training and Model Development accounted for 45.50% of revenue in 2025, while AI Inference is expected to record the highest CAGR at 29.57% through 2031.
• By computing platform, AI Servers held 48.52% share of the AI memory market in 2025, while Edge AI Systems are projected to grow at a 29.62% CAGR through 2031.
• By geography, North America held 38.41% share in 2025, while Asia-Pacific is projected to expand at a 29.48% CAGR through 2031.

Note: Market size and forecast figures in this report are generated using Mordor Intelligence’s proprietary estimation framework, updated with the latest available data and insights as of January 2026.

Global AI Memory Market Trends and Insights

Drivers Impact Analysis*

Growing Adoption of HBM3E And HBM4 in AI Accelerators

The move from HBM3E to HBM4 is progressing quickly as accelerator vendors push for more bandwidth and better power efficiency in each new platform generation. NVIDIA stated in May 2026 that the Vera Rubin platform was ramping into full production, which confirms that next-generation memory demand is now tied directly to production deployments rather than early sampling activity.[1] This matters because HBM is no longer a premium option inside leading AI systems and has become a required part of the compute stack. As buyers align hardware roadmaps with more capable accelerators, memory planning starts earlier and stays locked in for longer periods. That shifts procurement from short-cycle component buying toward platform-level commitment. For the AI memory market, this keeps HBM at the center of revenue growth and strategic supplier positioning.

Expansion of Hyperscale AI Server Fleets

Large AI server programs are moving from staged pilots to committed deployment cycles across cloud infrastructure. AMD and Meta Platforms announced in February 2026 an expanded partnership to deploy 6 gigawatts of AMD GPU infrastructure, with shipments for the first-gigawatt phase beginning in the second half of 2026.[2] Each rollout of that scale pulls in HBM, server DRAM, storage memory, and networking memory at the same time. That means growth is not confined to a single memory type and instead spreads across the full stack that supports AI clusters. It also favors suppliers that can offer dependable qualification, stable output, and roadmap continuity. The AI memory market is therefore benefiting from both larger fleet counts and higher memory content inside each deployed system.

Rising AI Training and Inference Compute Density

Training clusters and inference clusters are both carrying larger model state in active memory than earlier AI systems did. NVIDIA launched the BlueField-4 STX modular storage architecture in March 2026 with CMX context memory storage, which shows that memory is now being designed into inference infrastructure as a dedicated layer rather than treated as a secondary component.[3] Penguin Solutions introduced a production-ready CXL-based KV cache server in the same month with up to 11TB of CXL memory for enterprise-scale inference, reinforcing the same architecture shift. These launches show that adding compute alone does not remove throughput limits when long context windows and persistent session state have to stay close to the processor. As model usage becomes more interactive and continuous, memory bandwidth and capacity both rise in importance. That keeps the AI memory market tied closely to the way AI inference systems are being built.

Memory Content Inflation in AI Servers and Racks

New AI racks now carry more memory across the accelerator layer, the server layer, and the storage layer. NVIDIA said the Vera Rubin platform integrates HBM4 with a context memory storage architecture, which points to a broader system design that carries a larger memory footprint than earlier platforms. Penguin Solutions responded to the same pressure with a CXL-based server design that combines DDR5 and CXL add-in cards to expand available memory for inference deployments. These moves show that buyers are no longer sizing memory as a support component that can be adjusted late in the purchase cycle. Memory now shapes system cost, architecture choice, and procurement timing together. That creates a wider revenue base across HBM, server DRAM, LPDDR, and expansion memory inside the AI memory market.

Restraints Impact Analysis*

High Package-Level Thermal and Yield Constraints

Advanced stacked memory is harder to scale than conventional memory because more layers have to perform together inside a tighter thermal envelope. This is especially important in HBM, where finished output depends on packaging quality as much as wafer supply. If packaging performance slips, accelerator deliveries can slow even when end demand stays firm. Validation also takes time because each generation has to clear thermal, reliability, and platform-level requirements before volume shipment. That can narrow the usable ramp window for a new product generation. The AI memory market therefore stays exposed to package-level execution risk even when demand conditions remain strong.

Limited Qualified Supply Base for Advanced HBM

Only a small number of suppliers can support top AI accelerator programs with advanced HBM. Qualification takes time because the memory has to match the controller, package design, thermal profile, and performance targets of each platform. Buyers therefore depend on a narrow supply base when they plan large deployment cycles. That concentration gives qualified vendors more control over allocation and timing. It also raises the cost of a delayed product cycle for both suppliers and customers. The AI memory market will keep carrying this constraint until the qualified supplier base broadens in future HBM generations.

