GPUメモリ市場シェア分析、業界動向と統計、成長予測 2026-2031年

GPUメモリ市場の規模は、2025年の101億8,000万米ドルから2026年には124億米ドルへと拡大し、2031年には321億5,000万米ドルに達するとMordor Intelligenceでは予測しています（2026年～2031年の年平均成長率：20.90%）。GPUメモリ市場は構造的な需要に支えられています。これは、GPUの世代が新しくなるたびに、チップあたりのメモリ容量と帯域幅に対する要求が高まるためです。この傾向により、GPUメモリ市場は単なるGPUの出荷台数の伸びを上回るペースで拡大しています。ボードやラックのレベルにおいて、アクセラレータ1基あたりのメモリ搭載量が増加し続けているからです。

AIインフラへの投資拡大も購買行動を変化させており、顧客は個々のプロセッサ向けだけでなく、サーバーラックやクラスター全体に必要なメモリを確保するようになっています。現在、パッケージングの生産能力、サプライヤー認定のタイミング、3D積層技術の歩留まりといった要因が、最終需要とほぼ同等の影響をGPUメモリ市場に及ぼしています。そのため、メーカーが新たな生産ラインを増設しているにもかかわらず、供給の逼迫状態が続いています。また、GDDR7の登場により、GPUメモリ市場の対象範囲も拡大しています。GDDR7は、コストやシステム適合性の面でHBMが最適とは言えない推論、可視化、ゲーミング、エッジ展開といった用途にも対応できるためです。

レポートの主なポイント

• メモリタイプ別では、2025年にHBMが42.11%の売上シェアを占めて首位となり、2031年までの年平均成長率（CAGR）も21.52%で拡大すると予測されています。
• メモリ容量別では、2025年に16GB～32GBの区分が30.55%のシェアを占めた一方、64GB超の区分は2031年まで21.46%という最高のCAGRを記録すると見込まれています。
• 用途別では、2025年にAIの学習および推論がGPUメモリ市場シェアの35.55%を占め、エッジAIおよび組み込み型アクセラレーションは2031年まで21.35%のCAGRで成長すると予測されています。
• 地域別では、2025年にアジア太平洋地域が48.34%のシェアを占めてGPUメモリ市場における最大の収益地域となり、同地域の成長率は2031年まで21.56%を記録すると見込まれています。

メモリタイプ別：HBMが売上シェアと成長率で首位を維持

2025年時点で、HBMはGPUメモリ市場シェアの42.11%を占め、最大のメモリタイプとなりました。同市場におけるHBMの規模は、2031年まで年平均成長率（CAGR）21.52%で拡大すると予測されています。HBMが優位性を維持している背景には、大規模なAI学習・推論システムが、高い帯域幅、高密度パッケージング、そしてプラットフォームの世代交代ごとに増大するGPUあたりのメモリ容量を必要としている現状があります。NVIDIAの現在のデータセンター向けロードマップを見ても、要件の増大ペースは明らかであり、Blackwellおよびその次世代システムでは、アクセラレータおよびラックあたりのHBM搭載量が大幅に増加する見通しです。また、Samsung、SK hynix、Micronの3社すべてが2026年のNVIDIA向けHBM4供給の認定を受けたことも確認されており、これはHBMの売上拡大における次なるフェーズを後押しするものです。

HBMの成長傾向は、サプライヤー各社の2026年に向けた積極的なロードマップによっても裏付けられています。SamsungとSK hynixは、12層積層のHBM4Eおよび関連する次世代製品の顧客への導入を進めています。SK hynixは、2026年6月時点でのHBM4Eサンプル出荷においてスタックあたり48GBを達成したと発表しており、これは将来のAIアクセラレータにおけるメモリ容量の大幅な拡大に向けた動きを支えるものです。GPUメモリ市場において、HBMを補完する主要なメモリタイプは依然としてGDDRです。GDDRは、最大帯域幅よりもシステムの経済性が重視される推論、ゲーミング、可視化、ワークステーションといった用途において、低コストでの導入を可能にするためです。RambusとNVIDIAはいずれも、GDDR7が主流製品ラインに移行しつつあることを示しており、これにより市場はHBM主体のサーバークラスター以外の分野でも、広範な第二の成長基盤を確保することになります。他のメモリタイプも依然として重要ではありますが、その役割はより限定的であり、ピーク帯域幅を犠牲にしつつ低コスト化やコストあたりの容量拡大を図るアーキテクチャに関連して採用されています。

