데이터센터 가속기 시장 : 점유율 분석, 산업 동향 및 통계 데이터, 성장 예측(2026-2032년)

According to Mordor Intelligence, data center accelerator market size in 2026 is estimated at USD 14.69 billion, growing from 2025 value of USD 12.89 billion with 2032 projections showing USD 32.13 billion, growing at 13.96% CAGR over 2026-2032.

This report is Segmented by Processor Type (CPU and More), Application (High-Performance Computing, and More), Deployment Model (On-Premise, Colocation, and More), End-User Industry (IT and Telecom, and More), and Geography (North America and More). The Market Forecasts are Provided in (USD).

Global Data Center Accelerator Market Trends and Insights

Surging AI/ML Training Workloads in Hyperscale Data Centers

Hyperscale operators now deploy data halls purpose-built for AI that demand 10-100 times more compute density than legacy enterprise workloads. Meta's USD 800 million Indiana campus exemplifies the shift as it standardizes liquid-cooled racks to accommodate multi-petaflop GPU clusters. Microsoft earmarked more than USD 80 billion for U.S. AI facilities in fiscal-year 2025, underscoring the geographic clustering of training infrastructure. Amazon's USD 100 billion multistate expansion further signals that cloud hyperscalers are pursuing scale economics, but every new megawatt must meet internal renewable-energy thresholds. A balancing act is emerging as enterprises move from prototype models to inference pipelines, resulting in more heterogeneous rack designs that integrate GPU, CPU, and ASIC nodes. Financial institutions exemplify this dual-track buildout, allocating discrete GPU clusters for real-time fraud detection while maintaining CPU-heavy analytics farms for regulatory reporting.

GPU Scarcity Driving Cloud-Based Accelerator Rentals

Chronic shortages of premium GPUs have spawned GPU-as-a-Service platforms that decouple hardware ownership from usage. Oracle Cloud Infrastructure's supercluster supports 16,384 AMD Instinct MI300X GPUs and offers consumption-based web portals, reducing procurement lead times from months to minutes. Re-purposed cryptocurrency-mining sites in North America and Europe contribute power-dense real estate, allowing operators to monetize stranded electrical capacity. The rental model democratizes access for small and midsize organizations that could not previously justify the capital outlay for top-tier accelerators. Service providers also gain leverage when negotiating vendor allocations, enhancing resilience against single-supplier constraints.

Tight Global Supply of Advanced Packaging Substrates

Accelerators that integrate HBM stacks and chiplets rely on Ajinomoto Build-Up Film and CoWoS packaging, materials now subject to year-long lead times. Suppliers prioritize high-margin SKUs, leaving smaller vendors scrambling for limited allocations. Organic-interposer experimentation is underway but will not meaningfully ease constraints for at least two production cycles. Taiwan and South Korea have announced aggressive substrate-capacity expansions, yet the ramp window extends beyond current demand inflection points.

Other drivers and restraints analyzed in the detailed report include:

1. Rapid Adoption of Generative AI in SaaS Platforms
2. Quantum-Inspired Algorithms Demanding Heterogeneous Accelerators
3. Steep Learning Curve for Heterogeneous Programming Models

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

Segment Analysis

GPU processors retained a 73.20% stake in 2025, reflecting their versatility across both model-training and inference tasks. ASIC shipments, however, are projected to rise at a 15.42% CAGR to 2032 as enterprises tune for lower power draw during steady-state inference workloads. Google's internally developed TPU v6 exemplifies the in-house silicon trend that balances performance and cost. Meanwhile, AMD's Instinct MI350 family expands HBM capacity to 288 GB, targeting memory-bound transformer models. CPU sockets still orchestrate I/O and housekeeping tasks, while FPGA cards maintain relevance in telecom edge nodes that call for deterministic latency.

ASIC growth illustrates shifting buyer priorities. Power budgets inside co-location cages rarely scale linearly with rack density, driving operators to favor TOPS-per-watt metrics. Inference-dense SaaS offerings, such as customer-support chatbots and real-time personalization engines, require predictable latency that ASIC designs now deliver. Training workloads will still concentrate on multi-GPU clusters, yet a portion of compute cycles migrates to specialized tensor engines integrated into next-gen GPUs, blurring categorical boundaries. Overall, processor diversity strengthens vendor competition, offering buyers leverage on pricing and supply continuity.

