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

According to Mordor Intelligence, the GPU cloud market size is expected to increase from USD 7.73 billion in 2025 to USD 15.62 billion in 2026 and reach USD 37.69 billion by 2031, growing at a CAGR of 19.26% over 2026-2031.

This report is Segmented by Service Model (IaaS, and PaaS), GPU Workload Class (AI Training and Large-Scale HPC GPU Instances, and More), Hosting Model (Hosted Private GPU Cloud, and More), Organization Size (SMEs, and More), Application (AI Training and Fine-Tuning, and More), End-User (IT, Telecom, Software and Internet Platforms, and More), and Geography. The Market Forecasts are Provided in Terms of Value (USD).

Global GPU Cloud Market Trends and Insights

Rising Generative AI And LLM Training Demand

Generative AI and large language model development remain the central demand engine for the GPU cloud market. Each new model generation requires larger training clusters, denser networking, and more memory per deployment, which pushes providers to secure capacity earlier and on longer terms. This has increased the advantage of operators that already control large GPU estates and can deliver tightly integrated training environments. The GPU cloud market is also becoming more concentrated at the top of the training stack because very large model builds need specialized infrastructure that only a limited number of providers can assemble at scale. CoreWeave's public filings and operating disclosures showed how access to large GPU fleets and large data center commitments became a defining competitive asset for training-focused providers in this market. CoreWeave's March 2025 IPO announcement further showed that investors viewed large-scale AI compute capacity as a durable growth category rather than a short-lived build cycle.

Growth In Agentic AI Inference Workloads

The GPU cloud market is also being pushed forward by a rapid increase in agentic AI inference workloads. Production agents do more than answer prompts because they plan actions, retrieve context, invoke tools, and evaluate outputs across multiple cycles for a single task. That pattern keeps inference clusters active for longer periods and raises the value of low-latency serving capacity inside the GPU cloud market. NVIDIA leadership stated in 2025 that agentic inference can require far more compute than early generative AI systems, which supports the expectation of heavier serving demand over time. CoreWeave's May 2026 launch of a unified agentic AI platform showed how providers are linking reinforcement learning, production inference, observability, and continuous improvement into one managed workflow. As this operating model spreads, the GPU cloud market is shifting toward more persistent inference demand and away from a purely training-led utilization curve.

HBM And Advanced Packaging Supply Constraints

High-bandwidth memory remains the clearest physical bottleneck for the GPU cloud market because modern AI accelerators depend on it for performance at scale. When memory supply tightens, cloud providers cannot expand deployable capacity at the same pace as demand, even if data center space and buyer interest remain strong. This pressure is amplified by advanced packaging constraints, which slow the conversion of chip demand into usable systems. Reporting tied to AMD leadership in 2026 noted that HBM demand growth was outpacing supply growth, while major suppliers had already sold through their 2026 HBM3E output. The GPU cloud market, therefore, rewards providers with long-term allocation relationships because supply access is functioning as a competitive moat rather than a normal procurement input. This constraint also limits how quickly new entrants can challenge established operators in the highest-value parts of the GPU cloud market.

Other drivers and restraints analyzed in the detailed report include:

1. Enterprise Shift Toward Elastic Pay-Per-Use GPU Capacity
2. Sovereign AI And Data Residency Requirements
3. GPU Spot Price Volatility And Capacity Hoarding

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

Segment Analysis

Infrastructure as a Service accounted for 78.66% of the GPU cloud market in 2025, which made it the dominant service layer for buyers that wanted direct control over compute, networking, and memory policies. That position reflected the early maturity of the GPU cloud market, where many large customers still preferred to assemble and tune environments themselves. Raw GPU access remained attractive for training-heavy users who needed flexibility across frameworks, cluster designs, and scaling rules. The service mix also showed that most spending still sat closest to the infrastructure layer, even as buyer expectations were beginning to change.

Platform as a Service is projected to grow at a 19.32% CAGR through 2031, which points to a steady narrowing between raw capacity and managed AI environments. The GPU cloud market is moving in this direction because enterprises increasingly need orchestration, observability, reinforcement learning workflows, and model serving within one operating layer. CoreWeave's May 2026 rollout of unified agentic AI capabilities illustrated how providers are folding training and inference operations into a closed improvement loop rather than offering compute alone. This means the GPU cloud market is not replacing IaaS, but it is adding more value above it as customers push for faster deployment and lower engineering overhead. Over time, providers that combine strong infrastructure with usable platform tooling should hold a more durable position than providers that compete on GPU access alone.

