GPU as a Service (GPUaaS) Market Forecasts to 2034 – Global Analysis By Component (Hardware, Software, and Services), Deployment Model, Service Type, Organization Size, Application, End User and By Geography
Description
According to Stratistics MRC, the Global GPU as a Service (GPUaaS) Market is accounted for $5159.31 million in 2026 and is expected to reach $19393.13 million by 2034 growing at a CAGR of 18.0% during the forecast period. GPU as a Service (GPUaaS) is a cloud-based computing model that provides on-demand access to powerful graphics processing units through the internet. Instead of purchasing and maintaining expensive GPU hardware, users can rent GPU resources from cloud providers based on their workload needs. This model supports high-performance tasks such as artificial intelligence, machine learning, data analytics, scientific simulations, and graphics rendering. GPUaaS offers scalability, cost efficiency, and flexibility, enabling organizations to accelerate compute-intensive applications while focusing on innovation rather than infrastructure management.
Market Dynamics:
Driver:
Surge in generative AI & LLMs
The generative AI & LLMs models require immense computational power, which GPUs are uniquely suited to deliver at scale. Enterprises are increasingly leveraging GPUaaS to accelerate training and inference workloads without investing in costly on-premise infrastructure. The rise of applications such as conversational AI, image synthesis, and autonomous systems is intensifying GPU utilization. Cloud providers are expanding GPUaaS offerings to support diverse industries, from finance to entertainment. As organizations pursue innovation in AI-driven products, GPUaaS is becoming a critical enabler of competitive advantage. This surge in AI workloads is expected to remain the primary driver of market growth throughout the forecast period.
Restraint:
Data security & privacy concerns
Sensitive workloads in healthcare, finance, and government sectors often involve confidential datasets that organizations hesitate to process in shared cloud environments. Concerns around unauthorized access, data leakage, and compliance with regulations such as GDPR and HIPAA limit broader deployment. Cloud providers must invest heavily in encryption, secure multi-tenancy, and compliance certifications to reassure clients. Smaller enterprises may struggle to navigate complex regulatory landscapes, slowing their migration to GPUaaS platforms. The integration of AI into sensitive decision-making processes further amplifies the need for robust safeguards.
Opportunity:
Edge computing integration
By deploying GPU resources closer to data sources, latency can be reduced and real-time analytics enhanced. Industries such as autonomous vehicles, smart manufacturing, and healthcare diagnostics benefit from edge-enabled GPUaaS solutions. This convergence supports decentralized AI training and inference, enabling faster decision-making in mission-critical environments. Cloud providers are investing in hybrid architectures that combine centralized GPU clusters with distributed edge nodes. The rise of 5G networks further strengthens this opportunity by enabling seamless connectivity between edge devices and GPUaaS platforms. As edge computing adoption accelerates, GPUaaS providers can unlock new revenue streams and expand their customer base.
Threat:
Rising competition from custom ASICs
Tech giants and specialized startups are developing ASICs optimized for AI workloads, offering superior performance-per-watt compared to general-purpose GPUs. These alternatives threaten to erode GPUaaS demand, particularly in hyperscale data centers. ASICs also provide cost advantages for organizations running repetitive, large-scale AI tasks. However, GPUs retain flexibility across diverse workloads, which ASICs often lack. The challenge for GPUaaS providers lies in differentiating their offerings through scalability, accessibility, and ecosystem integration. Rising ASIC adoption underscores the need for GPUaaS platforms to continuously innovate and maintain relevance in a rapidly evolving hardware landscape.
Covid-19 Impact:
Lockdowns disrupted hardware supply chains, leading to shortages and delayed deployments of GPU clusters. At the same time, remote work and digital transformation accelerated demand for cloud-based AI services. Industries such as healthcare and life sciences leveraged GPUaaS for drug discovery, diagnostics, and pandemic modeling. The surge in online entertainment and e-commerce also boosted GPUaaS utilization for recommendation engines and content generation. Cloud providers responded by scaling infrastructure and offering flexible pricing models to meet rising demand. Post-pandemic strategies now emphasize resilience, distributed architectures, and automation across GPUaaS ecosystems.
