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

According to Mordor Intelligence, the north america data center GPU market size is expected to increase from USD 24.89 billion in 2026 to USD 43.88 billion by 2031, growing at a CAGR of 12.01% over 2026-2031.

This report is Segmented by Deployment Type (Cloud Data Centers, and More), GPU Type (Training GPUs and Inference GPUs), Interconnect (PCIe-Based GPUs and High-Bandwidth Interconnect GPUs), Workload Type (AI and ML, HPC, and More), End-User (Hyperscalers/CSPs, Enterprises, and More), and by Country (United States, Canada, and More). The Market Forecasts are Provided in Value (USD).

North America Data Center GPU Market Trends and Insights

Surging AI and ML Training Workloads in Hyperscale Data Centers

Hyperscalers are now training trillion-parameter frontier models on clusters with more than 100,000 GPUs, a scale unlocked by NVLink fabrics that reduce all-reduce latency from minutes to seconds.Record revenue at a leading GPU vendor in 2025 underscored a demand cycle fueled by model budgets surpassing USD 100 million per run. Public-sector projects such as Solstice and Equinox are adopting 10,000-plus GPU clusters for climate models, reinforcing long-term visibility for suppliers. Operators increasingly factor test-time compute into capacity planning, effectively doubling life-cycle GPU requirements as inference budgets grow to parity with training allocations. The resulting pull-through effect keeps advanced-node fabs fully allocated and intensifies competition for HBM capacity.

Growing Adoption of Hybrid Cloud Strategies Among Fortune 500 Enterprises

Enterprises are repatriating AI workloads to on-premises GPU stacks to control proprietary data and avoid cloud egress fees that can top 30% of total spend. Turnkey private-cloud-AI appliances with 4-64 GPUs and SaaS-like management are enabling firms in pharmaceuticals, automotive, and media to fine-tune LLMs behind their firewalls. The hybrid model is underpinned by mature virtualization, with vGPU 19.0 supporting 48 virtual machines per Blackwell GPU and slicing accelerators for multiple business units. During seasonal peaks, overflow jobs burst into CSP capacity, preserving agility without long-term public-cloud lock-in. This fluidity in workload is expanding the addressable market for mid-sized data centers and fueling demand for GPU leasing.

Persistent Semiconductor Supply-Chain Constraints for Advanced Nodes

Lead times for Blackwell and Rubin GPUs now exceed 50 weeks as advanced packaging remains supply-constrained. CoWoS capacity is short of demand, and HBM3E supply is trailing orders through 2026. Vendors are responding with United States fab expansions, but ramp timelines limit near-term relief, forcing hyperscalers into multi-billion-dollar pre-purchase agreements and equity-linked deals. Meta's 6 GW Instinct commitment secured warrants for AMD shares, illustrating how customers leverage balance-sheet capacity to lock in allocation. Start-ups without similar negotiating leverage face prolonged qualification cycles and postponed revenue.

Other drivers and restraints analyzed in the detailed report include:

1. Accelerated Deployment of Generative-AI-Optimized GPU Instances by CSPs
2. Expansion of Sovereign Cloud Regions Demanding On-Prem GPU Capacity
3. Rising Data Center Electricity Tariffs and Carbon-Emission Regulations

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

Segment Analysis

Cloud facilities dominated the North America data center GPU market in 2025, accounting for 58.90% share, yet edge nodes will compound at a 13.89% CAGR to 2031 as conversational AI, AR, and autonomous-vehicle inference shift closer to users. The North America data center GPU market size for edge deployments is climbing as telecom carriers deploy 10-50 GPU pods in central offices, shaving latency by double-digit milliseconds. Liquid-cooled micro-modules help meet noise and heat limits in retail and campus environments, while improved orchestration lets operators partition GPUs for bursty multi-tenant traffic.

