랙 스케일 GPU 시장 : 점유율 분석, 업계 동향 및 통계, 성장 예측(2026-2031년)

According to Mordor Intelligence, the rack-scale GPU market size is projected to expand from USD 6.67 billion in 2025 and USD 9.27 billion in 2026 to USD 40.60 billion by 2031, registering a CAGR of 34.37% between 2026 and 2031.

This report is Segmented by Offering (Hardware, Software, and Services), Rack Density (Up To 16 GPUs, 17-64 GPUs, 65-128 GPUs, and Above 128 GPUs), Cooling Technology (Air Cooled, Liquid Cooled, and Hybrid Cooled), End-User (Cloud Service Providers, Enterprises, Government and Research Institutions, and Telecom and Edge Operators), and Geography. The Market Forecasts are Provided in Terms of Value (USD).

Global Rack-Scale GPU Market Trends and Insights

Rising Hyperscale AI Cluster Density Requirements

The rack-scale GPU market is being driven by hyperscale cloud operators building larger AI clusters that require denser infrastructure in every new deployment cycle. NVIDIA stated that the Vera Rubin NVL72 platform combines 72 Rubin GPUs and 36 Vera CPUs in a single rack-scale system, underscoring how the performance target has already shifted from isolated nodes to integrated fabrics. NVIDIA also showed that GB200 NVL72-class systems reached 132 kW per rack in 2025, and that Vera Rubin-class platforms are moving toward far higher rack densities, making each expansion phase increasingly dependent on purpose-built power and cooling design. As a result, the rack-scale GPU market is drawing higher spending not only for accelerators, but also for rack delivery, liquid loops, optical scale-up, and factory-level validation before systems are installed. Dell confirmed the first shipment of systems built on the NVIDIA Vera Rubin platform to CoreWeave in June 2026, reflecting how hyperscale demand now favors integrated rack delivery over slower component-led installation cycles. The same shift is evident in NVIDIA's description of million-GPU AI factories, where the rack-scale GPU market is being shaped by platform-scale buildouts rather than typical server refresh cycles.

Shift From GPU Nodes To Rack-Scale Fabrics

The rack-scale GPU market is also advancing as AI buyers increasingly want racks that behave like a single logical compute system rather than a collection of separate GPU servers. NVIDIA described Vera Rubin NVL72 as a unified rack platform tied together by a 260 TB/s NVLink 6 fabric, and that architecture directly supports the move toward large shared memory domains for demanding AI workloads. Open Compute Project's work on open cluster designs for AI further shows that infrastructure standards are now being built around high-power cluster layouts, wider rack formats, and rack-native power distribution, rather than legacy node assumptions. That matters for the rack-scale GPU market because procurement is becoming more system-oriented, with buyers evaluating enclosures, networking, liquid cooling, and service readiness as one package. AMD also tied its Helios AI rack design to Meta's Open Compute work, suggesting that this fabric-led direction is not confined to a single supplier ecosystem. The rack-scale GPU market is therefore moving toward platform competition where switching costs, validation cycles, and operational familiarity matter almost as much as raw accelerator performance.

High Upfront Capital Intensity

The rack-scale GPU market still faces a meaningful restraint because full-rack AI systems require a large upfront commitment across hardware, rack integration, cooling equipment, networking, and facility preparation. Vendor announcements themselves show how much value is concentrated in the full system, since buyers are no longer ordering only GPU boards and are instead procuring complete rack-scale environments with supporting infrastructure. That cost profile keeps the rack-scale GPU market tilted toward hyperscalers, sovereign programs, and a small group of well-capitalized cloud specialists. Managed deployment and hosted AI compute can reduce the ownership burden for enterprises, and AMD's 30 MW agreement with Rackspace shows that service-led access models are becoming part of the response. Even so, the rack-scale GPU market remains harder to enter than conventional server markets because each deployment requires a matched investment in both compute and facility capability. This capital profile supports long-term growth but narrows the immediate customer pool.

Other drivers and restraints analyzed in the detailed report include:

1. Liquid-Cooling Readiness in New AI Data Centers
2. Sovereign AI Infrastructure Buildouts
3. Power And Cooling Retrofit Complexity

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

Segment Analysis

Hardware accounted for 62.98% of revenue in 2025, making it the largest offering in the rack-scale GPU market and reflecting the heavy spending required for accelerators, NVLink switches, rack enclosures, power systems, and cooling hardware. That position was consistent with an early build cycle, when many customers were still building new AI capacity and had to purchase the full physical stack before optimization layers became the primary focus of spending. Dell, HPE, Supermicro, Lenovo, and other system builders are commercializing full-rack AI platforms, keeping hardware at the center of buyers' budgets during the current expansion phase. Software remains smaller in revenue share, yet it has become operationally more important as fabrics, cooling controls, and workload orchestration become harder to manage with traditional HPC tools. That means the rack-scale GPU market is no longer defined solely by server hardware, even if hardware still anchors spending.