The hardware segment is expected to be the largest during the forecast period
The hardware segment is expected to account for the largest market share during the forecast period, due to its foundational role in GPUaaS delivery. GPUs, servers, and networking equipment form the backbone of cloud-based AI infrastructure. Continuous innovation in GPU architectures, such as NVIDIA’s H100 and AMD’s MI300, is driving performance improvements. Hardware investments are critical for supporting increasingly complex AI workloads across industries. Cloud providers are expanding data center capacity to meet surging demand for GPUaaS services. The scalability and efficiency of hardware directly influence service quality and adoption rates.
The healthcare & life sciences segment is expected to have the highest CAGR during the forecast period
Over the forecast period, the healthcare & life sciences segment is predicted to witness the highest growth rate, due to its reliance on GPUaaS for advanced analytics. Applications such as genomics, drug discovery, and medical imaging require massive computational resources. GPUaaS enables researchers to accelerate simulations and improve diagnostic accuracy without heavy capital investment. The pandemic highlighted the importance of GPU-powered modeling in vaccine development and epidemiology. Hospitals and research institutions are increasingly adopting GPUaaS for AI-driven clinical decision support. Cloud providers are tailoring GPUaaS solutions to meet compliance requirements in healthcare.
Region with largest share:
During the forecast period, the North America region is expected to hold the largest market share, due to its technological leadership and strong cloud ecosystem. The U.S. hosts major GPUaaS providers such as AWS, Microsoft Azure, and Google Cloud. Robust investments in AI R&D and enterprise digital transformation are driving adoption. North America’s healthcare, finance, and automotive industries are early adopters of GPUaaS solutions. Favorable regulatory frameworks and advanced infrastructure further support market expansion. Strategic partnerships between cloud providers and enterprises are accelerating innovation in GPUaaS applications.
Region with highest CAGR:
Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR, owing to rapid digitalization and expanding AI adoption. Countries such as China, India, and Japan are investing heavily in cloud infrastructure and GPU clusters. Government initiatives promoting AI innovation and smart city projects are boosting demand for GPUaaS. The region’s growing startup ecosystem is leveraging GPUaaS for scalable AI development. Rising internet penetration and 5G rollout are enabling new GPUaaS applications in e-commerce, gaming, and mobility. Local cloud providers are partnering with global players to expand service availability.
Key players in the market
Some of the key players in GPU as a Service (GPUaaS) Market include NVIDIA Corporation, Fujitsu, Amazon Web Services (AWS), Baidu AI Cloud, Microsoft Corporation, DigitalOcean Holdings, Google Cloud, Vultr, IBM Corporation, Lambda Labs, Oracle Corporation, CoreWeave, Inc., Alibaba, Rescale, and Tencent.
Key Developments:
In January 2026, NVIDIA and CoreWeave, Inc. announced an expansion of their long-standing complementary relationship to enable CoreWeave to accelerate the buildout of more than 5 gigawatts of AI factories by 2030 to advance AI adoption at global scale. NVIDIA has invested $2 billion in CoreWeave Class A common stock at a purchase price of $87.20 per share. The investment reflects NVIDIA’s confidence in CoreWeave’s business, team and growth strategy as a cloud platform built on NVIDIA infrastructure.
In January 2026, Datavault AI Inc. announced it will deliver enterprise-grade AI performance at the edge in New York and Philadelphia through an expanded collaboration with IBM (NYSE: IBM) using the SanQtum AI platform. Operated by Available Infrastructure, SanQtum AI is a fleet of synchronized micro edge data centers running IBM’s watsonx portfolio of AI products on a zero-trust network. The combined deployment is designed to enable cybersecure data storage and compute.