AI Training and Large-Scale HPC GPU Instances held a 62.34% share of the GPU cloud market by workload class in 2025. That lead reflected the heavy concentration of spending around frontier model development, large enterprise fine-tuning programs, and research computing projects that still required large training clusters. In the earlier phase of the GPU cloud market, training demand shaped how providers built data center footprints, interconnect designs, and capacity planning models. Those workloads remain central because they still consume dense, high-value compute over defined project windows. Training also continues to anchor provider reputation because customers often judge platform strength by how well it supports demanding model development tasks.

AI Inference and General Accelerated Compute GPU Instances are projected to grow at a 19.41% CAGR through 2031, which marks a clear change in where the GPU cloud market will spend more time and capacity. Once models move into production, they create continuous serving demand with tighter latency requirements and longer utilization cycles. That changes the economics of the GPU cloud market because inference can accumulate more total compute-hours over a model's operating life than a single training run. Research presented at ECRTS in 2025 on hardware compute partitioning for NVIDIA GPUs pointed to more efficient scheduling approaches that can improve utilization across varied workload profiles. As production AI expands, providers in the GPU cloud market will need to balance training credibility with strong inference architecture and scheduling discipline.

Complete Report Scope:

• By Service Model
  • Infrastructure as a Service
  • Platform as a Service
• By GPU Workload Class
  • AI Training and Large-Scale HPC GPU Instances
  • AI Inference and General Accelerated Compute GPU Instances
  • Graphics, Visualization, Rendering, and VDI GPU Instances
  • Cost-Optimized and Legacy GPU Instances
• By Hosting Model
  • Shared Multi-Tenant Public GPU Cloud
  • Dedicated Single-Tenant GPU Cloud
  • Hosted Private GPU Cloud
  • Sovereign and Regulated GPU Cloud
• By Organization Size
  • Large Enterprises
  • Small and Medium Enterprises
  • Research Institutions, Academia, and Public-Sector Organizations
• By Application
  • AI Training and Fine-Tuning
  • AI Inference and Model Serving
  • High-Performance Computing and Scientific Simulation
  • Rendering, Animation, VFX, and Virtual Production
  • Cloud Gaming
  • Others (Visualization, Virtual Workstations, and Digital Twins)
• By End-User Industry
  • IT, Telecom, Software, and Internet Platforms
  • Media, Entertainment, Gaming, and Advertising
  • BFSI
  • Automotive, Mobility, and Autonomous Systems
  • Healthcare, Life Sciences, and Pharmaceuticals
  • Manufacturing, Semiconductor, and Industrial
  • Other End-User Industries (Retail and E-Commerce, Energy and Utilities)
• 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 72.76% of the GPU cloud market in 2025, which made it the clear center of current global demand. The region benefits from the concentration of frontier AI labs, major hyperscalers, deep private capital pools, and a large base of enterprise AI adopters. In the GPU cloud market, this creates a reinforcing cycle where providers, buyers, and technical talent remain close to one another and shorten the path from infrastructure buildout to commercial use. Canada and Mexico also support the regional position through data center expansion, cross-border provisioning, and adjacency to the United States demand patterns.

Europe held the second-largest share of the GPU cloud market, and its growth path is being shaped less by raw scale and more by sovereignty requirements. Demand in the region is being pulled by compliance expectations tied to data residency, regulated AI use, and the need for stronger local control over infrastructure. This favors providers that can combine meaningful capacity with regional trust and certification positioning. Deutsche Telekom's Munich AI factory plan showed how European incumbents are building large domestic GPU estates to support industrial and regulated use cases. Nebius also announced in June 2026 that it would invest approximately GBP 1.7 billion, approximately USD 2.16 billion, in new NVIDIA-powered infrastructure deployments in the United Kingdom. These moves show that the GPU cloud market in Europe is being built around local capacity relevance rather than volume alone.

Asia-Pacific is projected to grow at a 19.68% CAGR through 2031, which makes it the fastest-growing regional segment in the GPU cloud market. Growth is being supported by expanding domestic AI programs, rising enterprise adoption, and the need for local infrastructure that can support national and regional data requirements. Microsoft's USD 10 billion commitment in Japan in April 2026 underscored the scale of regional investment now flowing into AI infrastructure, cybersecurity, and talent capacity. South America and the Middle East and Africa remain earlier-stage parts of the GPU cloud market, but they are developing as selective growth zones where local hosting demand and sovereign compute ambitions are starting to attract more infrastructure attention.