AI training accounted for 49.30% of Data Center Accelerator market revenue in 2025, but inference workloads will record a faster 15.55% CAGR through 2032. Businesses once content with pilot projects are now releasing chatbots, recommendation models, and image-analysis services into production, where latency slippage translates directly into customer churn. High-performance computing remains a stable niche centered on weather modeling, genomics, and computational fluid dynamics, relying on GPUs with larger HBM stacks rather than pure ASICs.

Inference growth ripples across hardware selection. Batch-size variability and strict service-level agreements necessitate accelerators that optimize memory bandwidth over raw floating-point throughput. Healthcare providers employ inference-optimized boards to perform diagnostic imaging at the point of care, shortening time to diagnosis for conditions such as stroke. Financial institutions likewise leverage accelerators for real-time risk scoring, embedding compute nodes inside private-cloud environments for regulatory compliance. The expanding application mix will continue to diversify purchase criteria, with software ecosystem maturity increasingly tipping buying decisions.

Complete Report Scope:

• By Processor Type
  • CPU
  • GPU
  • FPGA
  • ASIC
• By Application
  • High-Performance Computing
  • Artificial Intelligence Training
  • Artificial Intelligence Inference
  • Other Workloads
• By Deployment Model
  • On-Premise/ Enteprise/Edge
  • Colocation
  • Public Cloud
• By End-user Industry
  • IT and Telecom
  • BFSI
  • Healthcare and Life Sciences
  • Government and Defense
  • Media and Entertainment
  • Others End Users
• By Geography
  • North America
    • United States
    • Mexico
    • Canada
  • South America
    • Brazil
    • Rest of South America
  • Europe
    • Germany
    • United Kingdom
    • France
    • Russia
    • Rest of Europe
  • Asia-Pacific
    • China
    • Japan
    • India
    • South Korea
    • Rest of Asia-Pacific
  • Middle East and Africa
    • Middle East
      • Saudi Arabia
      • UAE
      • Turkey
    • Africa
      • South Africa
      • Rest of Africa

Geography Analysis

North America remains the largest buyer, underpinned by hyperscale capital-expenditure plans from Amazon, Microsoft, and Google. Microsoft's spending alone surpasses USD 80 billion for domestic facilities in 2025. Canada and Mexico emerge as near-shore options that balance power-cost and latency considerations while staying within North American regulatory frameworks.

APAC will post the highest CAGR, buoyed by sovereign-cloud mandates and the construction of enormous campuses such as South Korea's USD 35 billion complex. China advances domestic accelerators like Huawei's Ascend series to navigate export-control limitations. Japan's Rapidus consortium and SoftBank's chip initiatives, aided by public funding, aim to reclaim semiconductor manufacturing relevance.

Europe's GAIA-X and IPCEI-CIS programs foster cross-border data-sovereignty clouds. Blackstone's USD 13 billion UK data-center commitment underscores investor confidence in regional AI demand. Middle East and Africa growth hinges on sovereign wealth-fund backing, with energy-price advantages supporting power-hungry installations in the UAE and Saudi Arabia.

1. NVIDIA Corporation
2. Intel Corporation
3. Advanced Micro Devices Inc.
4. Google LLC (TPU)
5. Amazon Web Services Inc.
6. Qualcomm Technologies Inc.
7. Xilinx Inc. (AMD)
8. Achronix Semiconductor Corp.
9. NEC Corporation
10. Dell Technologies Inc.
11. IBM Corporation
12. Cisco Systems Inc.
13. Huawei Technologies Co. Ltd.
14. Alibaba Cloud (Hanguang)
15. Baidu Inc. (Kunlun)
16. Graphcore Ltd.
17. Cerebras Systems Inc.
18. Tenstorrent Inc.
19. Ampere Computing LLC
20. Arm Ltd.
21. Broadcom Inc.
22. Marvell Technology Inc.
23. Samsung Electronics Co. Ltd.
24. Fujitsu 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 Surging AI/ML training workloads in hyperscale data centers
  • 4.2.2 GPU scarcity driving cloud-based accelerator rentals
  • 4.2.3 Rapid adoption of Generative AI in SaaS platforms
  • 4.2.4 Quantum-inspired algorithms demanding heterogeneous accelerators
  • 4.2.5 Edge-to-core workload orchestration (5G and telco clouds)
  • 4.2.6 Sovereign-cloud programs subsidising domestic accelerator fabs
• 4.3 Market Restraints
  • 4.3.1 Tight global supply of advanced packaging substrates
  • 4.3.2 Steep learning-curve for heterogenous programming models
  • 4.3.3 Rising Scope-3 emission targets constraining mega-GPU clusters
  • 4.3.4 Export-control regimes on high-end GPUs/ASICs
• 4.4 Value / Supply-Chain Analysis
• 4.5 Regulatory Landscape
• 4.6 Technological Outlook
• 4.7 Porter's Five Forces
  • 4.7.1 Threat of New Entrants
  • 4.7.2 Bargaining Power of Suppliers
  • 4.7.3 Bargaining Power of Buyers
  • 4.7.4 Threat of Substitutes
  • 4.7.5 Competitive Rivalry