Components Covered:
• Hardware
• Software
• Services
Deployment Models Covered:
• Public Cloud
• Private Cloud
• Hybrid Cloud
Service Types Covered:
• On-Demand GPUaaS
• Reserved/Subscription GPUaaS
• Pay-Per-Use GPUaaS
Organization Sizes Covered:
• Large Enterprises
• Small & Medium-Sized Enterprises (SMEs)
Applications Covered:
• Artificial Intelligence (AI) & Machine Learning
• Data Analytics
• Gaming & Graphics Rendering
• High-Performance Computing (HPC)
• Autonomous Vehicles
• Virtual Reality (VR) & Augmented Reality (AR)
• Media
• Other Applications
End Users Covered:
• IT & Telecom
• Healthcare & Life Sciences
• Automotive
• Entertainment
• Government & Defense
• Education & Research
• Retail & E-Commerce
• Financial Services
• Energy & Utilities
Regions Covered:
• North America
US
Canada
Mexico
• Europe
Germany
UK
Italy
France
Spain
Rest of Europe
• Asia Pacific
Japan
China
India
Australia
New Zealand
South Korea
Rest of Asia Pacific
• South America
Argentina
Brazil
Chile
Rest of South America
• Middle East & Africa
Saudi Arabia
UAE
Qatar
South Africa
Rest of Middle East & Africa
What our report offers:
- Market share assessments for the regional and country-level segments
- Strategic recommendations for the new entrants
- Covers Market data for the years 2023, 2024, 2025, 2026, 2027, 2028, 2030, 2032 and 2034
- Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
- Strategic recommendations in key business segments based on the market estimations
- Competitive landscaping mapping the key common trends
- Company profiling with detailed strategies, financials, and recent developments
- Supply chain trends mapping the latest technological advancements
Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances
Market Dynamics:
Driver:
Surge in generative AI & LLMs
The generative AI & LLMs models require immense computational power, which GPUs are uniquely suited to deliver at scale. Enterprises are increasingly leveraging GPUaaS to accelerate training and inference workloads without investing in costly on-premise infrastructure. The rise of applications such as conversational AI, image synthesis, and autonomous systems is intensifying GPU utilization. Cloud providers are expanding GPUaaS offerings to support diverse industries, from finance to entertainment. As organizations pursue innovation in AI-driven products, GPUaaS is becoming a critical enabler of competitive advantage. This surge in AI workloads is expected to remain the primary driver of market growth throughout the forecast period.
Restraint:
Data security & privacy concerns
Sensitive workloads in healthcare, finance, and government sectors often involve confidential datasets that organizations hesitate to process in shared cloud environments. Concerns around unauthorized access, data leakage, and compliance with regulations such as GDPR and HIPAA limit broader deployment. Cloud providers must invest heavily in encryption, secure multi-tenancy, and compliance certifications to reassure clients. Smaller enterprises may struggle to navigate complex regulatory landscapes, slowing their migration to GPUaaS platforms. The integration of AI into sensitive decision-making processes further amplifies the need for robust safeguards.
Opportunity:
Edge computing integration
By deploying GPU resources closer to data sources, latency can be reduced and real-time analytics enhanced. Industries such as autonomous vehicles, smart manufacturing, and healthcare diagnostics benefit from edge-enabled GPUaaS solutions. This convergence supports decentralized AI training and inference, enabling faster decision-making in mission-critical environments. Cloud providers are investing in hybrid architectures that combine centralized GPU clusters with distributed edge nodes. The rise of 5G networks further strengthens this opportunity by enabling seamless connectivity between edge devices and GPUaaS platforms. As edge computing adoption accelerates, GPUaaS providers can unlock new revenue streams and expand their customer base.
Threat:
Rising competition from custom ASICs
Tech giants and specialized startups are developing ASICs optimized for AI workloads, offering superior performance-per-watt compared to general-purpose GPUs. These alternatives threaten to erode GPUaaS demand, particularly in hyperscale data centers. ASICs also provide cost advantages for organizations running repetitive, large-scale AI tasks. However, GPUs retain flexibility across diverse workloads, which ASICs often lack. The challenge for GPUaaS providers lies in differentiating their offerings through scalability, accessibility, and ecosystem integration. Rising ASIC adoption underscores the need for GPUaaS platforms to continuously innovate and maintain relevance in a rapidly evolving hardware landscape.
Covid-19 Impact:
Lockdowns disrupted hardware supply chains, leading to shortages and delayed deployments of GPU clusters. At the same time, remote work and digital transformation accelerated demand for cloud-based AI services. Industries such as healthcare and life sciences leveraged GPUaaS for drug discovery, diagnostics, and pandemic modeling. The surge in online entertainment and e-commerce also boosted GPUaaS utilization for recommendation engines and content generation. Cloud providers responded by scaling infrastructure and offering flexible pricing models to meet rising demand. Post-pandemic strategies now emphasize resilience, distributed architectures, and automation across GPUaaS ecosystems.