GPU Memory Market Analysis by Mordor Intelligence

The GPU memory market size is expected to increase from USD 10.18 billion in 2025 to USD 12.40 billion in 2026 and reach USD 32.15 billion by 2031, growing at a CAGR of 20.90% over 2026-2031. The GPU memory market is moving on a structural demand base because each new GPU generation requires more memory capacity and more bandwidth per chip than the one before it. That pattern means the GPU memory market is expanding faster than GPU unit shipments alone, since memory content per accelerator keeps rising at the board and rack level. AI infrastructure spending is also changing buying behavior, as customers now secure memory for entire server racks and clusters instead of only for individual processors. Packaging capacity, supplier qualification timing, and 3D stacking yields now influence the GPU memory market almost as much as end demand, which keeps supply tight even while manufacturers add new lines. GDDR7 is also widening the addressable scope of the GPU memory market because it supports inference, visualization, gaming, and edge deployments where HBM is not the preferred cost or system fit.

Key Report Takeaways

• By memory type, HBM led with 42.11% revenue share in 2025 and is also projected to expand at a 21.52% CAGR through 2031.
• By memory capacity, the 16 GB to 32 GB band held 30.55% share in 2025, while the Above 64 GB band is expected to record the highest CAGR of 21.46% through 2031.
• By application, AI training and inference accounted for 35.55% share of the GPU memory market in 2025, while edge AI and embedded acceleration is projected to grow at a 21.35% CAGR through 2031.
• By geography, Asia-Pacific held 48.34% share in 2025, making it the leading regional revenue base for the GPU memory market, while the same segment grew at 21.56% 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 GPU Memory Market Trends and Insights

Drivers Impact Analysis*

AI Training Cluster Expansion Increasing HBM Consumption

AI infrastructure buildouts have turned HBM from a premium memory option into one of the main supply gates for the GPU memory market. NVIDIA specified 192 GB of HBM3E for each Blackwell B200 GPU, and the GB300 Blackwell Ultra raises that requirement to 288 GB per GPU, which sharply lifts memory content per accelerator generation.[1] NVIDIA also stated that a single GB200 NVL72 rack carries more than 13.4 TB of HBM3E, which shows why procurement has shifted from chip-level decisions to rack-level commitments. This change means the GPU memory market grows not only because more accelerators are shipped, but also because each deployment now absorbs far more memory than earlier system designs. The near-term effect is tighter supply utilization, since additional wafer starts alone do not solve limits in stacking, packaging, and qualification across the GPU memory market.

NVIDIA Blackwell and Successor Platform Rollouts Increasing Qualification Demand

Platform transitions are now one of the strongest timing factors in the GPU memory market because memory suppliers need to qualify each new generation before large customer volumes begin. Bloomberg reported in June 2026 that Samsung, SK hynix, and Micron all cleared HBM4 certification for NVIDIA Vera Rubin, making qualification the gateway to the next supply cycle. SK hynix then deepened that position with a multi-year technology partnership with NVIDIA covering HBM4, Vera Rubin systems, RTX Spark-powered PCs, and Jetson Thor robotics platforms.[2] These platform rollouts force suppliers to support overlapping roadmaps for HBM3E, HBM4, and next-generation derivatives at the same time, which raises development pressure across the GPU memory market. The practical result is that early certification often matters as much as manufacturing scale, because qualified volume is what customers can deploy without delay.

Cloud Gaming and AI Workstation Upgrades Increasing GDDR7 Adoption

GDDR7 is widening the reach of the GPU memory market by serving deployments where HBM is not necessary or not economical. Rambus noted that JEDEC finalized the GDDR7 standard in early 2024, and by 2025 the three major memory suppliers had moved into mass production paths around the new standard. NVIDIA stated that its RTX PRO 6000 Blackwell Server Edition uses 96 GB of GDDR7 and delivers up to 1.6 TB/s, which shows that workstation and server use cases now form a real demand base beyond gaming. This broadens the GPU memory market across inference, visualization, digital twins, and cloud-based graphics services where memory cost and deployment density matter more than peak bandwidth alone. It also gives manufacturers a second high-value growth track while HBM capacity remains under pressure, which helps balance product mix across the GPU memory market.