*Our forecasts treat driver/restraint impacts as directional, not additive. The impact forecasts reflect baseline growth, mix effects, and variable interactions.

Segment Analysis

By Memory Technology: HBM Anchors Revenue While GDDR Scales Across Broader AI Deployments

HBM held 55.60% of AI memory market share in 2025, which shows how strongly hyperscale training infrastructure shaped revenue during the year. Its lead came from the fact that leading AI accelerators depend on very high bandwidth memory and cannot sustain expected throughput without it. NVIDIA said in May 2026 that Vera Rubin was ramping into full production, which supports continued HBM demand as next-generation systems move into active deployment. AI Server DRAM remained the second-largest technology layer because large AI systems still need substantial main memory around the accelerator complex.

GDDR is projected to record the fastest AI memory market size growth at 29.44% CAGR through 2031, supported by the commercial rollout of GDDR7. Rambus noted that GDDR7 was standardized by JEDEC in March 2024 and brought a higher-performance path for graphics and accelerator applications. Within the AI memory industry, that gives GDDR a stronger position in GPUs and edge accelerators that need higher bandwidth but do not always require HBM-class packaging. AI Edge Memory LPDDR is also set to expand as inference systems and edge devices need better bandwidth per watt under tight thermal limits. Other memory technology, including CXL-based expansion, adds a longer runway because Penguin Solutions introduced a production-ready CXL-based KV cache server with up to 11TB of memory for enterprise inference in March 2026.

By AI Application: Training Leads Current Revenue While Inference Reshapes the Memory Stack

AI Training and Model Development held 45.50% of revenue in 2025, which kept it as the largest application area in the AI memory market. That position reflected the heavy memory needs of frontier model development, where thousands of accelerators operate together and require large active memory pools. AI Inference is forecast to grow at the fastest pace with a 29.57% CAGR through 2031 as AI services move from periodic training events to constant user-facing workloads. This shift matters because inference runs continuously and scales with every prompt, retrieval cycle, and context update.

Penguin Solutions stated in March 2026 that inference workloads are 30% compute-driven and 70% memory-driven, which underscores why memory bandwidth and accessible capacity are becoming the main design variables in deployment planning. NVIDIA also introduced CMX context memory storage for agentic AI inference, which reflects the growing need to keep KV cache and related model state close to the active workload. In the AI memory industry, this makes inference more than a demand add-on because it changes the memory hierarchy inside the deployed system. HPC and Scientific AI remains the third application block and provides a steadier demand base through national labs, climate modeling, and drug discovery workloads that often rely more on broader server memory balance than on the top HBM tier.

By Computing Platform: AI Servers Dominate Today While Edge AI Systems Expand the Demand Base

AI Servers accounted for 48.52% of revenue in 2025, giving them the largest share among computing platforms in the AI memory market. Their lead came from the fact that rack-scale AI systems absorb HBM, server DRAM, storage memory, and networking memory in the same deployment cycle. AI Compute Accelerators also matter because they define the memory interface requirements that shape demand for each new HBM and DRAM generation. As a result, platform competition in AI hardware directly feeds into memory roadmaps and production planning.

Edge AI Systems are projected to expand at the fastest pace with a 29.62% CAGR through 2031, which widens the reach of the AI memory market beyond centralized data centers. These systems need sustained bandwidth within stricter power and thermal limits, which supports demand for LPDDR and selected GDDR configurations. Across the AI memory industry, that makes the platform mix more diverse because demand now comes from both large AI server clusters and distributed intelligent devices. AMD and Meta announced in February 2026 a plan to deploy 6 gigawatts of AMD GPU infrastructure, which illustrates how single platform commitments can pull forward large memory requirements over the following production cycle. AI Networking Infrastructure and Other Computing Platform categories also gain relevance as larger clusters need memory-aware switching, storage, and context-serving layers to keep inference performance stable.