The hardware segment is expected to be the largest during the forecast period
The hardware segment is expected to account for the largest market share during the forecast period, due to its foundational role in GPUaaS delivery. GPUs, servers, and networking equipment form the backbone of cloud-based AI infrastructure. Continuous innovation in GPU architectures, such as NVIDIA’s H100 and AMD’s MI300, is driving performance improvements. Hardware investments are critical for supporting increasingly complex AI workloads across industries. Cloud providers are expanding data center capacity to meet surging demand for GPUaaS services. The scalability and efficiency of hardware directly influence service quality and adoption rates.
The healthcare & life sciences segment is expected to have the highest CAGR during the forecast period
Over the forecast period, the healthcare & life sciences segment is predicted to witness the highest growth rate, due to its reliance on GPUaaS for advanced analytics. Applications such as genomics, drug discovery, and medical imaging require massive computational resources. GPUaaS enables researchers to accelerate simulations and improve diagnostic accuracy without heavy capital investment. The pandemic highlighted the importance of GPU-powered modeling in vaccine development and epidemiology. Hospitals and research institutions are increasingly adopting GPUaaS for AI-driven clinical decision support. Cloud providers are tailoring GPUaaS solutions to meet compliance requirements in healthcare.
Region with largest share:
During the forecast period, the North America region is expected to hold the largest market share, due to its technological leadership and strong cloud ecosystem. The U.S. hosts major GPUaaS providers such as AWS, Microsoft Azure, and Google Cloud. Robust investments in AI R&D and enterprise digital transformation are driving adoption. North America’s healthcare, finance, and automotive industries are early adopters of GPUaaS solutions. Favorable regulatory frameworks and advanced infrastructure further support market expansion. Strategic partnerships between cloud providers and enterprises are accelerating innovation in GPUaaS applications.
Region with highest CAGR:
Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR, owing to rapid digitalization and expanding AI adoption. Countries such as China, India, and Japan are investing heavily in cloud infrastructure and GPU clusters. Government initiatives promoting AI innovation and smart city projects are boosting demand for GPUaaS. The region’s growing startup ecosystem is leveraging GPUaaS for scalable AI development. Rising internet penetration and 5G rollout are enabling new GPUaaS applications in e-commerce, gaming, and mobility. Local cloud providers are partnering with global players to expand service availability.
Key players in the market
Some of the key players in GPU as a Service (GPUaaS) Market include NVIDIA Corporation, Fujitsu, Amazon Web Services (AWS), Baidu AI Cloud, Microsoft Corporation, DigitalOcean Holdings, Google Cloud, Vultr, IBM Corporation, Lambda Labs, Oracle Corporation, CoreWeave, Inc., Alibaba, Rescale, and Tencent.
Key Developments:
In January 2026, NVIDIA and CoreWeave, Inc. announced an expansion of their long-standing complementary relationship to enable CoreWeave to accelerate the buildout of more than 5 gigawatts of AI factories by 2030 to advance AI adoption at global scale. NVIDIA has invested $2 billion in CoreWeave Class A common stock at a purchase price of $87.20 per share. The investment reflects NVIDIA’s confidence in CoreWeave’s business, team and growth strategy as a cloud platform built on NVIDIA infrastructure.
In January 2026, Datavault AI Inc. announced it will deliver enterprise-grade AI performance at the edge in New York and Philadelphia through an expanded collaboration with IBM (NYSE: IBM) using the SanQtum AI platform. Operated by Available Infrastructure, SanQtum AI is a fleet of synchronized micro edge data centers running IBM’s watsonx portfolio of AI products on a zero-trust network. The combined deployment is designed to enable cybersecure data storage and compute.