Services are projected to expand at a 34.96% CAGR through 2031, making it the fastest-growing offering and showing how quickly operating complexity is moving beyond the comfort level of many buyers. CoreWeave's June 2026 bring-up of NVIDIA Vera Rubin NVL72 involved liquid-cooled storage, custom software-defined cooling control, and unified rack management, which illustrates the depth of coordination required before a rack enters production use. Dell's Integrated Rack Scalable Systems model also points in the same direction, packaging validation, on-site deployment, and lifecycle support alongside the hardware rather than selling them as optional follow-on work. In the rack-scale GPU industry, this creates a wider role for commissioning, thermal tuning, firmware validation, optical interconnect setup, and security configuration. The rack-scale GPU market is therefore likely to see service revenue rise faster wherever enterprises, second-tier clouds, and public institutions want the capability of rack-native AI without building a full operations team internally.

The 17-64 GPU tier accounted for 39.83% of revenue in 2025, making it the leading density class in the rack-scale GPU market, as it meets a broad set of enterprise AI, regional cloud, and mid-scale sovereign requirements. This tier offered a practical middle ground where buyers could deploy meaningful compute density without immediately moving into the largest and most demanding rack footprints. The segment also matched the needs of organizations that wanted advanced training and inference capacity while still working within more manageable power envelopes and deployment schedules. For that reason, 17-64 GPUs held 39.83% of the rack-scale GPU market share in 2025, and it remained the workhorse bracket for buyers seeking scale without the full complexity of the highest-density formats. The rack-scale GPU market benefited from this segment, as it bridged early enterprise adoption and full hyperscale configurations.

The above-128 GPU tier is projected to expand at a 35.17% CAGR through 2031, which shows where the next wave of scale is heading as larger AI models demand tighter intra-rack communication and lower latency. Supermicro said its Vera Rubin NVL4 DCBBS blueprint can scale to 1,152 NVIDIA Rubin GPUs within a 3.2 MW unit, which demonstrates how suppliers are already designing around extremely dense AI deployment blocks. Dell also introduced the PowerEdge XE8812, which supports up to 144 GPUs per ORv3-standard rack, further confirming that the rack-scale GPU market is moving toward much denser rack classes. The Open Compute Project, which works on open cluster designs, adds a standards layer to this movement by preparing enclosures and power formats for future high-density AI clusters. In the rack-scale GPU industry, the up-to-16 and 65-128 GPU brackets still matter, but the strongest momentum is clearly shifting toward larger rack domains that can reduce communication overhead for frontier workloads.

Complete Report Scope:

• By Offering
  • Hardware
  • Software
  • Services
• By Rack Density
  • Up to 16 GPUs
  • 17-64 GPUs
  • 65-128 GPUs
  • Above 128 GPUs
• By Cooling Technology
  • Air Cooled
  • Liquid Cooled
  • Hybrid Cooled
• By End User
  • Cloud Service Providers
  • Enterprises
  • Government and Research Institutions
  • Telecom and Edge Operators
• 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

North America accounted for 53.34% of the rack-scale GPU market in 2025, making it the leading regional market and reflecting the concentration of hyperscale cloud buyers, AI infrastructure specialists, and early liquid-cooled buildouts. The United States remains the main anchor because the largest platform launches, first system shipments, and many of the most visible AI factory projects are centered there. Dell shipped Vera Rubin-based systems to CoreWeave in June 2026, showing that the rack-scale GPU market in North America still benefits from close vendor-customer coordination and fast commercialization cycles. NVIDIA also invested USD 2 billion in CoreWeave and expanded the partnership to support a 5+ GW AI factory buildout by 2030, underscoring the scale of the infrastructure commitment already underway in the region. The regional lead is therefore not only a matter of current capacity, but also of faster execution across power, rack integration, and ecosystem support.

Europe is growing from a smaller base, but the rack-scale GPU market there is gaining traction through research computing, sovereign AI priorities, and rising interest in efficient high-density infrastructure. NVIDIA's 2026 science systems announcement included the Leibniz Supercomputing Center, demonstrating that the region remains active in deploying advanced rack-native AI and HPC platforms. HPE and Lenovo also positioned their 2026 AI factory and Vera Rubin programs for multi-tenant and large-scale deployments, which supports the view that European buyers are moving toward full-rack platforms rather than incremental node additions. The regional profile suggests steady growth where compute sovereignty, research workloads, and efficiency-oriented facility design come together.

Asia-Pacific is projected to expand at a 35.31% CAGR through 2031, making it the fastest-growing regional segment in the rack-scale GPU market. Japan is already demonstrating stronger deployment readiness through high-density liquid-cooled operations and commercial liquid-cooling services from operators such as IDC Frontier, Vertiv, and Equinix. China is advancing along a distinct domestic path, and Huawei's CloudMatrix384 paper described a 384 NPU rack-scale supernode architecture with unified memory pooling across 16 racks. Huawei also said its Atlas 950 SuperPoD would scale to 8,192 NPUs, which shows how quickly local alternatives are moving toward system-level AI infrastructure. Outside Asia-Pacific, South America remains a smaller market, centered on selective hyperscale colocation demand, while the Middle East and Africa are becoming more visible through sovereign AI buildouts and large AI factory ambitions, even though the installed base remains more concentrated than in North America.