Complete Report Scope:

• By Memory Technology
  • HBM
  • AI Server DRAM
  • GDDR
  • AI Edge Memory LPDDR
  • Other Memory Technologies (CXL Memory and Other Emerging Memory Types)
• By AI Application
  • AI Training and Model Development
  • AI Inference
  • HPC and Scientific AI
• By Computing Platform
  • AI Servers
  • AI Compute Accelerators
  • AI Networking Infrastructure
  • Edge AI Systems
  • Other Computing Platforms (Specialized AI Computing Platforms and AI Workstations)
• By Geography
  • North America
    • United States
    • Canada
    • Mexico
  • Europe
    • United Kingdom
    • Germany
    • France
    • Italy
    • Rest of Europe
  • Asia-Pacific
    • China
    • Japan
    • South Korea
    • India
    • Rest of Asia-Pacific
  • South America
  • Middle East and Africa

Geography Analysis

North America held 38.41% of AI memory market share in 2025, which made it the largest regional demand center. Its lead came from hyperscaler AI infrastructure buildouts that turned memory into a strategic procurement item instead of a routine server component. The region also benefits from the concentration of major cloud and accelerator platform buyers, which gives it strong pull on qualified supply. This keeps North America central to both near-term allocation decisions and longer-term platform planning in the AI memory market.

Asia-Pacific is projected to grow at a 29.48% CAGR through 2031, making it the fastest-growing region in the AI memory market. The region matters on both the demand side and the production side because it houses the main HBM manufacturing base and much of the supporting packaging ecosystem. South Korea remains the core production center for advanced AI memory, while Japan is strengthening its role as an additional manufacturing node through new investment in HBM capacity. China adds another layer of demand through rising domestic AI model development, even as technology restrictions shape the type of memory infrastructure that can be deployed. India is emerging more as a consumption market during the forecast period, supported by cloud expansion and a growing AI startup base.

Europe and the remaining regions represented a smaller share of the AI memory market in 2025, but their demand pattern is strategically distinct. In Europe, demand is centered on industrial AI, financial services, life sciences, and sovereign computing programs that place a premium on traceability and energy efficiency. South America, the Middle East, and Africa remain earlier-stage markets, yet they are developing additional AI inference demand through sovereign cloud programs and wider regional data center investment. This means the global footprint of the AI memory market is broadening even though revenue concentration remains highest in North America and production concentration remains strongest in Asia-Pacific.

Competitive Landscape

The AI memory market is highly consolidated at the manufacturing tier, especially in HBM, which represented 55.60% of revenue in 2025. The supply of advanced HBM remains limited to a very small group of qualified vendors, which gives that part of the market a much tighter structure than the broader memory category. Competition is therefore shaped less by headline product launches alone and more by qualification speed, packaging readiness, and the ability to stay aligned with leading accelerator roadmaps. Those conditions make scale and execution more important than price-only competition.

Several strategic moves in 2026 showed how vendors are positioning for the next phase of the AI memory market. Micron broke ground on an HBM plant expansion in Hiroshima in July 2026, which supports its long-term place in advanced AI memory supply. Applied Materials and TSMC announced an innovation partnership in May 2026 to accelerate materials engineering and process integration for next-generation AI semiconductor devices. NVIDIA launched BlueField-4 STX and CMX context memory storage in March 2026, which widened the commercial role of memory beyond the accelerator package itself. Penguin Solutions also introduced a production-ready CXL-based KV cache server in March 2026, showing that new entrants can still find room in adjacent memory tiers even when core HBM supply stays concentrated.

The main white space in the AI memory market sits in advanced packaging support, CXL expansion memory, and AI-native storage layers rather than in direct HBM substitution. Standards and interface ecosystems also reinforce incumbent strength because they raise the qualification burden for any new entrant. Rambus remains relevant here because controller IP and memory interface support help determine how quickly new standards move into deployable products. The result is a market where the core manufacturing tier is narrow, while the surrounding ecosystem offers selective entry points for specialized suppliers.

Recent Industry Developments

• July 2026: Micron Technology broke ground on a USD 9.3 billion HBM plant expansion in Hiroshima, Japan, supported by up to 500 billion yen from Japan’s Ministry of Economy, Trade and Industry, with mass production targeted from summer 2028. The facility is dedicated to HBM4 production for AI accelerator platforms and is part of Micron’s broader USD 200 billion U.S. and international manufacturing expansion commitment.
• June 2026: NVIDIA CEO Jensen Huang confirmed at GTC Taipei that Samsung Electronics, SK hynix, and Micron Technology had all qualified as HBM4 suppliers for the Vera Rubin platform, the first instance of concurrent triple-vendor qualification for a single AI accelerator generation.
• May 2026: NVIDIA announced that the Vera Rubin platform was ramping into full production at GTC Taipei on May 31, 2026, with production shipments scheduled to begin from the third quarter of 2026. The platform delivers 10x agent throughput at scale compared with the prior Grace Blackwell generation and integrates HBM4 across its accelerator and CMX context memory storage architecture.
• May 2026: Applied Materials and TSMC announced an innovation partnership at Applied Materials’ EPIC Center in Silicon Valley, targeting co-development of materials engineering and process integration technologies for next-generation AI semiconductor devices spanning data center and edge applications.