Components Covered:
• Hardware
• Software
• Services
Deployment Models Covered:
• Public Cloud
• Private Cloud
• Hybrid Cloud
Service Types Covered:
• On-Demand GPUaaS
• Reserved/Subscription GPUaaS
• Pay-Per-Use GPUaaS
Organization Sizes Covered:
• Large Enterprises
• Small & Medium-Sized Enterprises (SMEs)
Applications Covered:
• Artificial Intelligence (AI) & Machine Learning
• Data Analytics
• Gaming & Graphics Rendering
• High-Performance Computing (HPC)
• Autonomous Vehicles
• Virtual Reality (VR) & Augmented Reality (AR)
• Media
• Other Applications
End Users Covered:
• IT & Telecom
• Healthcare & Life Sciences
• Automotive
• Entertainment
• Government & Defense
• Education & Research
• Retail & E-Commerce
• Financial Services
• Energy & Utilities
Regions Covered:
• North America
US
Canada
Mexico
• Europe
Germany
UK
Italy
France
Spain
Rest of Europe
• Asia Pacific
Japan
China
India
Australia
New Zealand
South Korea
Rest of Asia Pacific
• South America
Argentina
Brazil
Chile
Rest of South America
• Middle East & Africa
Saudi Arabia
UAE
Qatar
South Africa
Rest of Middle East & Africa
What our report offers:
- Market share assessments for the regional and country-level segments
- Strategic recommendations for the new entrants
- Covers Market data for the years 2023, 2024, 2025, 2026, 2027, 2028, 2030, 2032 and 2034
- Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
- Strategic recommendations in key business segments based on the market estimations
- Competitive landscaping mapping the key common trends
- Company profiling with detailed strategies, financials, and recent developments
- Supply chain trends mapping the latest technological advancements
Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances
Table of Contents
200 Pages
- 1 Executive Summary
- 1.1 Market Snapshot and Key Highlights
- 1.2 Growth Drivers, Challenges, and Opportunities
- 1.3 Competitive Landscape Overview
- 1.4 Strategic Insights and Recommendations
- 2 Research Framework
- 2.1 Study Objectives and Scope
- 2.2 Stakeholder Analysis
- 2.3 Research Assumptions and Limitations
- 2.4 Research Methodology
- 2.4.1 Data Collection (Primary and Secondary)
- 2.4.2 Data Modeling and Estimation Techniques
- 2.4.3 Data Validation and Triangulation
- 2.4.4 Analytical and Forecasting Approach
- 3 Market Dynamics and Trend Analysis
- 3.1 Market Definition and Structure
- 3.2 Key Market Drivers
- 3.3 Market Restraints and Challenges
- 3.4 Growth Opportunities and Investment Hotspots
- 3.5 Industry Threats and Risk Assessment
- 3.6 Technology and Innovation Landscape
- 3.7 Emerging and High-Growth Markets
- 3.8 Regulatory and Policy Environment
- 3.9 Impact of COVID-19 and Recovery Outlook
- 4 Competitive and Strategic Assessment
- 4.1 Porter's Five Forces Analysis
- 4.1.1 Supplier Bargaining Power
- 4.1.2 Buyer Bargaining Power
- 4.1.3 Threat of Substitutes
- 4.1.4 Threat of New Entrants
- 4.1.5 Competitive Rivalry
- 4.2 Market Share Analysis of Key Players
- 4.3 Product Benchmarking and Performance Comparison
- 5 Global GPU as a Service (GPUaaS) Market, By Component
- 5.1 Introduction
- 5.2 Hardware
- 5.2.1 GPUs
- 5.2.2 Memory Units
- 5.2.3 Storage Systems
- 5.3 Software
- 5.3.1 Virtualization Software
- 5.3.2 Deployment & Management Software
- 5.4 Services
- 5.4.1 Consulting
- 5.4.2 Training & Support
- 6 Global GPU as a Service (GPUaaS) Market, By Deployment Model
- 6.1 Introduction
- 6.2 Public Cloud
- 6.3 Private Cloud
- 6.4 Hybrid Cloud
- 7 Global GPU as a Service (GPUaaS) Market, By Service Type
- 7.1 Introduction
