동남아시아의 데이터센터 GPU : 시장 점유율 분석, 업계 동향과 통계, 성장 예측(2026-2031년)

According to Mordor Intelligence, the Southeast Asia data center GPU market size is projected to be USD 1.61 billion in 2025, USD 1.95 billion in 2026, and reach USD 3.93 billion by 2031, growing at a CAGR of 15.03% from 2026 to 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, and More). The Market Forecasts are Provided in Terms of Value (USD).

Southeast Asia Data Center GPU Market Trends and Insights

Rapid Build-Out of AI-Optimized Hyperscale Facilities

Hyperscaler capital commitments topped USD 13 billion between January 2025 and March 2026, highlighted by Microsoft's USD 5.5 billion plan for Singapore and USD 1.1 billion for Thailand. New campuses in Batam and Johor are designed around GPU-dense racks that exceed 80 kilowatts, a configuration that pushes liquid-cooling adoption and shapes vendor roadmaps toward chassis-level heat reuse.Proximity to subsea cable landings improves latency to major Asian metros, which keeps workloads such as large language model serving anchored in the same availability zones. However, clustering of megawatt-scale projects inside a few corridors exposes operators to grid caps that can slow additional build-outs. Overall, sustained multiyear capex pipelines provide a clear demand signal that underpins the next wave of GPU volume orders.

Proliferation of 5G-Enabled Edge Nodes for Low-Latency AI Inference

More than 15,000 5G base stations installed during 2025 embedded micro data centers that host 4- to 8-GPU appliances for video analytics, autonomous vehicle telemetry, and industrial IoT workloads. Singtel's Paragon platform showed sub-10-millisecond performance, proving that inference can shift away from centralized cloud while meeting quality-of-service targets.Telecommunications operators are now bundling infrastructure-as-a-service contracts that spread GPU capex across multiyear leases, which shortens sales cycles for entry-level accelerators. Yet fragmented site footprints create operational complexity because each edge location demands on-site maintenance skills, hardened enclosures, and remote orchestration stacks.

Chronic Grid Instability and Power-Supply Constraints

Johor regulators rejected nearly one-third of data center applications in 2025 because substations could not meet megawatt-scale loads, placing operators in multiyear queues for capacity upgrades. Indonesia's coal-dependent grid delivers only 99.7% uptime, short of the 99.995% needed for Tier III certification, leading to voltage swings that trip GPU thermal throttling safeguards. Power demand across the region is set to jump from 9 terawatt-hours in 2024 to 68 terawatt-hours by 2030, far outpacing confirmed generation projects. Enterprises are forced to lease diesel generators, which lift operating expenditure by up to 20% and undermine net-zero pledges, creating a wedge between sustainability rhetoric and day-to-day resiliency planning.

Other drivers and restraints analyzed in the detailed report include:

1. Rising Adoption of GPU-Accelerated Databases for Fintech
2. Government Incentives for Green Data Centers and Carbon Credits
3. Escalating Geopolitical Risk to Global GPU Supply Chains

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

Segment Analysis

Cloud installations delivered 58.76% of regional shipments in 2025, anchored in Singapore and Johor campuses that string tens of thousands of GPUs behind NVLink and InfiniBand fabrics to train and serve trillion-parameter models. Hyperscalers benefit from renewable power contracts that assure sub-1.3 power usage effectiveness as well as tax abatements linked to export revenue. Edge facilities, though smaller individually, are multiplying quickly because 5G densification demands inference at the radio access network; each micro site carries 4-8 NVIDIA T4 or A2 cards to guarantee sub-10-millisecond response for video analytics. The data center GPU market size tied to edge nodes is projected to surge at more than 22% CAGR, driven by telecom partnerships that spread capex across monthly subscriptions. Enterprise and private data centers round out the picture, mainly serving regulated industries that must retain certain records on-premises and burst excess loads to the public cloud when seasonal peaks hit.

Smaller footprints at the edge shift infrastructure design toward modular blades with single-phase immersion cooling and remote orchestration, a contrast to the monolithic chillers deployed in 120-kilowatt cloud racks. Telecommunication operators now negotiate joint procurement pools to unlock volume discounts, but heterogeneous deployment standards still inflate integration overhead. Meanwhile, colocation landlords in Jakarta and Bangkok bundle dedicated dark fiber into leases to capture hybrid workloads that pin sensitive data on premises while leaning on hyperscaler GPU bursts for peak analytics. This distributed topology diversifies revenue for the data center GPU market and hedges location risk, yet also fragments vendor relationships, complicating firmware management at scale.

Inference accelerators captured 57.52% share in 2025 as enterprises prioritized monetizable services like chatbots, recommendation engines, and fraud screening over pure research training. The data center GPU market share tied to inference is expected to widen as transformer quantization reduces memory requirements and allows four inference GPUs to serve workloads previously needing eight. NVIDIA H100 NVL and L40S boards headline deployments in hyperscaler inference farms, while AMD MI300X competes on cost per token processed, especially in subscription tiers engineered for small-to-mid-size enterprises. Training GPUs such as H200 and MI325X remain vital for new foundation model development, but their share is bound by high memory premiums and longer lead times.