List of Companies Covered in this Report:

• SK hynix Inc.
• Samsung Electronics Co., Ltd.
• Micron Technology, Inc.
• NVIDIA Corporation
• Advanced Micro Devices, Inc.
• Intel Corporation
• Broadcom Inc.
• Marvell Technology, Inc.
• Taiwan Semiconductor Manufacturing Company Limited
• ASML Holding N.V.
• Applied Materials, Inc.
• Lam Research Corporation
• KLA Corporation
• Tokyo Electron Limited
• Amkor Technology, Inc.
• ASE Technology Holding Co., Ltd.
• JCET Group Co., Ltd.
• Powertech Technology Inc.
• Google LLC
• Amazon.com, Inc.
• Meta Platforms, 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 Growing Adoption of HBM3E and HBM4 in AI Accelerators
4.2.2 Rising AI Training and Inference Compute Density
4.2.3 Expansion of Hyperscale AI Server Fleets
4.2.4 Higher Bandwidth Demand Per Watt in Advanced GPUs and ASICs
4.2.5 Persistent KV Cache Growth in Long-Context Agentic AI Workloads
4.2.6 Memory Content Inflation in AI Servers and Racks
4.3 Market Restraints
4.3.1 High Package-Level Thermal and Yield Constraints
4.3.2 Limited Qualified Supply Base for Advanced HBM
4.3.3 Heavy Dependence on Advanced Packaging Capacity
4.3.4 Rapid Obsolescence Risk Across HBM Generations
4.4 Industry Value Chain Analysis
4.5 Industry Supply Chain Analysis
4.6 Impact of Macroeconomic Factors on the Market
4.7 Regulatory Landscape
4.8 Technological Outlook
4.9 Porter’s Five Forces Analysis
4.9.1 Bargaining Power of Suppliers
4.9.2 Bargaining Power of Buyers
4.9.3 Threat of New Entrants
4.9.4 Threat of Substitutes
4.9.5 Competitive Rivalry

5 MARKET SIZE AND GROWTH FORECASTS (VALUE)
5.1 By Memory Technology
5.1.1 HBM
5.1.2 AI Server DRAM
5.1.3 GDDR
5.1.4 AI Edge Memory LPDDR
5.1.5 Other Memory Technologies (CXL Memory and Other Emerging Memory Types)
5.2 By AI Application
5.2.1 AI Training and Model Development
5.2.2 AI Inference
5.2.3 HPC and Scientific AI
5.3 By Computing Platform
5.3.1 AI Servers
5.3.2 AI Compute Accelerators
5.3.3 AI Networking Infrastructure
5.3.4 Edge AI Systems
5.3.5 Other Computing Platforms (Specialized AI Computing Platforms and AI Workstations)
5.4 By Geography
5.4.1 North America
5.4.1.1 United States
5.4.1.2 Canada
5.4.1.3 Mexico
5.4.2 Europe
5.4.2.1 United Kingdom
5.4.2.2 Germany
5.4.2.3 France
5.4.2.4 Italy
5.4.2.5 Rest of Europe
5.4.3 Asia-Pacific
5.4.3.1 China
5.4.3.2 Japan
5.4.3.3 South Korea
5.4.3.4 India
5.4.3.5 Rest of Asia-Pacific
5.4.4 South America
5.4.5 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 SK hynix Inc.
6.4.2 Samsung Electronics Co., Ltd.
6.4.3 Micron Technology, Inc.
6.4.4 NVIDIA Corporation
6.4.5 Advanced Micro Devices, Inc.
6.4.6 Intel Corporation
6.4.7 Broadcom Inc.
6.4.8 Marvell Technology, Inc.
6.4.9 Taiwan Semiconductor Manufacturing Company Limited
6.4.10 ASML Holding N.V.
6.4.11 Applied Materials, Inc.
6.4.12 Lam Research Corporation
6.4.13 KLA Corporation
6.4.14 Tokyo Electron Limited
6.4.15 Amkor Technology, Inc.
6.4.16 ASE Technology Holding Co., Ltd.
6.4.17 JCET Group Co., Ltd.
6.4.18 Powertech Technology Inc.
6.4.19 Google LLC
6.4.20 Amazon.com, Inc.
6.4.21 Meta Platforms, Inc.

7 MARKET OPPORTUNITIES AND FUTURE OUTLOOK
7.1 White-Space and Unmet-Need Assessment

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