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

According to Mordor Intelligence, the GPU middleware market size is expected to increase from USD 2.69 billion in 2025 to USD 3.64 billion in 2026 and reach USD 14.11 billion by 2031, growing at a CAGR of 31.12% over 2026-2031.

This report is Segmented by Component (Software, and Services), Deployment Mode (Cloud, On-Premises, Hybrid, and Edge/Embedded), Enterprise Size (Large Enterprises, and Small and Medium Enterprises), Application (Virtual Desktop Infrastructure and Remote Workstations, and More), End-User Industry (Healthcare and Life Sciences, and More), and Geography. The Market Forecasts are Provided in Terms of Value (USD).

Global GPU Middleware Market Trends and Insights

Rising AI Model Training and Inference GPU Density

The GPU middleware market is rising as AI model development increasingly depends on larger, denser compute environments. NVIDIA presented the Vera Rubin platform in June 2026 as a rack-scale system that combines CPU, GPU, networking, and software for science and AI workloads, which shows how tightly integrated next-generation deployments are becoming. Microsoft also announced a new datacenter campus in Pecos, Texas, in June 2026 to add around 2 gigawatts of AI and cloud capacity, reflecting how quickly infrastructure expansion continues in the core buyer base. NVIDIA and Eli Lilly also formed a co-innovation AI lab in January 2026, with up to USD 1 billion in planned investment over 5 years, indicating that large model workloads are spreading into highly regulated commercial settings. As these clusters grow, the operational load on schedulers, memory control layers, and resource allocation tools also rises. That pattern keeps the GPU middleware market closely tied to every new wave of AI training and scale-up in inference.

Growing Enterprise Demand for Multi-Tenant GPU Resource Sharing

The GPU middleware market is also gaining from the need to share limited GPU resources across teams, models, and workloads without losing control. IBM Research, Red Hat, and NxtGen Cloud reported in June 2026 that the open-source llm-d framework delivered 3 to 5 times faster inference and doubled throughput on mixed GPU hardware, while indicating potential annual savings of up to USD 5.25 million per large deployment. NVIDIA also moved its Dynamic Resource Allocation driver for GPUs into the Kubernetes community and onboarded the KAI Scheduler as a CNCF Sandbox project in March 2026, enabling wider use of shared, policy-driven GPU scheduling. Red Hat is built in the same direction as its AI Factory with NVIDIA, which offers pooled access, intelligent orchestration, and automatic checkpointing for long-running jobs. These moves reduce the need for a single application or team to reserve entire clusters for long periods. That supports the GPU middleware market because multi-tenant control becomes a central buying criterion rather than an optional feature.

High GPU Infrastructure Cost and Integration Complexity

The GPU middleware market still faces a clear barrier: scaling GPU infrastructure requires significant upfront investment and careful integration. CoreWeave closed a USD 8.5 billion financing facility in March 2026, followed by a further USD 3.1 billion loan facility in May 2026, underscoring how capital-intensive GPU platform expansion has become, even for specialized providers. Microsoft's June 2026 Pecos announcement also underlined that major AI capacity additions now happen at a very large scale, which smaller buyers cannot easily match. On the deployment side, Red Hat's AI Factory with NVIDIA was validated across hardware from Cisco, Dell Technologies, Lenovo, and Supermicro, which shows that real-world implementation often requires tested combinations across multiple layers. HPE's AI Factory portfolio follows the same logic by packaging orchestration, tenancy, and enterprise software into certified systems. Until these deployments become easier and cheaper to stand up, the GPU middleware market will continue to face slower adoption.

Other drivers and restraints analyzed in the detailed report include:

1. Increasing Need for GPU Utilization Optimization in Cloud and On-Premises Environments
2. Expansion of High-Performance Computing Workloads Across Industries
3. Limited Talent for GPU Orchestration, CUDA Tuning, and Cluster Optimization

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

Segment Analysis

Software accounted for 74.28% of revenue in 2025, while services are forecast to grow at a 32.56% CAGR through 2031. This revenue split shows that buyers still spend most heavily on schedulers, virtualization tools, and runtime software when they first build out GPU environments. The current mix also reflects how the GPU middleware market developed around platform control, cluster management, and container-ready software layers. NVIDIA's decision to open-source the KAI Scheduler and donate the Dynamic Resource Allocation driver for GPUs in 2026 reinforced software's central position in the stack. Red Hat's general availability release of Dynamic Resource Allocation in OpenShift 4.21 also highlighted a maturing software layer that is becoming more standardized across enterprise deployments.

Services, however, are expanding faster because deployment complexity is now harder to absorb solely through software. Red Hat AI Factory with NVIDIA includes GPU-as-a-Service orchestration, pooled access, and automatic checkpointing, and that kind of rollout usually requires structured implementation support and operating guidance. HPE also pushed the same direction through its AI Factory portfolio, where Mission Control software and hybrid deployment support are packaged with enterprise infrastructure services. Anyscale's June 2026 release of Ray Data with NVIDIA cuDF support showed that even cost and performance gains at the workload layer still depend on strong integration into broader operating environments. In practice, the faster growth of services suggests that the GPU middleware market is moving from software purchase decisions toward software plus deployment outcomes. That shift should keep service-heavy engagements important as organizations move from pilot clusters to production-scale estates.