Advanced Packaging Capacity Buildout Unlocking New GPU Memory Supply

Advanced packaging expansion is a supply driver for the GPU memory market because HBM cannot be used in AI accelerators without 2.5D integration and interposer-based assembly. Epoch AI found that advanced packaging and HBM, not logic die production, were the main bottlenecks in AI chip output during 2025. That makes packaging capacity a direct lever for GPU memory availability, since more packaged accelerators can only ship when memory stacks and interposer assembly scale together. Korea JoongAng Daily reported that Samsung and SK hynix are both expanding HBM-related production and packaging infrastructure, which strengthens the supply base centered in South Korea. As a result, capacity additions in Taiwan and South Korea do not just support chip output, they also unlock more usable volume for the GPU memory market.

Restraints Impact Analysis*

Qualified Supplier Concentration Limiting Volume Flexibility

Supplier concentration remains one of the clearest constraints on the GPU memory market because only a very small group of manufacturers can deliver qualified HBM at scale. Bloomberg confirmed in 2026 that Samsung, SK hynix, and Micron were the three memory makers certified for NVIDIA’s HBM4 cycle, which underlines how limited the qualified supplier base remains. Seoul Economic Daily reported that global large technology companies even proposed direct funding for SK hynix production lines and equipment, which shows how difficult it is to secure incremental supply through normal purchasing channels. This concentration reduces volume flexibility across the GPU memory market because new customers cannot quickly diversify to alternative sources when demand rises faster than planned capacity. It also means procurement timing is increasingly set by allocation agreements and qualification access, not only by end demand or GPU wafer availability.

Yield Sensitivity In 3D Stacking and TSV Processing

Yield sensitivity in 3D stacking continues to restrain the GPU memory market because HBM manufacturing depends on fine TSV structures, thin dies, and stable bonding results across many layers. Semiconductor Engineering reported that TSV processing operates in a narrow dimensional regime, where manufacturing tolerances differ sharply from conventional planar DRAM production.[3] FSM Wafer explained that HBM4 stack development pushes dies toward 30-micron thickness, which raises wafer warpage risk and makes stress control a major yield variable. KED Global reported that Samsung’s 12-layer HBM3E qualification for NVIDIA took an extended development period before clearance, showing how yield issues can delay commercial entry into a platform cycle. In the GPU memory market, these technical setbacks matter because a defect in any die within a deep stack can affect the value of the full assembly and delay customer deployment plans.

*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 Type: HBM Leads Across Revenue Share and Growth

HBM held 42.11% of the GPU memory market share in 2025, making it the largest memory type, and this part of the GPU memory market size is projected to rise at a 21.52% CAGR through 2031. HBM keeps that lead because large AI training and inference systems now depend on high bandwidth, dense packaging, and rising memory-per-GPU specifications across each platform cycle. NVIDIA’s current data center roadmap already shows how fast requirements are increasing, with Blackwell and successor systems moving to much higher HBM loads per accelerator and per rack. Bloomberg also confirmed that Samsung, SK hynix, and Micron all qualified for NVIDIA’s HBM4 cycle in 2026, which supports the next phase of HBM revenue expansion.

The growth profile of HBM is also reinforced by active supplier roadmaps in 2026, with Samsung and SK hynix moving 12-layer HBM4E and related next-generation products toward customer deployment. SK hynix stated that its June 2026 HBM4E sample shipments reached 48 GB per stack, which supports the move toward much larger memory footprints in future AI accelerators. GDDR remains the main complement to HBM in the GPU memory market because it supports lower-cost inference, gaming, visualization, and workstation deployments where system economics matter more than maximum bandwidth. Rambus and NVIDIA both showed that GDDR7 is now moving into mainstream product lines, which gives the market a broad second growth base outside HBM-heavy server clusters. Other memory types still matter, but their role remains more selective and is tied to architectures that trade peak bandwidth for lower cost or higher capacity per dollar.