- 7.2 On-Demand GPUaaS
- 7.3 Reserved/Subscription GPUaaS
- 7.4 Pay-Per-Use GPUaaS
- 8 Global GPU as a Service (GPUaaS) Market, By Organization Size
- 8.1 Introduction
- 8.2 Large Enterprises
- 8.3 Small & Medium-Sized Enterprises (SMEs)
- 9 Global GPU as a Service (GPUaaS) Market, By Application
- 9.1 Introduction
- 9.2 Artificial Intelligence (AI) & Machine Learning
- 9.3 Data Analytics
- 9.4 Gaming & Graphics Rendering
- 9.5 High-Performance Computing (HPC)
- 9.6 Autonomous Vehicles
- 9.7 Virtual Reality (VR) & Augmented Reality (AR)
- 9.8 Media
- 9.9 Other Applications
- 10 Global GPU as a Service (GPUaaS) Market, By End User
- 10.1 Introduction
- 10.2 IT & Telecom
- 10.3 Healthcare & Life Sciences
- 10.4 Automotive
- 10.5 Entertainment
- 10.6 Government & Defense
- 10.7 Education & Research
- 10.8 Retail & E-Commerce
- 10.9 Financial Services
- 10.10 Energy & Utilities
- 11 Global GPU as a Service (GPUaaS) Market, By Geography
- 11.1 North America
- 11.1.1 United States
- 11.1.2 Canada
- 11.1.3 Mexico
- 11.2 Europe
- 11.2.1 United Kingdom
- 11.2.2 Germany
- 11.2.3 France
- 11.2.4 Italy
- 11.2.5 Spain
- 11.2.6 Netherlands
- 11.2.7 Belgium
- 11.2.8 Sweden
- 11.2.9 Switzerland
- 11.2.10 Poland
- 11.2.11 Rest of Europe
- 11.3 Asia Pacific
- 11.3.1 China
- 11.3.2 Japan
- 11.3.3 India
- 11.3.4 South Korea
- 11.3.5 Australia
- 11.3.6 Indonesia
- 11.3.7 Thailand
- 11.3.8 Malaysia
- 11.3.9 Singapore
- 11.3.10 Vietnam
- 11.3.11 Rest of Asia Pacific
- 11.4 South America
- 11.4.1 Brazil
- 11.4.2 Argentina
- 11.4.3 Colombia
- 11.4.4 Chile
- 11.4.5 Peru
- 11.4.6 Rest of South America
- 11.5 Rest of the World (RoW)
- 11.5.1 Middle East
- 11.5.1.1 Saudi Arabia
- 11.5.1.2 United Arab Emirates
- 11.5.1.3 Qatar
- 11.5.1.4 Israel
- 11.5.1.5 Rest of Middle East
- 11.5.2 Africa
- 11.5.2.1 South Africa
- 11.5.2.2 Egypt
- 11.5.2.3 Morocco
- 11.5.2.4 Rest of Africa
- 12 Strategic Market Intelligence
- 12.1 Industry Value Network and Supply Chain Assessment
- 12.2 White-Space and Opportunity Mapping
- 12.3 Product Evolution and Market Life Cycle Analysis
- 12.4 Channel, Distributor, and Go-to-Market Assessment
- 13 Industry Developments and Strategic Initiatives
- 13.1 Mergers and Acquisitions
- 13.2 Partnerships, Alliances, and Joint Ventures
- 13.3 New Product Launches and Certifications
- 13.4 Capacity Expansion and Investments
- 13.5 Other Strategic Initiatives
- 14 Company Profiles
- 14.1 NVIDIA Corporation
- 14.2 Fujitsu
- 14.3 Amazon Web Services (AWS)
- 14.4 Baidu AI Cloud
- 14.5 Microsoft Corporation
- 14.6 DigitalOcean Holdings
- 14.7 Google Cloud
- 14.8 Vultr
- 14.9 IBM Corporation
- 14.10 Lambda Labs
- 14.11 Oracle Corporation
- 14.12 CoreWeave, Inc.
- 14.13 Alibaba
- 14.14 Rescale
- 14.15 Tencent
- List of Tables
- Table 1 Global GPU as a Service (GPUaaS) Market Outlook, By Region (2023-2034) ($MN)
- Table 2 Global GPU as a Service (GPUaaS) Market Outlook, By Component (2023-2034) ($MN)
- Table 3 Global GPU as a Service (GPUaaS) Market Outlook, By Hardware (2023-2034) ($MN)
- Table 4 Global GPU as a Service (GPUaaS) Market Outlook, By GPUs (2023-2034) ($MN)
- Table 5 Global GPU as a Service (GPUaaS) Market Outlook, By Memory Units (2023-2034) ($MN)
- Table 6 Global GPU as a Service (GPUaaS) Market Outlook, By Storage Systems (2023-2034) ($MN)
- Table 7 Global GPU as a Service (GPUaaS) Market Outlook, By Software (2023-2034) ($MN)
- Table 8 Global GPU as a Service (GPUaaS) Market Outlook, By Virtualization Software (2023-2034) ($MN)
- Table 9 Global GPU as a Service (GPUaaS) Market Outlook, By Deployment & Management Software (2023-2034) ($MN)