Cloud represented 52.41% of revenue in 2025, while hybrid is projected to record the fastest growth at a 31.96% CAGR through 2031. That split shows that the largest share of current spending still goes to managed GPU access, which lets enterprises get started quickly without building dedicated infrastructure first. At the same time, the faster hybrid growth rate indicates that the GPU middleware market is moving toward mixed operating models rather than a cloud-only default. This pattern meets enterprise needs by combining burst training, internal data control, and varying latency requirements across workloads. It also reflects the fact that cloud and on-premises environments are being treated less as alternatives and more as interconnected parts of a single operating stack.

Vendor moves support that direction. Red Hat's AI Factory with NVIDIA was built around pooled access and orchestration across enterprise environments, which fits organizations that want internal control without giving up flexible resource sharing. HPE's March 2026 AI Factory updates also highlighted multi-scale tenancy, Mission Control integration, and Red Hat OpenShift support for hybrid AI deployments. Microsoft's April 2026 announcement on Japan showed how in-country AI infrastructure and local compute services remain important where data residency matters. The GPU middleware market is therefore seeing hybrid gain traction because it gives buyers policy control, workload flexibility, and more room to align infrastructure with internal governance. Edge and embedded deployments remain smaller, but the same hybrid logic is starting to influence real-time use cases in automotive and industrial settings.

Complete Report Scope:

• By Component
  • Software
  • Services
• By Deployment Mode
  • Cloud
  • On-Premises
  • Hybrid
  • Edge/Embedded
• By Enterprise Size
  • Large Enterprises
  • Small and Medium Enterprises
• By Application
  • Artificial Intelligence and Machine Learning
  • High-Performance Computing and Scientific/Engineering Simulation
  • Data Analytics, Databases and Graph Processing
  • Graphics, Visualization and Rendering
  • Virtual Desktop Infrastructure and Remote Workstations
  • Digital Twins and Industrial Simulation
• By End-User Industry
  • Cloud Service Providers, Hyperscalers and Data Center Operators
  • Information Technology and Telecommunications
  • Banking, Financial Services and Insurance
  • Healthcare and Life Sciences
  • Media and Entertainment
  • Automotive
  • Manufacturing
  • Other End-User Industries
• 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 accounted for 43.72% of global revenue in 2025, making it the largest regional contributor to the GPU middleware market. This lead reflects the region's dense concentration of hyperscaler campuses, AI software vendors, and enterprise GPU deployments. Microsoft announced a new datacenter campus in Pecos, Texas, in June 2026, adding around 2 gigawatts of AI and cloud capacity, which reinforced North America's role as the largest infrastructure build zone in the current cycle. NVIDIA also invested USD 2 billion in CoreWeave in January 2026 as part of an expanded collaboration to accelerate more than 5 gigawatts of AI factory capacity by 2030. These moves support the region's scale advantage because infrastructure, software, and service layers are being expanded together. South America remained smaller, but demand continued to build through managed cloud access and regional enterprise modernization programs. In practical terms, North America still sets the pace for product maturity, deployment scale, and vendor alignment in the GPU middleware market.

Europe is developing the GPU middleware market through a different path that places more weight on governance, sovereignty, and enterprise control. The region's demand pattern favors cloud-agnostic and on-premises deployment models in workloads that involve compliance-sensitive data. France's national AI strategy identified GPU middleware innovation as a priority area for support, which showed that the software coordination layer is being treated as strategically important rather than secondary. This policy backdrop supports steady demand for orchestration tools that can fit localized deployment choices and stricter operating requirements. As a result, Europe contributes to the GPU middleware market less through sheer scale and more through the push for controllable and sovereign operating models.

Asia-Pacific is the fastest-growing region, with the GPU middleware market size in this geography projected to advance at a 32.15% CAGR through 2031. The region is benefiting from sovereign AI investment, expanding enterprise demand, and a wider push for in-country compute capability. Microsoft announced a USD 10 billion investment in Japan in April 2026, including work with Sakura Internet and SoftBank to provide GPU-based AI compute services with domestic data residency. That example captures the regional theme clearly, because growth is being driven not only by capacity additions but also by local control requirements. Asia-Pacific's faster pace means it is becoming a more important source of new contracts, especially where enterprises and public institutions want AI infrastructure within national boundaries. This momentum should keep the GPU middleware market geographically more balanced over time, even though North America still leads in current revenue.