By Memory Capacity: The 64 GB And Above Tier Defines Next-Generation Platform Requirements

The 16 GB to 32 GB band held 30.55% share in 2025, which reflects the large installed base of gaming, workstation, and enterprise GPUs already deployed across the GPU memory market. The Above 64 GB band is the fastest-growing capacity tier, and this portion of the GPU memory market size is forecast to expand at a 21.46% CAGR from 2026 to 2031. That shift is closely tied to AI accelerator designs that now require 192 GB to 288 GB of memory per GPU, which moves demand well beyond the capacity bands that defined earlier product cycles. SK hynix stated in June 2026 that its 12-layer HBM4E sample reached 48 GB per stack, and the same release linked the product to future systems that will need very large aggregate memory per processor.

The 8 GB to 16 GB range still supports meaningful unit demand in consumer and mid-range workstation products, but its revenue role is becoming less central as larger cards move into the mainstream. The Up to 8 GB band continues to fade because even entry-level graphics and AI-assisted rendering workloads now require more headroom than earlier gaming generations. The 32 GB to 64 GB tier is gaining ground in professional use cases, and NVIDIA’s RTX PRO 6000 Blackwell Server Edition with 96 GB of GDDR7 shows how workstation-class products are moving into higher-capacity configurations. Across the GPU memory market, the key mix change is no longer just a move to faster memory, it is a move to materially larger memory pools per accelerator.

By Application: Edge AI Fastest-Growing As AI Inference Decentralizes

AI training and inference accounted for 35.55% of revenue in 2025, which made it the largest application in the GPU memory market. Edge AI and embedded acceleration is projected to grow at a 21.35% CAGR through 2031, as inference workloads move closer to industrial, medical, automotive, and robotics end points. This pattern expands the GPU memory market beyond hyperscaler campuses, because more memory-enabled compute is being placed in distributed systems that need local processing and low latency. It also supports a wider product mix, since edge deployments often prefer compact GDDR-based designs while centralized training systems continue to favor HBM-heavy architectures.

Cloud GPU and data center acceleration remains the second major demand block because large service providers keep scaling training clusters and inference services around the latest GPU platforms. High-performance computing continues to provide a stable base for the GPU memory market, especially in simulation, weather modeling, and research workloads that value both memory bandwidth and capacity. Gaming and consumer graphics still contribute high unit volumes, but average memory content per card is far below what data center accelerators consume, which keeps revenue intensity lower for this application mix. Professional visualization and content creation are also expanding as workstation products combine larger GDDR7 pools with AI acceleration features, which lets one hardware class serve rendering, design, and inference use cases.

Complete Report Scope:

• By Memory Type
  • HBM
  • GDDR
  • Other GPU Memory Types
• By Memory Capacity
  • Up to 8 GB
  • 8 GB to 16 GB
  • 16 GB to 32 GB
  • 32 GB to 64 GB
  • Above 64 GB
• By Application
  • Professional Visualization and Content Creation
  • AI Training and Inference
  • High-Performance Computing
  • Cloud GPU and Data Center Acceleration
  • Gaming and Consumer Graphics
  • Edge AI and Embedded Acceleration
• By Geography
  • North America
    • United States
    • Canada
    • Mexico
  • Europe
    • Germany
    • United Kingdom
    • France
    • Italy
    • Rest of Europe
  • Asia Pacific
    • China
    • Japan
    • South Korea
    • India
    • Southeast Asia
    • Rest of Asia-Pacific
  • South America
  • Middle East and Africa

Geography Analysis

Asia-Pacific accounted for 48.34% of the GPU memory market share in 2025, which made it the largest regional base for both supply and revenue. The region holds that position because South Korea anchors HBM manufacturing through Samsung and SK hynix, while Taiwan remains central to advanced packaging that is required for HBM deployment in AI accelerators. Korea JoongAng Daily reported that Samsung and SK hynix are scaling HBM-related production, which reinforces Asia-Pacific’s role as the core production center for the GPU memory market. Japan adds depth through semiconductor equipment, testing systems, and memory research support, which strengthens the regional ecosystem around manufacturing continuity. India and Southeast Asia are also becoming more relevant to the GPU memory market as cloud operators expand data center footprints in Singapore, Malaysia, and Indonesia.

