AI 가속기 클러스터 시장 : 점유율 분석, 업계 동향 및 통계, 성장 예측(2026-2031년)

According to Mordor Intelligence, the AI accelerator cluster market size is expected to grow from USD 65.28 billion in 2025 to USD 76.25 billion in 2026 and is forecast to reach USD 166.27 billion by 2031 at 16.87% CAGR over 2026-2031.

This report is Segmented by Component (Compute Infrastructure, Storage Infrastructure, and More), Accelerator Architecture (GPU-Based Clusters, and More), Cluster Size (Up To 256 Accelerators, and More), Function (Training, and Inference), Deployment (On-Premises, and More), End User (Hyperscale Cloud Service Providers, and More), and Geography. The Market Forecasts are Provided in Terms of Value (USD).

Global AI Accelerator Cluster Market Trends and Insights

Rapid Expansion Of Generative AI Training Workloads

The AI accelerator cluster market is being pushed higher by the rapid increase in training scale for frontier models, because buyers now plan for much larger compute blocks than they did a few years ago. Rack-level and factory-level infrastructure have moved into production to support this shift, which shows that vendors now expect sustained demand rather than short project spikes. The training cycle matters beyond the first purchase, because once a model family is built on a certain compute and networking stack, later deployment often stays aligned with the same architecture. That makes hardware demand more durable across the AI accelerator cluster market, especially when customers want smoother software compatibility and easier fleet expansion. It also raises the cost of delay for operators, because every postponed build can affect later inference readiness, vendor qualification, and software tuning work. The result is a procurement pattern in the AI accelerator cluster market where large training systems influence several later spending decisions across compute, memory, software, and facility design.

Hyperscale Cloud Capital Expenditure On AI Infrastructure

Large cloud operators continue to shape the AI accelerator cluster market because their infrastructure programs set the pace for system orders, supplier commitments, and deployment calendars. The scale of current factory-style rollouts is visible in the production ramp of NVIDIA's Vera Rubin platform, which entered full production in May 2026 through 150 supply-chain partners, 350+ factories, and 30 countries. The same pattern appears in system design, where liquid-cooled rack platforms are now being shipped as complete infrastructure blocks rather than as isolated server upgrades. This matters for the AI accelerator cluster market because long planning cycles in memory, advanced packaging, networking, and cooling now start much earlier in the buying process. It also makes supply access more uneven, since vendors with long-term design wins and partner alignment can secure build capacity ahead of smaller buyers. As a result, hyperscale spending is not only adding capacity in the AI accelerator cluster market, it is also shaping who can obtain key components on time.

High Total Cost Of Ownership For Power And Cooling

Power and cooling remain a major restraint on the AI accelerator cluster market because facility readiness is now a deciding factor in whether systems can be deployed on schedule. NVIDIA stated that Vera Rubin NVL72 runs at around 132 kW under sustained training loads, which makes direct liquid cooling a requirement rather than an option. The same source noted that liquid cooling is becoming central to AI factory design, which means the hardware sales often depend on whether the site can handle thermal and power density at the rack level. The International Energy Agency also reported that data center electricity demand could double between 2022 and 2026, and it flagged power grid limits as a growing source of project delay. This creates a layered cost issue in the AI accelerator cluster market, because buyers must fund accelerators, cooling systems, facility upgrades, and, in some cases, grid-related waiting periods. It also slows broader enterprise adoption, since many mid-sized operators cannot absorb the retrofit burden as easily as hyperscalers or state-backed programs.

Other drivers and restraints analyzed in the detailed report include:

1. Rising Cluster-Scale Demand For Low-Latency Interconnects
2. Shift Toward Dedicated Inference Clusters In Enterprise Data Centers
3. Supply Constraints In Advanced Packaging And High-Bandwidth Memory

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

Segment Analysis

Compute infrastructure held 70.46% of revenue in 2025, which made it the largest component in the AI accelerator cluster market. That lead reflected the central role of accelerator silicon, rack-scale server systems, and power distribution hardware in every deployment. Networking infrastructure remained the next most important spending layer, because high-bandwidth fabrics determine whether larger clusters can operate efficiently under training and inference loads. Storage infrastructure and services also remained important, especially where parallel file systems and nearby object storage were needed to reduce data movement delays. The AI accelerator cluster market still shows a clear hardware bias at the component level, because most early spending goes first to systems that can be installed, energized, and brought into production.

Cluster management software is projected to grow at 17.04% CAGR from 2026 to 2031, which makes it the fastest-moving component in the AI accelerator cluster market. This reflects a practical shift, because scheduling, fault recovery, and power-aware orchestration become harder as systems move toward very large accelerator counts. Software also gains value when operators manage mixed hardware environments and want higher utilization across different node types. In that sense, the AI accelerator cluster industry is not moving away from hardware demand, but it is assigning more value to the layer that keeps large systems stable and productive. Over time, software orchestration is likely to capture a larger share of wallet inside the AI accelerator cluster market because cluster complexity keeps rising with scale.

