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

According to Mordor Intelligence, the singapore data center GPU market size was valued at USD 0.42 billion in 2026 and is estimated to grow from USD 0.36 billion in 2025 to reach USD 0.79 billion by 2031, advancing at a 13.37% CAGR over 2026-2031.

This report is Segmented by Deployment Type (Cloud Data Centers, Enterprise/Private Data Centers, and More), GPU Type (Training GPUs, Inference GPUs), Interconnect (PCIe-Based GPUs, High-Bandwidth Interconnect GPUs), Workload Type (AI and ML, HPC, and More), and End-User (Hyperscalers/CSPs, Enterprises, Government, and Research Institutions). Market Forecasts are Provided in Terms of Value (USD).

Singapore Data Center GPU Market Trends and Insights

Surge in Generative AI and LLM Training Demand

Large language model training has become the single biggest catalyst for new GPU clusters. The ASPIRE 2A+ supercomputer's 320 NVIDIA H100 GPUs cut the MERaLiON model's training time from 340 days to under 6 days, proving the productivity leap enabled by dense accelerators. Sovereign AI initiatives now require on-premises capacity to maintain data residency, prompting agencies to deploy B200 DGX SuperPODs for frontier workloads. Regional model builders such as Firmus AI reserve hundreds of H200 GPUs for months, a demand pattern spot markets cannot match.University clusters support video generative AI, surgical intelligence, and materials science, broadening use cases beyond natural language processing. As model sizes swell, interconnect bandwidth and memory capacity dictate architectural choices, reinforcing the shift to InfiniBand fabrics.

Hyperscaler Expansion and Pre-Committed Capacity in Singapore

Microsoft, Amazon Web Services, and Google have collectively earmarked more than USD 19 billion for Singapore builds between 2024 and 2029, with a disproportionate share targeting GPU-dense availability zones. Keppel DC REIT's hyperscaler rent climbed to 69.3% of revenue in fiscal 2025, and rental reversions reached 45%, signaling that cloud giants will pay premiums for liquid-cooled halls. The DC-CFA2 program's 200-megawatt cap, coupled with a March 2026 build deadline, triggered a land rush that locked hyperscalers into multi-year commitments. Asset-backed conversions of legacy halls into GPU rooms accelerated, highlighted by KDC Singapore 7 and 8's SGD 1.4 billion purchase. These moves cement Singapore as the region's AI gravity center despite more affordable capacity in neighboring Malaysia.

Land and Power Caps Limiting New Facilities

Singapore's moratorium, lifted only partially through the DC-CFA2 call, restricts expansion to 200 megawatts and mandates 50% renewable energy, forcing rack densities up to 120 kilowatts. Space scarcity drives cooling, electrical, and structural complexities that lengthen project timelines. Operators hedge by securing capacity in Malaysia and Australia, but latency-sensitive AI inference still gravitates to Singapore. Cross-border renewable imports remain uncertain, and solar yield is capped by limited rooftop real estate, sustaining the constraint beyond 2026.

Other drivers and restraints analyzed in the detailed report include:

1. Government Incentives for Green Data Centers
2. Rapid Enterprise Adoption of AI Workloads
3. Global GPU Supply Constraints and Price Volatility

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

Segment Analysis

Cloud data centers captured 67.42% of the Singapore data center GPU market size in 2025, reflecting hyperscaler scale economics and their ability to sign multi-year renewable power purchase agreements, while edge data centers were identified as the fastest-growing segment through at 16.94% CAGR through 2031. The concentration deepened as Microsoft and AWS reserved GPU halls years in advance, pushing colocation rates toward the upper end of USD 480 per kilowatt per month. Enterprise-class private clouds mounted a comeback once data residency clauses tightened in financial services, leading banks to carve out on-premises GPU zones inside Tier 4 facilities. Edge builds recorded the sharpest growth, driven by autonomous vehicle testing tracks in Tuas and live-stream analytics at the port, where sub-10-millisecond latency is mandatory.

In 2026, the Singapore data center GPU market sees cloud operators retrofit existing halls with immersion tanks while edge specialists deploy prefabricated 6-kilowatt pods near 5G base stations. Nxera's cable-landing integration model further blurs lines between core and edge by offering regional inference at cloud-class throughput. Universities and government labs continue to build in-country clusters for sovereign workloads, ensuring that the cloud's share edges down slightly even as absolute capacity rises.

Inference devices led the segment with 56.93% share of the Singapore data center GPU market in 2025 as customer-facing chatbots, fraud detectors, and digital twins demanded low-latency responses, whereas training GPUs are registering the highest growth at 17.45% CAGR through 2031 momentum across the forecast window. Banks opted for H100 NVL cards configured for 60-watt power caps to fit within legacy air corridors, while logistics firms standardized on L40S boards for computer vision. Training-class accelerators, however, posted the quickest growth as large language model developers locked in H200 and early Blackwell allocations.

The Singapore data center GPU market share tilted toward training when public-sector buyers ordered DGX SuperPODs for national security language models. Multi-tenancy constraints limited private clouds to inference-only racks, but new isolation features in GB200-class systems will allow mixed-workload clusters from 2027 onward. Training demand also spurred the adoption of unified memory clusters, ensuring that the two GPU types increasingly coexist rather than compete.

