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GPU as a Service Market Size, Share, and Analysis, By Deployment Model (Public GPU Cloud, Private GPU Cloud and Hybrid GPU Cloud), By End-User (BFSI, AI & Machine Learning, IT & Telecom, Healthcare, Automotive, Finance, Media & Entertainment, and Others), By Enterprise Type (Large Enterprises and Small & Medium-Sized Enterprises), and By Region (North America, Europe, Asia-Pacific, And Rest of the World) And Regional Forecast 2024-2034
GPU as a Service Market is anticipated to grow at a CAGR of 29% in the forecast period (2024-2034), with the market size valued at USD 4.3 billion in 2023 and projected to reach USD 71.7 billion by 2034.
GPU as a service or GPUaaS refers to cloud computing models that helps in accessing graphic processing units, based on subscription plans. These services allow consumer who are accessing these facilities, to utilize the GPUs without any need for dedicated hardware. GPUaaS companies provide virtualized GPU units that users can rent for a variety of applications, which eliminates the need for companies to invest in expensive computing infrastructure. This design allows customers to expand GPU resources as needed, thereby providing flexibility and affordability. Additionally, GPUaaS provides access to advanced computational power without forcing customers to manage hardware maintenance, which makes it useful in areas such as AI, gaming, and scientific research.
GPU as a Service Market is anticipated to reach USD 71.7 billion, growing at a CAGR of 29% during the forecast period, due to the growth in IT and services across various industries, which demand fast operating GPU systems. Graphic processing units are categorised based on deployment model and significantly used in artificial intelligence, machine learning, and automotive industries. Additionally, AI and ML require vast codes and algorithms that can span thousands of pages, due to which power-efficient systems such as GPUs are needed to perform tasks which cannot be completed using CPUs. Moreover, new automotive vehicles and machinery are getting technologically advanced, and with the emergence of the EV segments, the demand for data analytics and visualization using is rapidly expanding. GPU assist in enabling these operations without the usage of equipment, which makes tasks for many industries. Thus, the GPUaaS market displayed durability and innovation, with continuous improvements in performance and efficiency, which drives its growth across various industries.
Source: Fatpos Global
By End-User, the GPU as a service market is segmented into BFSI, AI & Machine Learning, IT & Telecom, Healthcare, Automotive, Finance, Media & Entertainment and Others. The AI & machine learning segment will dominate the GPUaaS industry due to major developments and shift towards such technology. Training AI/ML models require image and video analysis, with major mathematical calculations. Moreover, traditional systems don’t comply with such fast processing, which makes GPU ideal for such tasks. Additionally, as more startups and small businesses integrate AI and ML models into their operations, the demand for GPU resources will grow. The AI and ML industries are often operated under high workload settings with demanding computational needs. Therefore, modern GPUs are crucial for efficiently managing the computational needs of training and operating AI/ML algorithms.
By Deployment Model, the GPU as a service market is segmented into Public GPU Cloud, Private GPU Cloud and Hybrid GPU Cloud. Public GPU Cloud holds the largest market share as they provide high-efficiency GPU services without the need for investments in any computer hardware or infrastructure. Public GPUs provide pay-as-you-go pricing models, which helps in saving large expenses on GPUs. Public cloud providers eliminate the hassle for the customers by setting up and upgrading the GPU infrastructure themselves. These platforms are available in different configurations, which further help users to use resources in accordance to their needs. Moreover, new developments and integration of services such as AI/ML or storage & networking is making Public GPUs more accessible to general users. Therefore, while private and hybrid GPU cloud services are steadily expanding, public clouds provide more cost effective and ease for use of wider audience.
Source: Fatpos Global
Increasing Data Volumes and Complexity Will Drive the Use of Efficient GPUs
Organisations are becoming tech efficient, which presents various challenges for evaluating and extracting insights from large datasets. Additionally, classic computing technologies frequently struggle with higher performance on current datasets. Therefore, GPUs (Graphics Processing Units) emerge as a solution to this issue due to their parallel processing capabilities. They are perfect for massive processing as they can perform numerous tasks at once, unlike previously used techniques. Parallel processing allows companies to apply data analytics and obtain insights from complex information. Therefore, GPUs play a critical role in helping firms manage the complexities of advanced data analysis and generate useful insights more efficiently.
