20-05-2025
GPU as a Service Market Analysis by Service Model, GPU Type, Deployment, Enterprise Type - Global Forecast to 2030
The report will help the market leaders/new entrants in this market with information on the closest approximations of the revenue numbers for the overall GPU as a Service market and the subsegments. This report will help stakeholders understand the competitive landscape and gain more insights to position their businesses better and plan suitable go-to-market strategies. The report also helps stakeholders understand the pulse of the market and provides them with information on key market drivers, restraints, challenges, and opportunities.
GPU as a Service Market
Dublin, May 13, 2025 (GLOBE NEWSWIRE) -- The "GPU as a Service Market by Service Model (IaaS, PaaS), GPU Type (High-End GPUs, Mid-Range GPUs, Low-End GPUs), Deployment (Public Cloud, Private Cloud, Hybrid Cloud), Enterprise Type (Large Enterprises, SMEs) - Global Forecast to 2030" report has been added to GPU as a Service market is expected to be worth USD 8.21 billion in 2025 and is estimated to reach USD 26.62 billion by 2030, growing at a CAGR of 26.5% between 2025 and 2030
The growth of the GPU as a Service market is driven by increasing demand for high-performance GPUs in video rendering, 3D content creation, and real-time applications. Industries like gaming, film production, and architecture require scalable and cost-effective GPU solutions for complex visual effects (VFX) and simulations.
GPUaaS eliminates the need for expensive on-premises GPU clusters, providing on-demand access to cloud resources. Additionally, the rise of real-time rendering engines like Unreal Engine 5 and AI-driven content generation further accelerates market growth, enabling immersive virtual experiences and reducing production timelines for studios, developers, and content GPU segment to have highest CAGR in the forecasted high-end GPU segment will witness a rapid growth in the GPU as a Service market driven by increasing requirement for accelerated computation in AI, ML, and complicated simulations. High-end GPUs like NVIDIA's H100 Tensor Core GPUs and AMD's Instinct MI300X provide immense computing capabilities making them suitable to train large language models (LLMs) and generative AI applications. For example, Amazon Web Services (AWS) provides EC2 UltraClusters with NVIDIA H100 GPUs to support trillion-parameter AI Microsoft Azure and Google Cloud integrate high-end GPUs to provide scalable AI infrastructure for enterprises. The film and gaming industries are also contributing to this growth, using high-end GPUs for real-time rendering, special effects (VFX), and immersive virtual experiences. Platforms such as Epic Games' Unreal Engine 5 utilize GPUaaS for photorealistic virtual productions. Also, sectors such as healthcare and scientific research utilize GPUaaS for drug discovery and medical imaging analysis. With increased adoption of AI across industries, businesses opt for high-end GPUs to address growing computational needs. The flexible pay-as-you-go cloud model provides greater access to such powerful assets, further increasing the growth of the high-end GPU Enterprise Type- Large Enterprises segment will hold largest market share of GPU as a Service market in 2030The large enterprise segment will hold the highest market share within the GPU as a Service market given their high computing requirements and widespread AI deployment. Multinational conglomerates, Fortune 500 firms and industry titans from sectors such as healthcare, finance, automotive, and media utilize GPUaaS for AI applications such as medical imaging, drug discovery, fraud detection, and real-time analytics to a large extent. The need for scalable GPU resources to manage complex workloads, including large language model (LLM) training and algorithmic trading, drives this service providers offer tailored solutions with dedicated GPU clusters, high-bandwidth networking, and enterprise-grade security to meet the customization needs of large enterprises. In addition, the scalability of multi-cloud and hybrid cloud deployments allows companies to optimize costs while ensuring low latency and high availability. Enterprises benefit from long-term contracts, constructing predictable GPU usage costs and gaining access to the latest GPU technology.
Industries with mission-critical applications often allocate dedicated GPU resources for activities such as autonomous car development and financial modeling. With increasing AI adoption and rising dependence on data-driven decisions, the large corporations will continue to rule the GPUaaS market, leveraging its flexibility and cost-effectiveness for scalable Pacific is expected to hold high CAGR in during the forecast Pacific is expected to grow significantly in the GPU as a Service market as a result of accelerating growth in cloud computing, rising adoption of AI, and heavy investments in data center infrastructure. The growth is being led by China, Japan, South Korea, and India through government initiatives, private investment, and technological innovations. For instance, In May 2023, the Chinese government made plans to construct AI industrial bases, driving AI research. Moreover, policies such as the Shenzhen AI Regulation support AI adoption by pushing public data sharing and corporate innovation. Japan is seeing huge investments in AI invested USD 2.9 billion in Japan's cloud and AI infrastructure in April 2024, and Oracle pledged USD 8 billion to build cloud data centers. These projects give businesses access to scalable GPU capacity for AI applications. India is also moving ahead with GPUaaS adoption with its IndiaAI initiative. In March 2024, the Indian government sanctioned USD 124 billion in investments to deploy more than 10,000 GPUs, enabling AI research and startups. These strategic investments and efforts make Asia Pacific a high-growth region in the GPUaaS landscape
The report profiles key players in the GPU as a Service market with their respective market ranking analysis. Prominent players profiled in this report are Amazon web Servies, Inc. (US), Microsoft (US), Google (US), Oracle (US), IBM (US), Coreweave (US), Alibaba Cloud (China), Lambda (US), Tencent Cloud (China), (India), among from this, Fluidstack (UK), OVH SAS (France), E2E Networks Limited (India), RunPod (US), ScaleMatrix Holdings, Inc. (US), (US), AceCloud (India), Snowcell (Norway), Linode LLC. (US), Yotta Infrastructure (India), VULTR (US), DigitalOcean, LLC. (US), Rackspace Technology (US), Gcore (Luxembourg), and Nebius B.V. (Amsterdam), are among a few emerging companies in the GPU as a Service Attributes:
Report Attribute
Details
No. of Pages
282
Forecast Period
2025 - 2030
Estimated Market Value (USD) in 2025
$8.21 Billion
Forecasted Market Value (USD) by 2030
$26.62 Billion
Compound Annual Growth Rate
26.5%
Regions Covered
Global
Market Dynamics
Drivers
Surging Use of Cloud-Powered AI, ML, and Dl Frameworks
Increasing Need for Budget-Friendly Yet High-Performance GPU Solutions from Enterprises
Growing Deployment of GPU as a Service Model in Gaming and Virtualization Applications
Restraints
Supply Chain Bottlenecks and AI Demand Dynamics
Opportunities
Revolutionizing Media Production Workflows
Increasing Investments in AI Infrastructure by Cloud Service Providers
Rise of Pure-Play GPU Companies
Challenges
Managing High Power Consumption and Cooling Needs in Cloud Gpus
Confronting Security, Performance, and Scalability Challenges in Multi-Tenant Environments
Case Study Analysis
Nearmap Reduces Computing Cost and Increases Data Processing Capacity Using Amazon Ec2 G4 Instances
Soluna Deploys GPU Cloud Management Software to Boost Its Marketplace Reach
Computer Vision Technology Company Increases GPU Utilization to Improve Productivity and Reduce Dl Training Time
Epfl Optimizes AI Infrastructure to Prioritize Workload Demands Using Run:AI's GPU Orchestration Platform
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GPU as a Service Market
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