AI 추론 GPU용 GDDR7 시장 : 시장 점유율 분석, 업계 동향 및 통계, 성장 예측(2026-2031년)

According to Mordor Intelligence, the GDDR7 for AI inference GPU market size is expected to increase from USD 0.58 billion in 2025 to USD 0.89 billion in 2026 and reach USD 5.03 billion by 2031, growing at a CAGR of 41.4% over 2026-2031.

This report is Segmented by Memory Density (16 Gb, 24 Gb, and 32 Gb and Above), Memory Data Rate (Up To 32 Gbps, and Above 32 Gbps), Application (Data Center AI Inference, Edge AI Inference, Workstation AI, and More), End-User Industry (Cloud and Hyperscale Data Centers, OEM Workstations, Government and Defense, and More), and Geography. The Market Forecasts are Provided in Terms of Value (USD).

Global GDDR7 For AI Inference GPU Market Trends and Insights

AI Inference Throughput Gains on GDDR7 Platforms

The GDDR7 for AI inference GPU market is moving higher because memory bandwidth has become a direct limiter for token generation and response speed in production inference. The JEDEC GDDR7 standard sets initial data rates up to 32 Gbps and defines a roadmap to 48 Gbps, which materially increases data throughput compared to the prior generation. Rambus also noted that GDDR7 can deliver up to 192 GB/s per device, compared with 96 GB/s for GDDR6, which improves throughput without forcing a full shift to more expensive memory architectures. This matters in inference servers because higher bandwidth can reduce the number of memory devices needed to achieve a target performance level, helping lower board complexity and improve cost discipline. The same advantage matters in workstation and appliance formats, where power, thermal limits, and board space are tighter than in large training clusters. As more inference tasks move into commercial systems rather than research environments, the GDDR7 for AI inference GPU market is gaining from this practical balance between speed, cost, and system simplicity.

Power-Efficient Bandwidth Scaling With PAM3 Signaling

The GDDR7 for AI inference GPU market is also being supported by better energy efficiency, which matters as power density limits tighten across data centers and edge systems. Rambus explained that PAM3 signaling carries 50% more data per clock cycle than prior signaling methods, thereby raising effective data rates without an equal increase in clock frequency. Samsung stated that its 24 GB GDDR7 used clock control management and a dual-VDD structure, cutting power draw by more than 30% compared to its predecessor. Micron has also positioned GDDR7 as a platform for lower-latency and more power-efficient AI workflows across hybrid CPU, GPU, and NPU systems. This efficiency profile helps the GDDR7 for AI inference GPU market extend beyond mainstream cloud hardware into industrial appliances, telecom edge systems, and on-device AI platforms. It also gives suppliers a stronger case when buyers compare inference economics rather than peak training performance alone.

HBM Preference in Large-Scale AI Training

The largest restraint on the GDDR7 for the AI inference GPU market is the continued preference for HBM in large-scale AI training systems. Training clusters still prioritize the highest possible bandwidth per accelerator, and that makes HBM3e and HBM4 more attractive for the most expensive compute budgets. This limits how far the GDDR7 for AI inference GPU market can penetrate the top tier of hyperscale spending, even when it is well-suited for inference. Buyer familiarity adds another barrier, because procurement teams often apply training-era benchmarks and qualification expectations to inference hardware. That slows adoption in accounts that already standardized around HBM-equipped platforms and vendor stacks. The result is not a collapse in demand, but a ceiling on participation in the most premium training-led portions of the AI hardware cycle.

Other drivers and restraints analyzed in the detailed report include:

1. Rapid Adoption in AI Workstations and Enterprise Appliances
2. GDDR7 Design Wins in Premium AI GPU Segments
3. Limited Leading-Edge DRAM Capacity Amid HBM Competition

For complete list of drivers and restraints, kindly check the Table Of Contents.