Need for Faster Computation Will Influence the Whole Market Growth
Faster and more effective resources are in high demand due to the expanding technology sector. Scientific models and engineering design frequently require massive computing capacity to execute complex calculations and simulations. Therefore, due to their higher processing efficiency, GPUs are able to offer modern technology to customers through subscription-based agreements. They are designed to run parallel activities, which is useful for compute-intensive applications. Furthermore, GPUaaS customers can use increased processing capabilities to run scientific simulations faster and get swift output to complicated tasks. Therefore, organizations can use GPUaaS to simplify their operations, accelerate their R&D procedures, and eventually boost their competitiveness in a rapidly growing technology field.
Security Concerns with Sensitive Data on Cloud Platforms May Hinder Market Growth
Use of GPU as a service in cloud platforms can introduce unnecessary risks with sensitive data which is confidential to companies. Organisations must focus on navigating challenges associated with unauthorized access and data breaches by applying strong cyber security measures. Moreover, companies can use strong encryption methods or authentication, which ensures that only authorized personnel can interact with information. Therefore, striking a balance between using GPUaaS for computational benefits and maintaining a secure environment is necessary for organizations that are aiming to process sensitive data in shared cloud platforms.
GPU as a Service Market is segmented based on regional analysis into five major regions: North America, Latin America, Europe, Asia Pacific and the Middle East and Africa. The North America region dominates the GPU as a service market due to the early adoption of cloud services and developments in the AI/ML research. The region gains popularity owing to the presence of major companies such as AWS, Google Cloud which offer GaaS services. Moreover, North America holds a strong pool of tech talent and institutions in AI and ML.
Simultaneously, the Asia Pacific region will experience rapid growth due to increasing computational demands and the vast adoption of GPU-intensive applications, which drives the growth of GPU as a Service (GPUaaS). Furthermore, major cloud providers like Alibaba Cloud and Tencent Cloud offer GPU units within their cloud services. This accessibility empowers organizations to innovate and improve user experiences across the Asia Pacific region.
Source: Fatpos Global
Covid-19 had a major impact on the GPU as a service market. Initially, global lockdowns and restrictions in manufacturing hampered the availability of new GPUs and limited its expansion. Business became cautious about the pandemic, which led to remote work and affected the functioning of GPUaaS market. Despite some initial disadvantages, companies positively adapted to remote work, which accelerated the use of cloud-based services. Furthermore, new technology solutions have emerged to facilitate remote collaboration on AI/ML projects, which allowed firms to work with effective GaaS resources. Therefore, the pandemic had an unpredictable impact initially but resulted in long-term growth.
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ATTRIBUTE |
DETAILS |
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Study Period |
2018-2034 |
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Base Year |
2023 |
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Forecast Period |
2024-2034 |
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Historical Period |
2019-2022 |
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Growth Rate |
CAGR of 29% from 2024-2034 |
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Unit |
Value (USD Billion) |
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Segmentation |
Main Segments List |
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By Deployment Model |
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By End-User |
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By Enterprise Type |
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By Region |
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GPU as a Service Market size was values at USD 4.3 billion in 2023 and is projected to reach the value of USD 71.7 billion in 2034, exhibiting a CAGR of 29% during the forecast period.
GPU as a service (GaaS) are cloud computing models that helps to access the Graphics Processing Units, based on subscription plans. These services allow all consumer accessing these facilities, to leverage the GPUs without any requirements for any dedicated hardware.
The AI and Machine Learning (ML) segment and Public GPU cloud segment accounted for the largest GPU as a Service Market share.
Key players in the GPU as a Service Market include Amazon Web Services (AWS), Google Cloud Platform (GCP), Microsoft Azure, Paperspace, NVIDIA Tesla on Demand, Lambda Labs, Nimbix, Scaleway, Shadow, OVHcloud, Vectordash, Mainframe, Paperspace, Blazecloud, Tribeca Digital and Other Prominent Players.
Increasing data volumes and complexity, along with the need for faster computation are the factors driving the GPU as a service market.
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