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

According to Mordor Intelligence, the rack-Scale GPU infrastructure market size is projected to expand from USD 6.82 billion in 2025 and USD 9.58 billion in 2026 to USD 41.27 billion by 2031, registering a CAGR of 33.92% between 2026 and 2031.

This report is Segmented by Solution Type (Rack-Scale Compute Systems, Rack-Scale Networking Systems, Rack-Scale Cooling Systems, and More), Deployment Scale (Single-Rack Deployments, and More), Cooling Architecture (Immersion-Cooled Rack Infrastructure, and More), End User (Government and Research Institutions, and More), and Geography. The Market Forecasts are Provided in Terms of Value (USD).

Global Rack-Scale GPU Infrastructure Market Trends and Insights

Surging Hyperscaler Demand for AI Factory Racks

The rack-scale GPU infrastructure market is being led by hyperscaler capital plans that reached approximately USD 725 billion for 2026, up from USD 410 billion in 2025, with server and rack infrastructure absorbing an estimated USD 130 billion. Procurement has shifted from buying isolated servers to buying integrated rack-scale systems that combine compute trays, interconnect fabrics, liquid cooling, and power delivery into a single deployment package. That change increases revenue per installation because each platform cycle now carries more hardware and site infrastructure. Allocation queues for next-generation systems are also pushing some operators to secure current-generation capacity earlier, which supports near-term order flow. This keeps the rack-scale GPU infrastructure market tied not only to demand growth, but also to platform timing and facility readiness.

Rapid Shift to Liquid-Cooled High-Density Infrastructure

The rack-scale GPU infrastructure market is moving quickly toward liquid-cooled designs because Vera Rubin entered full production in May 2026 with a fully liquid-cooled, fanless rack architecture. Rubin rack configurations draw 300 kW or more, which means operators now need coolant distribution units, facility manifolds, and dedicated piping, in addition to the rack purchase. Open Rack Wide also shows that rack geometry, power, and cooling are being standardized together across dense AI environments. This changes buying behavior because cooling vendors now need qualification much earlier in the project cycle than in traditional data center builds. It also broadens the addressable spend in the rack-scale GPU infrastructure market because cooling and power are no longer secondary add-ons to the compute order.

Advanced Packaging and HBM Supply Bottlenecks

The rack-scale GPU infrastructure market remains limited by supply at the packaging and memory layer, especially in CoWoS capacity and high-bandwidth memory output. These constraints cap the number of accelerators that can be assembled into finished racks, even when end demand exceeds available supply. Tight availability also increases cost pressure on system vendors, as packaging inflation moves faster than many procurement cycles. That weakens margin flexibility for OEMs and integrators that are trying to scale large AI factory deployments on fixed customer timelines. In the rack-scale GPU infrastructure market, this means demand is strong, but shipment timing still depends on upstream memory and packaging availability.

Other drivers and restraints analyzed in the detailed report include:

1. Expanding Sovereign AI And National Compute Programs
2. Rack-Level Interconnect Standardization and Disaggregation
3. Grid Capacity and Site-Readiness Constraints

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

Segment Analysis

Rack-Scale Compute Systems accounted for 70.34% of total revenues in 2025, making it the largest segment of the rack-scale GPU infrastructure market. This lead reflects the continuing weight of GPU and accelerator trays in the total bill of materials for every rack deployment. Platform transitions from Hopper to Blackwell and now to Rubin have kept compute spending elevated because operators are replacing full rack systems rather than upgrading parts one server at a time. Rack-Scale Networking Systems remain the second-largest layer as buyers move toward high-radix Ethernet fabrics and optical interconnects that support larger AI clusters.

Rack-Scale Cooling Systems are projected to grow at a 35.52% CAGR from 2026 to 2031, the fastest pace among all solution types. That growth follows the move toward fully liquid-cooled architectures, where cooling design is specified alongside compute and networking rather than after rack selection. Rack-Scale Power Delivery Systems are also gaining strategic value as vendors align around 800 VDC architectures for denser AI deployments. In the rack-scale GPU infrastructure market, this means compute still anchors revenue, but cooling and power now play a larger role in determining who can actually deploy on time.