National supercomputing centers in Singapore and Thailand anchor most training clusters, which are now exploring partitioned scheduling that leases idle cycles to universities and startups. Inference boards, by contrast, surface everywhere from media studios rendering photorealistic scenes to fintech start-ups that refresh risk scores in milliseconds. The pivot toward inference shrinks average card power from 700 watts to 300 watts, easing rack integration and enabling incremental adoption of liquid-cooling retrofits rather than wholesale mechanical overhauls. Vendors that bridge software portability across FP16, FP8, and upcoming FP4 precisions can capture outsized share as model compression techniques proliferate.

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 (AI) and Machine Learning (ML)
  • High-Performance Computing (HPC) (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
• By Geography
  • Indonesia
  • Malaysia
  • Philippines
  • Singapore
  • Thailand
  • Vietnam
  • Rest of Southeast Asia

List of Companies Covered in this Report:

1. NVIDIA Corporation
2. Advanced Micro Devices, Inc.
3. Intel Corporation
4. Huawei Technologies Co., Ltd.
5. Qualcomm Technologies, Inc.
6. Graphcore Limited
7. Baidu, Inc.
8. Tencent Holdings Ltd.
9. Alibaba Group Holding Limited
10. Giga Computing Technology Co., Ltd. (Gigabyte)
11. AsusTek Computer Inc.
12. Lenovo Group Limited
13. ASRock Rack Inc.
14. Super Micro Computer, Inc.
15. Dell Technologies Inc.
16. Hewlett Packard Enterprise Company
17. Inspur Group
18. Acer Inc.
19. Fujitsu Limited
20. Amazon Web Services, Inc. (Annapurna Labs)
21. Google LLC
22. Samsung Electronics Co., Ltd.
23. EVGA Corporation
24. Xilinx, Inc. (AMD)
25. Arm Ltd.
26. Tyan Computer Corporation
27. Synopsys, 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 Rapid Build-Out of AI-Optimized Hyperscale Facilities
  • 4.2.2 Rising Adoption of GPU-Accelerated Databases for Fintech
  • 4.2.3 Government Incentives for Green Data Centers and Carbon Credits
  • 4.2.4 Growing Demand for Real-Time Digital Twin Platforms in Smart Cities
  • 4.2.5 Proliferation of 5G-Enabled Edge Nodes for Low-Latency AI Inference
  • 4.2.6 Expansion of Cloud Gaming Services Across Southeast Asia
• 4.3 Market Restraints
  • 4.3.1 Chronic Grid Instability and Power-Supply Constraints
  • 4.3.2 Limited Availability of Tier IV Data-Center Real Estate in Metro Hubs
  • 4.3.3 High Import Tariffs on Advanced Semiconductor Components
  • 4.3.4 Escalating Geopolitical Risk to Global GPU Supply Chains
• 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 Threat of New Entrants
  • 4.8.2 Bargaining Power of Buyers
  • 4.8.3 Bargaining Power of Suppliers
  • 4.8.4 Threat of Substitute Products or Services
  • 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 (AI) and Machine Learning (ML)
  • 5.4.2 High-Performance Computing (HPC) (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
• 5.6 By Geography
  • 5.6.1 Indonesia
  • 5.6.2 Malaysia
  • 5.6.3 Philippines
  • 5.6.4 Singapore
  • 5.6.5 Thailand
  • 5.6.6 Vietnam
  • 5.6.7 Rest of Southeast Asia

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 Huawei Technologies Co., Ltd.
  • 6.4.5 Qualcomm Technologies, Inc.
  • 6.4.6 Graphcore Limited
  • 6.4.7 Baidu, Inc.
  • 6.4.8 Tencent Holdings Ltd.
  • 6.4.9 Alibaba Group Holding Limited
  • 6.4.10 Giga Computing Technology Co., Ltd. (Gigabyte)
  • 6.4.11 AsusTek Computer Inc.
  • 6.4.12 Lenovo Group Limited
  • 6.4.13 ASRock Rack Inc.
  • 6.4.14 Super Micro Computer, Inc.
  • 6.4.15 Dell Technologies Inc.
  • 6.4.16 Hewlett Packard Enterprise Company
  • 6.4.17 Inspur Group
  • 6.4.18 Acer Inc.
  • 6.4.19 Fujitsu Limited
  • 6.4.20 Amazon Web Services, Inc. (Annapurna Labs)
  • 6.4.21 Google LLC
  • 6.4.22 Samsung Electronics Co., Ltd.
  • 6.4.23 EVGA Corporation
  • 6.4.24 Xilinx, Inc. (AMD)
  • 6.4.25 Arm Ltd.
  • 6.4.26 Tyan Computer Corporation
  • 6.4.27 Synopsys, Inc.

7 MARKET OPPORTUNITIES AND FUTURE OUTLOOK

• 7.1 White-Space and Unmet-Need Assessment