North America remains the main demand center in the GPU memory market because the largest AI infrastructure programs are still concentrated among U.S. cloud and platform companies. Mordor Intelligence stated that large-scale training and data center GPU deployments in North America continue to shape hardware procurement patterns, which directly supports memory demand across HBM and GDDR categories. The region also benefits from supply-chain diversification efforts, since U.S.-linked memory capacity is increasingly valued by customers that want a broader geographic sourcing base. This keeps North America important to the GPU memory market even though the heaviest manufacturing concentration remains in Asia-Pacific.

Europe is becoming a more meaningful consumption geography for the GPU memory market as hyperscalers and enterprises add AI-ready capacity under stricter governance and compliance needs. AWS committed EUR 33.7 billion (USD 35.7 billion), to expand GPU capacity in Spain through 2033, and Google announced EUR 5.5 billion (USD 5.83 billion), for GPU and TPU buildout in Hanau and Frankfurt in December 2025. The EU AI Act is also supporting demand for private cloud and on-premise AI infrastructure, which lifts enterprise hardware demand that depends on advanced memory content. South America and Middle East and Africa remain smaller in current scale, but the GPU memory market has room to deepen there as sovereign AI projects and digital infrastructure programs advance through the forecast period.

Competitive Landscape

The GPU memory market remains concentrated at the memory supply layer, where Samsung, SK hynix, and Micron control the qualified HBM pool that supports leading AI accelerators. Bloomberg’s June 2026 report that all three major suppliers cleared HBM4 certification for NVIDIA Vera Rubin shows how platform access is limited to a very small set of approved vendors. This makes competition in the GPU memory market less about broad commodity volume and more about qualification timing, execution quality, and the ability to align roadmaps with major GPU platforms. SK hynix strengthened its position in June 2026 by signing a multi-year technology partnership with NVIDIA that covers HBM4, future AI systems, PCs, and robotics products. That move matters because it ties a leading memory supplier more closely to one of the largest downstream demand engines in the GPU memory market.

Samsung is using a different strategy in the GPU memory market by pairing memory development with broader platform integration and high-volume ramp execution. Seoul Economic Daily and Dong-A Ilbo both showed that Samsung moved early in HBM4 mass production and quickly built commercial momentum in 2026, which supports its attempt to regain ground in advanced memory supply. Digital Today Korea also reported that Samsung signed a memorandum of understanding with AMD for HBM4 supply tied to the Instinct MI455X program, which gives Samsung a strategic socket in the current accelerator cycle. These steps show that competitive positioning in the GPU memory market now depends on customer-specific wins as much as on raw fabrication scale.

A second layer of competition sits around packaging, interfaces, and outsourced assembly because the GPU memory market cannot expand smoothly without those capabilities. Epoch AI showed that advanced packaging was one of the main bottlenecks in AI chip production during 2025, which elevates the strategic role of firms connected to CoWoS and related integration flows. Rambus also has ecosystem influence because standards and signaling requirements affect how quickly next-generation memory can move from design to production. As a result, the GPU memory market is not shaped only by chipmakers, it is also shaped by packaging specialists, interface technology providers, and test and assembly partners that can support deep-stack production at scale. The overall structure still favors a concentrated leadership group, because qualification, packaging, and system integration remain hard to replicate quickly.

Recent Industry Developments

• June 2026: SK hynix shipped 12-layer HBM4E samples to major AI customers, achieving 48 GB capacity, 16 Gbps per pin, and power efficiency improvements exceeding 20% over the prior generation, advancing toward NVIDIA Vera Rubin Ultra deployments expected in 2027. SK hynix applied its Advanced MR-MUF technology to achieve structural stability and a 17% improvement in heat resistance compared with HBM4.
• June 2026: SK hynix and NVIDIA announced a multi-year technology partnership to co-develop next-generation AI memory, covering HBM4 for NVIDIA Vera Rubin, Vera CPUs, RTX Spark-powered PCs, and Jetson Thor robotics platforms. The agreement also applies NVIDIA CUDA-X libraries and NVIDIA PhysicsNeMo to accelerate SK hynix’s semiconductor design and fab operations.
• June 2026: NVIDIA CEO Jensen Huang confirmed at Computex Taipei that Samsung, SK hynix, and Micron all cleared HBM4 certification for NVIDIA Vera Rubin, the first qualification cycle in which all 3 major HBM suppliers passed simultaneously.
• June 2026: Samsung’s HBM4 cumulative revenue surpassed USD 1 billion within 4 months of mass production launch in February 2026, with full-year 2026 revenue projected to approach USD 10 billion as GPU and ASIC-based hyperscaler customers expand procurement. HBM4 shipment volume is expected to increase more than 200% year over year in 2026.