GPU-based clusters held 80.27% of revenue in 2025, which kept them at the center of the AI accelerator cluster market. Their lead came from broad support across training, research, and flexible enterprise use cases where software compatibility remains a priority. This installed base also gave buyers confidence in support tools, system integration, and developer familiarity. TPU-based clusters remained concentrated in internal deployments, while FPGA-based clusters stayed limited to narrower use cases such as low-latency inference or telecom workloads. Heterogeneous accelerator formats remained relevant in selected environments, but they did not alter the main structure of the AI accelerator cluster market.

Custom AI ASIC-based clusters are projected to grow at 17.21% CAGR from 2026 to 2031, which makes them the fastest-growing architecture in the AI accelerator cluster market. Their growth reflects a clear buyer motive, because purpose-built silicon can deliver better cost efficiency in stable, high-volume inference environments. The shift is most visible among large operators that can spread design costs across very large deployments and repeated workloads. This keeps GPUs in a strong position for general-purpose flexibility, but it also gives custom silicon a stronger foothold where throughput-per-dollar matters more than broad programmability. As this mix evolves, the AI accelerator cluster market is likely to stay GPU-led in revenue while becoming more architecturally varied at the high-volume inference edge.

Complete Report Scope:

• By Component
  • Compute Infrastructure
  • Networking Infrastructure
  • Storage Infrastructure
  • Cluster Management Software
  • Services
• By Accelerator Architecture
  • GPU-Based Clusters
  • TPU-Based Clusters
  • FPGA-Based Clusters
  • Custom AI ASIC-Based Clusters
  • Heterogeneous Accelerator Clusters
• By Cluster Size
  • Up to 256 Accelerators
  • 257-2,048 Accelerators
  • 2,049-16,384 Accelerators
  • Above 16,384 Accelerators
• By Function
  • Training
  • Inference
• By Deployment Model
  • Cloud-Based
  • On-Premises
  • Hybrid
• By End User
  • Hyperscale Cloud Service Providers
  • Enterprises
  • Government and Research Institutions
  • Telecommunications Providers
  • Colocation Service Providers
• 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 held 52.64% of the AI accelerator cluster market share in 2025, which kept it as the largest regional market. The region benefits from the concentration of hyperscaler headquarters, mature data center corridors, and a deep pool of frontier AI development activity. It also has a strong base of value-chain companies across accelerators, servers, networking, and systems integration. At the same time, grid readiness has become a real constraint for new large-scale deployments, and policy attention around data centers and power access has increased. Texas Senate Bill 6, enacted in June 2025, was designed to streamline parts of the ERCOT large-load interconnection process and clarify how utility upgrade costs are handled for new demand.

Asia-Pacific is projected to expand at 17.22% CAGR from 2026 to 2031, which makes it the fastest-growing regional part of the AI accelerator cluster market. Growth in the region reflects a mix of domestic Chinese capacity buildout, public support for compute infrastructure, and stronger interest in power-rich deployment corridors. Huawei stated that its Ascend 950PR entered commercial deployment in 2026, and it also highlighted the Shenzhen Ascend cluster as a fully indigenous 14,000-card installation that went live in March 2026. That development shows how export-control pressure is pushing regional ecosystems to deepen domestic capability rather than step back from expansion. The region is therefore becoming more important to the AI accelerator cluster market not only because it is growing quickly, but also because it is forming more distinct local supply and deployment paths.

Europe held a meaningful position in the AI accelerator cluster market in 2025, with demand shaped more by sovereignty goals than by stand-alone hyperscaler expansion. The Franco-German joint paper on digital sovereignty in June 2026 called for a stronger European technology package and targeted a 30-50% sovereign compute share for public-sector workloads by 2027 and 2028. EuroHPC's AI Factory selections are reinforcing that direction by creating a coordinated public infrastructure base across the region. South America and the Middle East and Africa remained smaller markets, but sovereign investment and infrastructure diversification efforts are gradually making them more relevant to the wider AI accelerator cluster market.