- Table 10 Global GPU as a Service (GPUaaS) Market Outlook, By Services (2023-2034) ($MN)
- Table 11 Global GPU as a Service (GPUaaS) Market Outlook, By Consulting (2023-2034) ($MN)
- Table 12 Global GPU as a Service (GPUaaS) Market Outlook, By Training & Support (2023-2034) ($MN)
- Table 13 Global GPU as a Service (GPUaaS) Market Outlook, By Deployment Model (2023-2034) ($MN)
- Table 14 Global GPU as a Service (GPUaaS) Market Outlook, By Public Cloud (2023-2034) ($MN)
- Table 15 Global GPU as a Service (GPUaaS) Market Outlook, By Private Cloud (2023-2034) ($MN)
- Table 16 Global GPU as a Service (GPUaaS) Market Outlook, By Hybrid Cloud (2023-2034) ($MN)
- Table 17 Global GPU as a Service (GPUaaS) Market Outlook, By Service Type (2023-2034) ($MN)
- Table 18 Global GPU as a Service (GPUaaS) Market Outlook, By On-Demand GPUaaS (2023-2034) ($MN)
- Table 19 Global GPU as a Service (GPUaaS) Market Outlook, By Reserved/Subscription GPUaaS (2023-2034) ($MN)
- Table 20 Global GPU as a Service (GPUaaS) Market Outlook, By Pay-Per-Use GPUaaS (2023-2034) ($MN)
- Table 21 Global GPU as a Service (GPUaaS) Market Outlook, By Organization Size (2023-2034) ($MN)
- Table 22 Global GPU as a Service (GPUaaS) Market Outlook, By Large Enterprises (2023-2034) ($MN)
- Table 23 Global GPU as a Service (GPUaaS) Market Outlook, By Small & Medium-Sized Enterprises (SMEs) (2023-2034) ($MN)
- Table 24 Global GPU as a Service (GPUaaS) Market Outlook, By Application (2023-2034) ($MN)
- Table 25 Global GPU as a Service (GPUaaS) Market Outlook, By Artificial Intelligence (AI) & Machine Learning (2023-2034) ($MN)
- Table 26 Global GPU as a Service (GPUaaS) Market Outlook, By Data Analytics (2023-2034) ($MN)
- Table 27 Global GPU as a Service (GPUaaS) Market Outlook, By Gaming & Graphics Rendering (2023-2034) ($MN)
- Table 28 Global GPU as a Service (GPUaaS) Market Outlook, By High-Performance Computing (HPC) (2023-2034) ($MN)
- Table 29 Global GPU as a Service (GPUaaS) Market Outlook, By Autonomous Vehicles (2023-2034) ($MN)
- Table 30 Global GPU as a Service (GPUaaS) Market Outlook, By Virtual Reality (VR) & Augmented Reality (AR) (2023-2034) ($MN)
- Table 31 Global GPU as a Service (GPUaaS) Market Outlook, By Media (2023-2034) ($MN)
- Table 32 Global GPU as a Service (GPUaaS) Market Outlook, By Other Applications (2023-2034) ($MN)
- Table 33 Global GPU as a Service (GPUaaS) Market Outlook, By End User (2023-2034) ($MN)
- Table 34 Global GPU as a Service (GPUaaS) Market Outlook, By IT & Telecom (2023-2034) ($MN)
- Table 35 Global GPU as a Service (GPUaaS) Market Outlook, By Healthcare & Life Sciences (2023-2034) ($MN)
- Table 36 Global GPU as a Service (GPUaaS) Market Outlook, By Automotive (2023-2034) ($MN)
- Table 37 Global GPU as a Service (GPUaaS) Market Outlook, By Entertainment (2023-2034) ($MN)
- Table 38 Global GPU as a Service (GPUaaS) Market Outlook, By Government & Defense (2023-2034) ($MN)
- Table 39 Global GPU as a Service (GPUaaS) Market Outlook, By Education & Research (2023-2034) ($MN)
- Table 40 Global GPU as a Service (GPUaaS) Market Outlook, By Retail & E-Commerce (2023-2034) ($MN)
- Table 41 Global GPU as a Service (GPUaaS) Market Outlook, By Financial Services (2023-2034) ($MN)
- Table 42 Global GPU as a Service (GPUaaS) Market Outlook, By Energy & Utilities (2023-2034) ($MN)
- Note: Tables for North America, Europe, APAC, South America, and Rest of the World (RoW) are also represented in the same manner as above.
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