5 MARKET SIZE AND GROWTH FORECASTS (VALUE)

• 5.1 By Processor Type
  • 5.1.1 CPU
  • 5.1.2 GPU
  • 5.1.3 FPGA
  • 5.1.4 ASIC
• 5.2 By Application
  • 5.2.1 High-Performance Computing
  • 5.2.2 Artificial Intelligence Training
  • 5.2.3 Artificial Intelligence Inference
  • 5.2.4 Other Workloads
• 5.3 By Deployment Model
  • 5.3.1 On-Premise/ Enteprise/Edge
  • 5.3.2 Colocation
  • 5.3.3 Public Cloud
• 5.4 By End-user Industry
  • 5.4.1 IT and Telecom
  • 5.4.2 BFSI
  • 5.4.3 Healthcare and Life Sciences
  • 5.4.4 Government and Defense
  • 5.4.5 Media and Entertainment
  • 5.4.6 Others End Users
• 5.5 By Geography
  • 5.5.1 North America
    • 5.5.1.1 United States
    • 5.5.1.2 Mexico
    • 5.5.1.3 Canada
  • 5.5.2 South America
    • 5.5.2.1 Brazil
    • 5.5.2.2 Rest of South America
  • 5.5.3 Europe
    • 5.5.3.1 Germany
    • 5.5.3.2 United Kingdom
    • 5.5.3.3 France
    • 5.5.3.4 Russia
    • 5.5.3.5 Rest of Europe
  • 5.5.4 Asia-Pacific
    • 5.5.4.1 China
    • 5.5.4.2 Japan
    • 5.5.4.3 India
    • 5.5.4.4 South Korea
    • 5.5.4.5 Rest of Asia-Pacific
  • 5.5.5 Middle East and Africa
    • 5.5.5.1 Middle East
      • 5.5.5.1.1 Saudi Arabia
      • 5.5.5.1.2 UAE
      • 5.5.5.1.3 Turkey
    • 5.5.5.2 Africa
      • 5.5.5.2.1 South Africa
      • 5.5.5.2.2 Rest of Africa

6 COMPETITIVE LANDSCAPE

• 6.1 Market Share Analysis
• 6.2 Company Profiles (includes Global level Overview, Market level overview, Core Segments, Financials as available, Strategic Information, Market Rank/Share for key companies, Products and Services, and Recent Developments)
  • 6.2.1 NVIDIA Corporation
  • 6.2.2 Intel Corporation
  • 6.2.3 Advanced Micro Devices Inc.
  • 6.2.4 Google LLC (TPU)
  • 6.2.5 Amazon Web Services Inc.
  • 6.2.6 Qualcomm Technologies Inc.
  • 6.2.7 Xilinx Inc. (AMD)
  • 6.2.8 Achronix Semiconductor Corp.
  • 6.2.9 NEC Corporation
  • 6.2.10 Dell Technologies Inc.
  • 6.2.11 IBM Corporation
  • 6.2.12 Cisco Systems Inc.
  • 6.2.13 Huawei Technologies Co. Ltd.
  • 6.2.14 Alibaba Cloud (Hanguang)
  • 6.2.15 Baidu Inc. (Kunlun)
  • 6.2.16 Graphcore Ltd.
  • 6.2.17 Cerebras Systems Inc.
  • 6.2.18 Tenstorrent Inc.
  • 6.2.19 Ampere Computing LLC
  • 6.2.20 Arm Ltd.
  • 6.2.21 Broadcom Inc.
  • 6.2.22 Marvell Technology Inc.
  • 6.2.23 Samsung Electronics Co. Ltd.
  • 6.2.24 Fujitsu Ltd.

7 MARKET OPPORTUNITIES AND FUTURE OUTLOOK

• 7.1 White-space and Unmet-Need Assessment