Edge expansion reflects both economics and physics. Backhauling terabytes of sensor and video data to centralized clusters costs more than placing GPU capacity on-site, especially in Canada, where long-haul bandwidth pricing remains high. Multi-tenant vGPU slicing enables fractional consumption models that attract SMB developers. Meanwhile, hyperscaler outposts such as AWS Local Zones and Azure Edge Zones extend cloud management to regional POPs, blending cloud tools with edge sovereignty. Together, these factors propel edge nodes from pilot to production scale throughout the forecast window.

Training GPUs accounted for 57.82% of 2025 revenue, but inference accelerators will outpace it at a 13.45% CAGR as post-training compute budgets rise. The North America data center GPU market share for inference hardware is widening thanks to FP4 engines in Blackwell, 288 GB HBM3E on MI355X, and Gaudi 3's price-performance profile. Enterprises favor inference GPUs that cut watt-hours per generated token by half, improving TCO under carbon caps.

Architectural convergence blurs boundaries between training and serving. Unified GPU clusters now reconfigure on demand, with Kubernetes scheduling HBM-rich nodes for few-shot fine-tuning by day and high-throughput inference overnight. Test-time compute, chain-of-thought prompting, and RLHF loops increase inference cycles per user query, driving demand parity with training within three years. Consequently, vendors are optimizing memory bandwidth and scheduler microcode for real-time serving, redefining performance metrics around tokens per joule rather than pure FLOPs.

List of Companies Covered in this Report:

1. NVIDIA Corporation
2. Advanced Micro Devices Inc.
3. Intel Corporation
4. Graphcore Ltd.
5. Cerebras Systems Inc.
6. Tenstorrent Inc.
7. Qualcomm Technologies Inc.
8. Samsung Electronics Co., Ltd.
9. Huawei Technologies Co., Ltd.
10. Broadcom Inc.
11. Marvell Technology Inc.
12. Super Micro Computer Inc.
13. Dell Technologies Inc.
14. Hewlett Packard Enterprise Company

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 and ML training workloads in hyperscale data centers
  • 4.2.2 Growing adoption of hybrid cloud strategies among Fortune 500 enterprises
  • 4.2.3 Accelerated deployment of generative AI-optimized GPU instances by CSPs
  • 4.2.4 Expansion of sovereign cloud regions demanding on-prem GPU capacity
  • 4.2.5 Rapid emergence of GPU disaggregation and composable infrastructure
  • 4.2.6 Availability of energy-efficient liquid-cooled GPU servers lowering TCO
• 4.3 Market Restraints
  • 4.3.1 Persistent semiconductor supply-chain constraints for advanced nodes
  • 4.3.2 Rising data-center electricity tariffs and carbon-emission regulations
  • 4.3.3 Capital-expenditure freeze among SMBs owing to macro uncertainty
  • 4.3.4 Vendor lock-in risks tied to proprietary GPU software ecosystems
• 4.4 Industry Value Chain Analysis
• 4.5 Regulatory Landscape
• 4.6 Technological Outlook
• 4.7 Impact of Macroeconomic Factors on the Market
• 4.8 Porter's Five Forces Analysis
  • 4.8.1 Threat of New Entrants
  • 4.8.2 Bargaining Power of Suppliers
  • 4.8.3 Bargaining Power of Buyers
  • 4.8.4 Threat of Substitutes
  • 4.8.5 Industry 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 Country
  • 5.6.1 United States
  • 5.6.2 Canada
  • 5.6.3 Mexico

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 Graphcore Ltd.
  • 6.4.5 Cerebras Systems Inc.
  • 6.4.6 Tenstorrent Inc.
  • 6.4.7 Qualcomm Technologies Inc.
  • 6.4.8 Samsung Electronics Co., Ltd.
  • 6.4.9 Huawei Technologies Co., Ltd.
  • 6.4.10 Broadcom Inc.
  • 6.4.11 Marvell Technology Inc.
  • 6.4.12 Super Micro Computer Inc.
  • 6.4.13 Dell Technologies Inc.
  • 6.4.14 Hewlett Packard Enterprise Company

7 MARKET OPPORTUNITIES AND FUTURE OUTLOOK

• 7.1 White-Space and Unmet-Need Assessment