1. CoreWeave, Inc.
2. Lambda, Inc.
3. RunPod, Inc.
4. Vast.ai, Inc.
5. Nebius Group N.V.
6. Crusoe Energy Systems LLC
7. FluidStack Limited
8. Voltage Park, Inc.
9. WhiteFiber LLC
10. Denvr Dataworks Corp.
11. Genesis Cloud GmbH
12. Scaleway S.A.S.
13. OVH SAS
14. The Constant Company, LLC
15. Northern Data AG
16. DigitalOcean, LLC
17. Cirrascale Cloud Services, Inc.
18. TensorWave, Inc.
19. NexGen Cloud Limited
20. Jarvislabs.ai Private Limited

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 Rising Generative AI and LLM Training Demand
  • 4.3.2 Growth in Agentic AI Inference Workloads
  • 4.3.3 Expansion of GPU-Heavy Cloud Gaming and Real-Time Rendering
  • 4.3.4 Enterprise Shift Toward Elastic Pay-Per-Use GPU Capacity
  • 4.3.5 Sovereign AI and Data Residency Requirements
  • 4.3.6 Fractional GPU Scheduling and Composable GPU Fabrics
• 4.4 Market Restraints
  • 4.4.1 HBM and Advanced Packaging Supply Constraints
  • 4.4.2 GPU Spot Price Volatility and Capacity Hoarding
  • 4.4.3 Energy and Cooling Intensity of Dense GPU Racks
  • 4.4.4 Customer Lock-In Around Specialized AI Toolchains
• 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 Suppliers
  • 4.8.3 Bargaining Power of Buyers
  • 4.8.4 Threat of Substitutes
  • 4.8.5 Competitive Rivalry

5 MARKET SIZE AND GROWTH FORECASTS (VALUE)

• 5.1 By Service Model
  • 5.1.1 Infrastructure as a Service
  • 5.1.2 Platform as a Service
• 5.2 By GPU Workload Class
  • 5.2.1 AI Training and Large-Scale HPC GPU Instances
  • 5.2.2 AI Inference and General Accelerated Compute GPU Instances
  • 5.2.3 Graphics, Visualization, Rendering, and VDI GPU Instances
  • 5.2.4 Cost-Optimized and Legacy GPU Instances
• 5.3 By Hosting Model
  • 5.3.1 Shared Multi-Tenant Public GPU Cloud
  • 5.3.2 Dedicated Single-Tenant GPU Cloud
  • 5.3.3 Hosted Private GPU Cloud
  • 5.3.4 Sovereign and Regulated GPU Cloud
• 5.4 By Organization Size
  • 5.4.1 Large Enterprises
  • 5.4.2 Small and Medium Enterprises
  • 5.4.3 Research Institutions, Academia, and Public-Sector Organizations
• 5.5 By Application
  • 5.5.1 AI Training and Fine-Tuning
  • 5.5.2 AI Inference and Model Serving
  • 5.5.3 High-Performance Computing and Scientific Simulation
  • 5.5.4 Rendering, Animation, VFX, and Virtual Production
  • 5.5.5 Cloud Gaming
  • 5.5.6 Others (Visualization, Virtual Workstations, and Digital Twins)
• 5.6 By End-User Industry
  • 5.6.1 IT, Telecom, Software, and Internet Platforms
  • 5.6.2 Media, Entertainment, Gaming, and Advertising
  • 5.6.3 BFSI
  • 5.6.4 Automotive, Mobility, and Autonomous Systems
  • 5.6.5 Healthcare, Life Sciences, and Pharmaceuticals
  • 5.6.6 Manufacturing, Semiconductor, and Industrial
  • 5.6.7 Other End-User Industries (Retail and E-Commerce, Energy and Utilities)
• 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 Positioning 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 CoreWeave, Inc.
  • 6.4.2 Lambda, Inc.
  • 6.4.3 RunPod, Inc.
  • 6.4.4 Vast.ai, Inc.
  • 6.4.5 Nebius Group N.V.
  • 6.4.6 Crusoe Energy Systems LLC
  • 6.4.7 FluidStack Limited
  • 6.4.8 Voltage Park, Inc.
  • 6.4.9 WhiteFiber LLC
  • 6.4.10 Denvr Dataworks Corp.
  • 6.4.11 Genesis Cloud GmbH
  • 6.4.12 Scaleway S.A.S.
  • 6.4.13 OVH SAS
  • 6.4.14 The Constant Company, LLC
  • 6.4.15 Northern Data AG
  • 6.4.16 DigitalOcean, LLC
  • 6.4.17 Cirrascale Cloud Services, Inc.
  • 6.4.18 TensorWave, Inc.
  • 6.4.19 NexGen Cloud Limited
  • 6.4.20 Jarvislabs.ai Private Limited

7 MARKET OPPORTUNITIES AND FUTURE OUTLOOK

• 7.1 White-space and Unmet-Need Assessment