Cluster-Scale AI Factory Deployments are projected to grow at a 35.18% CAGR from 2026 to 2031, the fastest pace among deployment models. This reflects a shift among the largest operators from staged pod additions toward integrated campus-scale builds that can be repeated across multiple sites. Supermicro's DCBBS blueprints for NVIDIA Vera Rubin NVL72 were designed to scale from a 1,152-GPU building block to 1 GW, demonstrating how vendors are packaging large AI deployments as repeatable construction units rather than one-off engineering projects. The rack-scale GPU infrastructure market is therefore shifting toward larger contract structures that tie hardware strategy to long-term site planning.

Multi-Rack Pod Deployments held 50.46% of deployment-scale revenues in 2025 and remained the default procurement unit for many enterprises and mid-scale cloud builds. The pod model works well because 4 to 16 racks can be integrated around a shared network and cooling loop without the heavier design burden of a full campus program. Single-Rack Deployments still serve enterprise inference and first-time AI infrastructure programs where fabric overhead and facility changes would be harder to justify. In the rack-scale GPU infrastructure market, the move from single rack to pod and from pod to cluster is not just a size change, because each step adds more services, more facility coordination, and more integration revenue.

Complete Report Scope:

• By Solution Type
  • Rack-Scale Compute Systems
  • Rack-Scale Networking Systems
  • Rack-Scale Cooling Systems
  • Rack-Scale Power Delivery Systems
• By Deployment Scale
  • Single-Rack Deployments
  • Multi-Rack Pod Deployments
  • Cluster-Scale AI Factory Deployments
• By Cooling Architecture
  • Air-Cooled Rack Infrastructure
  • Direct-to-Chip Liquid-Cooled Rack Infrastructure
  • Immersion-Cooled Rack Infrastructure
  • Hybrid Cooling Rack Infrastructure
• By End User
  • Cloud Service Providers
  • Enterprises
  • Government and Research Institutions
  • Telecommunications Providers
  • Edge Infrastructure 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 45.29% of global revenues in 2025 and remained the largest regional market for rack-scale GPU infrastructure. The region benefits from the concentration of hyperscaler and neocloud campuses across Northern Virginia, Dallas, Phoenix, and the Pacific Northwest. At the same time, power availability has become the clearest brake on build speed, with the International Energy Agency warning that around 20% of planned data center projects are at risk of delay because of grid constraints. Canada added another layer of public demand when it launched its AI Sovereign Compute Infrastructure Program, with CAD 890 million (USD 648 million) for large-scale national AI compute capacity. This keeps North America central to the rack-scale GPU infrastructure market, even as site-readiness issues make delivery timing harder to predict.

Asia-Pacific is projected to expand at a 35.42% CAGR from 2026 to 2031, making it the fastest-growing geography in the rack-scale GPU infrastructure market. South Korea is part of that shift, with KISTI moving ahead on Supercomputer No. 6 under a KRW 382.5 billion (USD 278 million) contract with Hewlett Packard Enterprise. China also remains important to regional demand, but US export controls on advanced computing items under ECCNs 3A090 and 4A090 continue to limit access for China-headquartered entities to the most advanced classes of AI server systems. Japan added to the region's momentum when RIKEN announced the completion and operational launch of ROQUO in June 2026.

Europe remained the third-largest region in the rack-scale GPU infrastructure market and continued to build around public-sector compute programs. EuroHPC JU signed the EUR 55 million (USD 62.2 million) with Hewlett Packard Enterprise for HLRS Stuttgart in March 2026. The United Kingdom also committed GBP 1.1 billion (USD 1.4 billion) through its AI Hardware Plan and another GBP 750 million (USD 952.5 million) for a national supercomputer in Edinburgh, giving the region a meaningful sovereign infrastructure pipeline. This public funding base gives Europe steadier demand visibility than many privately led markets, even though project pacing still depends on site preparation and system integration capacity.