1. NVIDIA Corporation
2. Advanced Micro Devices, Inc.
3. Intel Corporation
4. Alphabet Inc.
5. Amazon.com, Inc.
6. Microsoft Corporation
7. Google LLC
8. Meta Platforms, Inc.
9. Hewlett Packard Enterprise Company
10. Dell Technologies Inc.
11. Super Micro Computer, Inc.
12. Lenovo Group Limited
13. Cisco Systems, Inc.
14. Broadcom Inc.
15. Marvell Technology, Inc.
16. Arista Networks, Inc.
17. Taiwan Semiconductor Manufacturing Company Limited
18. SK hynix Inc.
19. Micron Technology, Inc.
20. Samsung Electronics Co., Ltd.
21. Oracle Corporation

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 Rapid Expansion of Generative AI Training Workloads
  • 4.3.2 Hyperscale Cloud Capital Expenditure on AI Infrastructure
  • 4.3.3 Rising Cluster-Scale Demand for Low-Latency Interconnects
  • 4.3.4 Shift Toward Dedicated Inference Clusters in Enterprise Data Centers
  • 4.3.5 Export-Control Driven Regional Buildout of Domestic AI Compute Capacity
  • 4.3.6 Power-Dense Rack Design Improvements Enabling Larger Cluster Deployments
• 4.4 Market Restraints
  • 4.4.1 High Total Cost of Ownership for Power and Cooling
  • 4.4.2 Supply Constraints in Advanced Packaging and High-Bandwidth Memory
  • 4.4.3 Software Portability and Vendor Lock-In Risk
  • 4.4.4 Data Center Grid Interconnection Delays for Mega-Clusters
• 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 Suppliers
  • 4.8.2 Bargaining Power of Buyers
  • 4.8.3 Threat of New Entrants
  • 4.8.4 Threat of Substitutes
  • 4.8.5 Industry Rivalry
  • 4.8.6 Analysis

5 MARKET SIZE AND GROWTH FORECASTS (VALUE)

• 5.1 By Component
  • 5.1.1 Compute Infrastructure
  • 5.1.2 Networking Infrastructure
  • 5.1.3 Storage Infrastructure
  • 5.1.4 Cluster Management Software
  • 5.1.5 Services
• 5.2 By Accelerator Architecture
  • 5.2.1 GPU-Based Clusters
  • 5.2.2 TPU-Based Clusters
  • 5.2.3 FPGA-Based Clusters
  • 5.2.4 Custom AI ASIC-Based Clusters
  • 5.2.5 Heterogeneous Accelerator Clusters
• 5.3 By Cluster Size
  • 5.3.1 Up to 256 Accelerators
  • 5.3.2 257-2,048 Accelerators
  • 5.3.3 2,049-16,384 Accelerators
  • 5.3.4 Above 16,384 Accelerators
• 5.4 By Function
  • 5.4.1 Training
  • 5.4.2 Inference
• 5.5 By Deployment Model
  • 5.5.1 Cloud-Based
  • 5.5.2 On-Premises
  • 5.5.3 Hybrid
• 5.6 By End User
  • 5.6.1 Hyperscale Cloud Service Providers
  • 5.6.2 Enterprises
  • 5.6.3 Government and Research Institutions
  • 5.6.4 Telecommunications Providers
  • 5.6.5 Colocation Service Providers
• 5.7 By Geography
  • 5.7.1 North America
    • 5.7.1.1 United States
    • 5.7.1.2 Canada
    • 5.7.1.3 Mexico
  • 5.7.2 Europe
    • 5.7.2.1 Germany
    • 5.7.2.2 United Kingdom
    • 5.7.2.3 France
    • 5.7.2.4 Italy
    • 5.7.2.5 Rest of Europe
  • 5.7.3 Asia-Pacific
    • 5.7.3.1 China
    • 5.7.3.2 Japan
    • 5.7.3.3 South Korea
    • 5.7.3.4 India
    • 5.7.3.5 Southeast Asia
    • 5.7.3.6 Rest of Asia-Pacific
  • 5.7.4 South America
  • 5.7.5 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 NVIDIA Corporation
  • 6.4.2 Advanced Micro Devices, Inc.
  • 6.4.3 Intel Corporation
  • 6.4.4 Alphabet Inc.
  • 6.4.5 Amazon.com, Inc.
  • 6.4.6 Microsoft Corporation
  • 6.4.7 Google LLC
  • 6.4.8 Meta Platforms, Inc.
  • 6.4.9 Hewlett Packard Enterprise Company
  • 6.4.10 Dell Technologies Inc.
  • 6.4.11 Super Micro Computer, Inc.
  • 6.4.12 Lenovo Group Limited
  • 6.4.13 Cisco Systems, Inc.
  • 6.4.14 Broadcom Inc.
  • 6.4.15 Marvell Technology, Inc.
  • 6.4.16 Arista Networks, Inc.
  • 6.4.17 Taiwan Semiconductor Manufacturing Company Limited
  • 6.4.18 SK hynix Inc.
  • 6.4.19 Micron Technology, Inc.
  • 6.4.20 Samsung Electronics Co., Ltd.
  • 6.4.21 Oracle Corporation

7 MARKET OPPORTUNITIES AND FUTURE OUTLOOK

• 7.1 White-Space and Unmet-Need Assessment