Segment Analysis

The 16 GB segment held 63.8% of the GDDR7 for AI inference GPU market size in 2025, which reflected the first wave of Blackwell-based deployments and the wide availability of 16 GB parts across early product launches. This installed base gives 16 GB a durable role because enterprise and cloud refresh cycles do not turn over in a single year. Many buyers are still choosing this tier because it offers a practical balance between throughput, cost, and availability in current platforms. The 32 GB and Above segment is projected to grow at a 44.6% CAGR through 2031, making it the fastest-expanding density band in the GDDR7 for AI inference GPU market. That growth reflects rising demand for larger VRAM pools as inference jobs handle longer context windows, multimodal inputs, and more local model hosting.

The 24 GB segment sits in the middle and plays an important role, raising capacity per channel without requiring a full redesign of the memory subsystem. Samsung said in 2024 that its 24 Gb GDDR7 was built for next-generation AI computing and delivered both higher density and improved power efficiency. That makes 24 GB useful for vendors that need more memory headroom than 16 GB can offer but want a more measured cost step than very high-density configurations. Over time, the GDDR7 for AI inference GPU market is likely to see 16 Gb remain important for volume shipments while 24 Gb and 32 Gb and Above increasingly define the ceiling for premium inference hardware. In practical terms, density is becoming less about specification positioning and more about whether a model can stay resident in local VRAM without pushing data into slower system memory.

The Up to 32 Gbps segment captured 81.1% of the GDDR7 for AI inference GPU market in 2025, showing that the early market favored mature, more readily available speed bins. This tier benefits from broader supplier readiness and a better fit with current board designs, which lowers qualification friction for GPU makers. It also supports mainstream inference use cases that need strong throughput but do not require the most aggressive performance profile. The Above 32 Gbps segment is forecast to expand at a 43.9% CAGR through 2031, reflecting rising demand for larger context handling, real-time multimodal processing, and more demanding visual AI workloads. As system designers push for more performance per board, speed is becoming a stronger point of differentiation inside the GDDR7 for the AI inference GPU market.

The shift to faster tiers is not only a matter of memory silicon, because board materials, routing precision, and thermal design also become more demanding as speeds rise. JEDEC finalized the interoperability framework for GDDR7 in March 2024, which helps vendors scale across speed grades within a common standards structure. That standardization reduces single-supplier dependence and supports a clearer roadmap for future products. Even so, the GDDR7 for AI inference GPU industry will likely keep most near-term shipment volume in the Up to 32 Gbps band while faster bins remain concentrated in premium appliances and high-end accelerator designs. The result is a split structure where mature speed grades support volume growth and higher speed grades shape future performance leadership.

Complete Report Scope:

• By Memory Density
  • 16 Gb
  • 24 Gb
  • 32 Gb and Above
• By Memory Data Rate
  • Up To 32 Gbps
  • Above 32 Gbps
• By Application
  • Data Center AI Inference
  • Edge AI Inference
  • Workstation AI
  • Consumer AI Acceleration
• By End-User Industry
  • Cloud and Hyperscale Data Centers
  • Enterprise IT
  • OEM Workstations
  • Government and Defense
  • Other End-user Industries
• By Geography
  • North America
  • Europe
  • Asia-Pacific
    • China
    • Japan
    • South Korea
    • Taiwan
    • Rest of Asia-Pacific
  • Rest of the World

Geography Analysis

North America accounted for 45.9% of the GDDR7 market share for the AI inference GPU market in 2025, making it the largest regional contributor. The region benefits from the concentration of hyperscale cloud operators, AI chip designers, and enterprise hardware buyers in the United States. It also has strong pull-through from platform operators that can quickly commercialize new inference infrastructure. AWS showed that in January 2026, with its EC2 G7e launch, which brought GDDR7-based inference capacity into a broad enterprise cloud offering. North America also shapes the product roadmap because many system-level decisions by GPU architects, cloud companies, and enterprise software stacks begin there.

Europe represents a smaller but stable part of the GDDR7 for AI inference GPU market, supported by enterprise AI adoption, industrial automation, and public sector interest in more controlled compute environments. The region is well-suited to workstation and appliance deployments where privacy, data handling, and local control matter. Defense demand is also becoming more visible, especially in ruggedized and embedded compute formats. Kontron's July 2026 launch of the VX33211 for defense and aerospace AI inference reflects that shift toward mission-ready edge platforms. These factors give Europe a measured growth path rather than a sudden volume surge.