List of Companies Covered in this Report:

1. NVIDIA Corporation
2. Advanced Micro Devices, Inc.
3. Intel Corporation
4. Super Micro Computer, Inc.
5. Hewlett Packard Enterprise Company
6. Dell Technologies Inc.
7. Lenovo Group Limited
8. Giga-Byte Technology Co., Ltd.
9. Quanta Computer Inc.
10. Quanta Cloud Technology Inc.
11. Wiwynn Corporation
12. Inventec Corporation
13. Foxconn Technology Co., Ltd.
14. Ingrasys Technology Inc.
15. Pegatron Corporation
16. ASUS Computer Inc.
17. Giga Computing Technology Co., Ltd.
18. Tyan Computer Corporation
19. MiTAC Computing Technology Corporation
20. FII Group
21. Lambda, Inc.
22. Penguin Solutions, Inc.
23. H3C Technologies Co., Limited

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 Surging Hyperscaler Demand for AI Factory Racks
  • 4.2.2 Rapid Shift to Liquid-Cooled High-Density Infrastructure
  • 4.2.3 Expanding Sovereign AI and National Compute Programs
  • 4.2.4 Rack-Level Interconnect Standardization and Disaggregation
  • 4.2.5 Rising Adoption of Inference-Optimized Neocloud Deployments
  • 4.2.6 Under-Reported Driver, Power Density Redesign Favoring Prefabricated AI Racks
• 4.3 Market Restraints
  • 4.3.1 Advanced Packaging and HBM Supply Bottlenecks
  • 4.3.2 Grid Capacity and Site-Readiness Constraints
  • 4.3.3 Export Controls and Cross-Border Deployment Friction
  • 4.3.4 Under-Reported Restraint, Integration Complexity Across Compute, Power, Cooling, and Optical Fabrics
• 4.4 Industry Value Chain Analysis
• 4.5 Technological Outlook
• 4.6 Impact of Macroeconomic Factors on the Market
• 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 Industry Rivalry

5 MARKET SIZE AND GROWTH FORECASTS (VALUE)

• 5.1 By Solution Type
  • 5.1.1 Rack-Scale Compute Systems
  • 5.1.2 Rack-Scale Networking Systems
  • 5.1.3 Rack-Scale Cooling Systems
  • 5.1.4 Rack-Scale Power Delivery Systems
• 5.2 By Deployment Scale
  • 5.2.1 Single-Rack Deployments
  • 5.2.2 Multi-Rack Pod Deployments
  • 5.2.3 Cluster-Scale AI Factory Deployments
• 5.3 By Cooling Architecture
  • 5.3.1 Air-Cooled Rack Infrastructure
  • 5.3.2 Direct-to-Chip Liquid-Cooled Rack Infrastructure
  • 5.3.3 Immersion-Cooled Rack Infrastructure
  • 5.3.4 Hybrid Cooling Rack Infrastructure
• 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 Telecommunications Providers
  • 5.4.5 Edge Infrastructure 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 Vendor 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 NVIDIA Corporation
  • 6.4.2 Advanced Micro Devices, Inc.
  • 6.4.3 Intel Corporation
  • 6.4.4 Super Micro Computer, Inc.
  • 6.4.5 Hewlett Packard Enterprise Company
  • 6.4.6 Dell Technologies Inc.
  • 6.4.7 Lenovo Group Limited
  • 6.4.8 Giga-Byte Technology Co., Ltd.
  • 6.4.9 Quanta Computer Inc.
  • 6.4.10 Quanta Cloud Technology Inc.
  • 6.4.11 Wiwynn Corporation
  • 6.4.12 Inventec Corporation
  • 6.4.13 Foxconn Technology Co., Ltd.
  • 6.4.14 Ingrasys Technology Inc.
  • 6.4.15 Pegatron Corporation
  • 6.4.16 ASUS Computer Inc.
  • 6.4.17 Giga Computing Technology Co., Ltd.
  • 6.4.18 Tyan Computer Corporation
  • 6.4.19 MiTAC Computing Technology Corporation
  • 6.4.20 FII Group
  • 6.4.21 Lambda, Inc.
  • 6.4.22 Penguin Solutions, Inc.
  • 6.4.23 H3C Technologies Co., Limited

7 MARKET OPPORTUNITIES AND FUTURE OUTLOOK

• 7.1 White-Space and Unmet-Need Assessment