List of Companies Covered in this Report:

• Samsung Electronics Co., Ltd.
• SK hynix Inc.
• Micron Technology, Inc.
• NVIDIA Corporation
• Advanced Micro Devices, Inc.
• Intel Corporation
• Broadcom Inc.
• Marvell Technology, Inc.
• Taiwan Semiconductor Manufacturing Company Limited
• Amkor Technology, Inc.
• ASE Technology Holding Co., Ltd.
• Powertech Technology Inc.
• United Microelectronics Corporation
• Cadence Design Systems, Inc.
• Synopsys, Inc.
• Rambus Inc.
• Applied Materials, Inc.
• GlobalFoundries Inc.
• Chipbond Technology Corporation
• JCET Group Co., Ltd.
• Siliconware Precision Industries Co., Ltd.

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 Impact of Macroeconomic Factors on the Market
4.3 Market Drivers
4.3.1 AI Training Cluster Expansion Increasing HBM Consumption
4.3.2 NVIDIA Blackwell and Successor Platform Rollouts Increasing Qualification Demand
4.3.3 Cloud Gaming and AI Workstation Upgrades Increasing GDDR7 Adoption
4.3.4 Advanced Packaging Capacity Buildout Unlocking New GPU Memory Supply
4.3.5 OEM Preference for Higher Bandwidth Per Watt in Data Center GPUs
4.3.6 Rising Demand for High-Capacity Modules in Inference Accelerators
4.4 Market Restraints
4.4.1 Qualified Supplier Concentration Limiting Volume Flexibility
4.4.2 Yield Sensitivity in 3D Stacking and TSV Processing
4.4.3 Allocation Tension Between HBM and GDDR Wafer Starts
4.4.4 Thermal and Power-Delivery Limits in Compact GPU Platforms
4.5 Industry Value Chain Analysis
4.6 Regulatory Landscape
4.7 Technological Outlook
4.8 Porter’s Five Forces Analysis
4.8.1 Threat of New Entrants
4.8.2 Bargaining Power of Suppliers
4.8.3 Bargaining Power of Buyers
4.8.4 Threat of Substitutes
4.8.5 Competitive Rivalry

5 MARKET SIZE AND GROWTH FORECASTS
5.1 By Memory Type
5.1.1 HBM
5.1.2 GDDR
5.1.3 Other GPU Memory Types
5.2 By Memory Capacity
5.2.1 Up to 8 GB
5.2.2 8 GB to 16 GB
5.2.3 16 GB to 32 GB
5.2.4 32 GB to 64 GB
5.2.5 Above 64 GB
5.3 By Application
5.3.1 Professional Visualization and Content Creation
5.3.2 AI Training and Inference
5.3.3 High-Performance Computing
5.3.4 Cloud GPU and Data Center Acceleration
5.3.5 Gaming and Consumer Graphics
5.3.6 Edge AI and Embedded Acceleration
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 Germany
5.4.2.2 United Kingdom
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 Southeast Asia
5.4.3.6 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 Samsung Electronics Co., Ltd.
6.4.2 SK hynix Inc.
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 Amkor Technology, Inc.
6.4.11 ASE Technology Holding Co., Ltd.
6.4.12 Powertech Technology Inc.
6.4.13 United Microelectronics Corporation
6.4.14 Cadence Design Systems, Inc.
6.4.15 Synopsys, Inc.
6.4.16 Rambus Inc.
6.4.17 Applied Materials, Inc.
6.4.18 GlobalFoundries Inc.
6.4.19 Chipbond Technology Corporation
6.4.20 JCET Group Co., Ltd.
6.4.21 Siliconware Precision Industries Co., Ltd.

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

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