Asia-Pacific is the fastest-growing region, with a 43% CAGR through 2031, and it stands out because it combines production leadership with rising end-user demand. Samsung and SK hynix give the region major supply-side weight, while China, Japan, South Korea, and Taiwan add important demand and integration roles. Reuters reported that NVIDIA's China-focused Blackwell product would use GDDR7 instead of HBM, which shows how policy and regional access conditions are reshaping hardware design in Asia. Micron also positioned GDDR7 for AI PC and hybrid compute workflows in Japan, which points to widening enterprise demand beyond cloud infrastructure alone. Rest of the World remains smaller today, but sovereign AI investment and expanding cloud infrastructure could lift its role later in the forecast period.

1. Samsung Electronics Co., Ltd.
2. SK hynix Inc.
3. Micron Technology, Inc.
4. NVIDIA Corporation
5. Advanced Micro Devices, Inc.
6. Rambus Inc.
7. TSMC
8. Intel Corporation
9. Synopsys, Inc.
10. Cadence Design Systems, 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 AI Inference Throughput Gains over HBM in Cost-Sensitive Deployments
  • 4.2.2 Power-Efficient Bandwidth Scaling with PAM3 Signaling
  • 4.2.3 Rapid Adoption in AI Workstations and Edge Servers
  • 4.2.4 GDDR7 Design Wins in Premium GPU Launch Cycles
  • 4.2.5 Supplier Qualification Progress for 24 Gb and 32 Gb Dies
  • 4.2.6 Localized Inference Demand from Enterprise and Sovereign AI Programs
• 4.3 Market Restraints
  • 4.3.1 HBM Preference in Large-Scale Training and Bandwidth-Hungry Inference
  • 4.3.2 Limited Leading-Edge DRAM Capacity and Allocation Discipline
  • 4.3.3 Thermal and Board-Level Integration Limits in Compact Accelerators
  • 4.3.4 Qualification Friction Across GPU, Memory Controller, and Board Designs
• 4.4 Industry Value Chain Analysis
• 4.5 Regulatory Landscape
• 4.6 Technological Outlook
• 4.7 Porter's Five Forces Analysis
  • 4.7.1 Bargaining Power of Suppliers
  • 4.7.2 Bargaining Power of Buyers
  • 4.7.3 Threat of New Entrants
  • 4.7.4 Threat of Substitutes
  • 4.7.5 Intensity of Competitive Rivalry
• 4.8 Impact of Macroeconomic Factors on the Market

5 MARKET SIZE AND GROWTH FORECASTS (VALUE)

• 5.1 By Memory Density
  • 5.1.1 16 Gb
  • 5.1.2 24 Gb
  • 5.1.3 32 Gb and Above
• 5.2 By Memory Data Rate
  • 5.2.1 Up To 32 Gbps
  • 5.2.2 Above 32 Gbps
• 5.3 By Application
  • 5.3.1 Data Center AI Inference
  • 5.3.2 Edge AI Inference
  • 5.3.3 Workstation AI
  • 5.3.4 Consumer AI Acceleration
• 5.4 By End-User Industry
  • 5.4.1 Cloud and Hyperscale Data Centers
  • 5.4.2 Enterprise IT
  • 5.4.3 OEM Workstations
  • 5.4.4 Government and Defense
  • 5.4.5 Other End-user Industries
• 5.5 By Geography
  • 5.5.1 North America
  • 5.5.2 Europe
  • 5.5.3 Asia-Pacific
    • 5.5.3.1 China
    • 5.5.3.2 Japan
    • 5.5.3.3 South Korea
    • 5.5.3.4 Taiwan
    • 5.5.3.5 Rest of Asia-Pacific
  • 5.5.4 Rest of the World

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 Rambus Inc.
  • 6.4.7 TSMC
  • 6.4.8 Intel Corporation
  • 6.4.9 Synopsys, Inc.
  • 6.4.10 Cadence Design Systems, Inc.

7 MARKET OPPORTUNITIES AND FUTURE OUTLOOK

• 7.1 White-Space And Unmet-Need Assessment