1. Dell Technologies Inc.
2. Hewlett Packard Enterprise Company
3. Super Micro Computer, Inc.
4. Lenovo Group Limited
5. Inspur Group Co., Ltd.
6. NVIDIA Corporation
7. Advanced Micro Devices, Inc.
8. Cisco Systems, Inc.
9. Huawei Technologies Co., Ltd.
10. GIGABYTE Technology Co., Ltd.
11. ASUSTeK Computer Inc.
12. Fujitsu Limited
13. International Business Machines Corporation
14. Intel Corporation
15. Oracle Corporation
16. CoreWeave, Inc.
17. Quanta Computer Inc.
18. Wistron Corporation
19. Wiwynn Corporation
20. Hon Hai Precision Industry 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 Market Drivers
  • 4.2.1 Rising Hyperscale AI Cluster Density Requirements
  • 4.2.2 Shift From GPU Nodes to Rack-Scale Fabrics
  • 4.2.3 Liquid-Cooling Readiness in New AI Data Centers
  • 4.2.4 Sovereign AI Infrastructure Buildouts
  • 4.2.5 Power-Availability Constraints Favoring High-Density Rack Design
  • 4.2.6 Multi-Tenant AI Service Monetization Pressure
• 4.3 Market Restraints
  • 4.3.1 High Upfront Capital Intensity
  • 4.3.2 Power and Cooling Retrofit Complexity
  • 4.3.3 Limited Supply of Advanced Packaging and High-Bandwidth Memory
  • 4.3.4 Rack-Level Standardization Gaps Across OEMs
• 4.4 Impact of Macroeconomic Factors on the Market
• 4.5 Industry Value Chain Analysis
• 4.6 Regulatory Landscape
• 4.7 Technological Outlook
• 4.8 Porter's Five Forces Analysis
  • 4.8.1 Bargaining Power of Buyers
  • 4.8.2 Bargaining Power of Suppliers
  • 4.8.3 Threat of New Entrants
  • 4.8.4 Threat of Substitutes
  • 4.8.5 Intensity of Competitive Rivalry

5 MARKET SIZE AND GROWTH FORECASTS (VALUE)

• 5.1 By Offering
  • 5.1.1 Hardware
  • 5.1.2 Software
  • 5.1.3 Services
• 5.2 By Rack Density
  • 5.2.1 Up to 16 GPUs
  • 5.2.2 17-64 GPUs
  • 5.2.3 65-128 GPUs
  • 5.2.4 Above 128 GPUs
• 5.3 By Cooling Technology
  • 5.3.1 Air Cooled
  • 5.3.2 Liquid Cooled
  • 5.3.3 Hybrid Cooled
• 5.4 By End User
  • 5.4.1 Cloud Service Providers
  • 5.4.2 Enterprises
  • 5.4.3 Government and Research Institutions
  • 5.4.4 Telecom and Edge Operators
• 5.5 By Geography
  • 5.5.1 North America
    • 5.5.1.1 United States
    • 5.5.1.2 Canada
    • 5.5.1.3 Mexico
  • 5.5.2 Europe
    • 5.5.2.1 Germany
    • 5.5.2.2 United Kingdom
    • 5.5.2.3 France
    • 5.5.2.4 Italy
    • 5.5.2.5 Rest of 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 India
    • 5.5.3.5 Southeast Asia
    • 5.5.3.6 Rest of Asia-Pacific
  • 5.5.4 South America
  • 5.5.5 Middle East and Africa

6 COMPETITIVE LANDSCAPE

• 6.1 Market Concentration
• 6.2 Strategic Moves
• 6.3 Market Positioning 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 Dell Technologies Inc.
  • 6.4.2 Hewlett Packard Enterprise Company
  • 6.4.3 Super Micro Computer, Inc.
  • 6.4.4 Lenovo Group Limited
  • 6.4.5 Inspur Group Co., Ltd.
  • 6.4.6 NVIDIA Corporation
  • 6.4.7 Advanced Micro Devices, Inc.
  • 6.4.8 Cisco Systems, Inc.
  • 6.4.9 Huawei Technologies Co., Ltd.
  • 6.4.10 GIGABYTE Technology Co., Ltd.
  • 6.4.11 ASUSTeK Computer Inc.
  • 6.4.12 Fujitsu Limited
  • 6.4.13 International Business Machines Corporation
  • 6.4.14 Intel Corporation
  • 6.4.15 Oracle Corporation
  • 6.4.16 CoreWeave, Inc.
  • 6.4.17 Quanta Computer Inc.
  • 6.4.18 Wistron Corporation
  • 6.4.19 Wiwynn Corporation
  • 6.4.20 Hon Hai Precision Industry Co., Ltd.

7 MARKET OPPORTUNITIES AND FUTURE OUTLOOK

• 7.1 White-Space and Unmet-Need Assessment