Complete Report Scope:

• By Deployment Type
  • Cloud Data Centers
  • Enterprise / Private Data Centers
  • Edge Data Centers
• By GPU Type
  • Training GPUs
  • Inference GPUs
• By Interconnect
  • PCIe-Based GPUs
  • High-Bandwidth Interconnect GPUs
• By Workload Type
  • Artificial Intelligence and Machine Learning
  • High-Performance Computing (non-AI scientific computing)
  • Data Analytics (database acceleration, query processing)
  • Graphics and Visualization (VDI, rendering, digital twins)
• By End-User
  • Hyperscalers / Cloud Service Providers
  • Enterprises
  • Government and Research Institutions

List of Companies Covered in this Report:

1. NVIDIA Corporation
2. Advanced Micro Devices, Inc.
3. Intel Corporation
4. Dell Technologies Inc.
5. Hewlett Packard Enterprise Company
6. Lenovo Group Limited
7. Super Micro Computer, Inc.
8. ASUStek Computer Inc.
9. GIGABYTE Technology Co., Ltd.
10. Inspur Electronic Information Industry Co., Ltd.
11. Fujitsu Limited
12. Huawei Technologies Co., Ltd.
13. xFusion Digital Technologies Co., Ltd.
14. Equinix, Inc.
15. Digital Realty Trust, Inc.
16. ST Telemedia Global Data Centres
17. Keppel DC REIT
18. Singtel Group (Nxera)
19. Amazon Web Services, Inc.
20. Microsoft Corporation
21. Google LLC
22. Aethir Pte. 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 Surge in Generative AI and LLM Training Demand
  • 4.2.2 Hyperscaler Expansion and Pre-Committed Capacity in Singapore
  • 4.2.3 Government Incentives for Green Data Centers
  • 4.2.4 Rapid Enterprise Adoption of AI Workloads
  • 4.2.5 Decentralized GPUaaS Platforms Filling Capacity Gaps
  • 4.2.6 Integrated Cable-Landing Data Centers Lowering Latency
• 4.3 Market Restraints
  • 4.3.1 Land and Power Caps Limiting New Facilities
  • 4.3.2 Global GPU Supply Constraints and Price Volatility
  • 4.3.3 Skilled Workforce Shortage in Liquid-Cooling Operations
  • 4.3.4 Water-Use Scrutiny Impacting Facility Permits
• 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 Suppliers
  • 4.8.2 Bargaining Power of Buyers
  • 4.8.3 Threat of New Entrants
  • 4.8.4 Threat of Substitutes
  • 4.8.5 Competitive Rivalry

5 MARKET SIZE AND GROWTH FORECASTS (VALUE)

• 5.1 By Deployment Type
  • 5.1.1 Cloud Data Centers
  • 5.1.2 Enterprise / Private Data Centers
  • 5.1.3 Edge Data Centers
• 5.2 By GPU Type
  • 5.2.1 Training GPUs
  • 5.2.2 Inference GPUs
• 5.3 By Interconnect
  • 5.3.1 PCIe-Based GPUs
  • 5.3.2 High-Bandwidth Interconnect GPUs
• 5.4 By Workload Type
  • 5.4.1 Artificial Intelligence and Machine Learning
  • 5.4.2 High-Performance Computing (non-AI scientific computing)
  • 5.4.3 Data Analytics (database acceleration, query processing)
  • 5.4.4 Graphics and Visualization (VDI, rendering, digital twins)
• 5.5 By End-User
  • 5.5.1 Hyperscalers / Cloud Service Providers
  • 5.5.2 Enterprises
  • 5.5.3 Government and Research Institutions

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 Dell Technologies Inc.
  • 6.4.5 Hewlett Packard Enterprise Company
  • 6.4.6 Lenovo Group Limited
  • 6.4.7 Super Micro Computer, Inc.
  • 6.4.8 ASUStek Computer Inc.
  • 6.4.9 GIGABYTE Technology Co., Ltd.
  • 6.4.10 Inspur Electronic Information Industry Co., Ltd.
  • 6.4.11 Fujitsu Limited
  • 6.4.12 Huawei Technologies Co., Ltd.
  • 6.4.13 xFusion Digital Technologies Co., Ltd.
  • 6.4.14 Equinix, Inc.
  • 6.4.15 Digital Realty Trust, Inc.
  • 6.4.16 ST Telemedia Global Data Centres
  • 6.4.17 Keppel DC REIT
  • 6.4.18 Singtel Group (Nxera)
  • 6.4.19 Amazon Web Services, Inc.
  • 6.4.20 Microsoft Corporation
  • 6.4.21 Google LLC
  • 6.4.22 Aethir Pte. Ltd.

7 MARKET OPPORTUNITIES AND FUTURE OUTLOOK

• 7.1 White-Space and Unmet-Need Assessment
  • 7.1.1 Fujitsu Limited
  • 7.1.2 Amazon Web Services, Inc. (Annapurna Labs)
  • 7.1.3 Google LLC
  • 7.1.4 Samsung Electronics Co., Ltd.
  • 7.1.5 EVGA Corporation
  • 7.1.6 Xilinx, Inc. (AMD)
  • 7.1.7 Arm Ltd.
  • 7.1.8 Tyan Computer Corporation
  • 7.1.9 Synopsys, Inc.

8 MARKET OPPORTUNITIES AND FUTURE OUTLOOK

• 8.1 White-Space and Unmet-Need Assessment