1. NVIDIA Corporation
2. Amazon Web Services, Inc.
3. Microsoft Corporation
4. International Business Machines Corporation
5. Oracle Corporation
6. Red Hat, Inc.
7. Hewlett Packard Enterprise Company
8. CoreWeave, Inc.
9. RunPod Inc.
10. Rafay Systems, Inc.
11. DigitalOcean, LLC
12. Crusoe Energy Systems LLC
13. Fujitsu Limited
14. Lenovo Group Limited
15. OVH SAS
16. Scaleway SAS
17. The Constant Company, LLC
18. Linode, LLC
19. Civo Limited
20. Anyscale, Inc.
21. Modal Labs, 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 Rising AI Model Training and Inference GPU Density
  • 4.2.2 Growing Enterprise Demand for Multi-Tenant GPU Resource Sharing
  • 4.2.3 Increasing Need for GPU Utilization Optimization in Cloud and On-Premises Environments
  • 4.2.4 Expansion of High-Performance Computing Workloads Across Industries
  • 4.2.5 Rising Adoption of Containerized AI and MLOps Workflows
  • 4.2.6 Greater Use of Hybrid and Multi-Cloud GPU Infrastructure
• 4.3 Market Restraints
  • 4.3.1 High GPU Infrastructure Cost and Integration Complexity
  • 4.3.2 Limited Talent for GPU Orchestration, CUDA Tuning, and Cluster Optimization
  • 4.3.3 Power, Cooling, and Rack Density Constraints in Legacy Data Centers
  • 4.3.4 Export Controls and Hardware Allocation Volatility for Advanced GPUs
• 4.4 Industry Value Chain Analysis
• 4.5 Regulatory Landscape
• 4.6 Technological Outlook
• 4.7 Porter's Five Forces Analysis
  • 4.7.1 Bargaining Power of Suppliers
  • 4.7.2 Bargaining Power of Buyers
  • 4.7.3 Threat of New Entrants
  • 4.7.4 Threat of Substitutes
  • 4.7.5 Competitive Rivalry

5 MARKET SIZE AND GROWTH FORECASTS (VALUE)

• 5.1 By Component
  • 5.1.1 Software
  • 5.1.2 Services
• 5.2 By Deployment Mode
  • 5.2.1 Cloud
  • 5.2.2 On-Premises
  • 5.2.3 Hybrid
  • 5.2.4 Edge/Embedded
• 5.3 By Enterprise Size
  • 5.3.1 Large Enterprises
  • 5.3.2 Small and Medium Enterprises
• 5.4 By Application
  • 5.4.1 Artificial Intelligence and Machine Learning
  • 5.4.2 High-Performance Computing and Scientific/Engineering Simulation
  • 5.4.3 Data Analytics, Databases and Graph Processing
  • 5.4.4 Graphics, Visualization and Rendering
  • 5.4.5 Virtual Desktop Infrastructure and Remote Workstations
  • 5.4.6 Digital Twins and Industrial Simulation
• 5.5 By End-User Industry
  • 5.5.1 Cloud Service Providers, Hyperscalers and Data Center Operators
  • 5.5.2 Information Technology and Telecommunications
  • 5.5.3 Banking, Financial Services and Insurance
  • 5.5.4 Healthcare and Life Sciences
  • 5.5.5 Media and Entertainment
  • 5.5.6 Automotive
  • 5.5.7 Manufacturing
  • 5.5.8 Other End-User Industries
• 5.6 By Geography
  • 5.6.1 North America
    • 5.6.1.1 United States
    • 5.6.1.2 Canada
    • 5.6.1.3 Mexico
  • 5.6.2 Europe
    • 5.6.2.1 Germany
    • 5.6.2.2 United Kingdom
    • 5.6.2.3 France
    • 5.6.2.4 Italy
    • 5.6.2.5 Rest of Europe
  • 5.6.3 Asia-Pacific
    • 5.6.3.1 China
    • 5.6.3.2 Japan
    • 5.6.3.3 South Korea
    • 5.6.3.4 India
    • 5.6.3.5 Southeast Asia
    • 5.6.3.6 Rest of Asia-Pacific
  • 5.6.4 South America
  • 5.6.5 Middle East and Africa

6 COMPETITIVE LANDSCAPE

• 6.1 Market Concentration
• 6.2 Strategic Moves
• 6.3 Vendor 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 NVIDIA Corporation
  • 6.4.2 Amazon Web Services, Inc.
  • 6.4.3 Microsoft Corporation
  • 6.4.4 International Business Machines Corporation
  • 6.4.5 Oracle Corporation
  • 6.4.6 Red Hat, Inc.
  • 6.4.7 Hewlett Packard Enterprise Company
  • 6.4.8 CoreWeave, Inc.
  • 6.4.9 RunPod Inc.
  • 6.4.10 Rafay Systems, Inc.
  • 6.4.11 DigitalOcean, LLC
  • 6.4.12 Crusoe Energy Systems LLC
  • 6.4.13 Fujitsu Limited
  • 6.4.14 Lenovo Group Limited
  • 6.4.15 OVH SAS
  • 6.4.16 Scaleway SAS
  • 6.4.17 The Constant Company, LLC
  • 6.4.18 Linode, LLC
  • 6.4.19 Civo Limited
  • 6.4.20 Anyscale, Inc.
  • 6.4.21 Modal Labs, Inc.

7 MARKET OPPORTUNITIES AND FUTURE OUTLOOK

• 7.1 White-Space and Unmet-Need Assessment