Global GPU-as-a-Service Market By Product (Software and Services), By Delivery Model (Public Cloud, Private Cloud, and Hybrid Cloud), By Service Model (SaaS, PaaS, and IaaS), By Application (Gaming, Cryptocurrency Mining, Design & Manufacture, Automotive, Real-estate, and Healthcare) – Global Analysis & Forecast 2020-2030.

Report ID : 405  |  Published Date : Oct 2020  |  Pages : 160  |  Region : Global  |  Report Code : ICT-43





Global GPU-as-a-Service Market Overview and Introduction

GPU is an electronic circuit designed to rapidly manipulate and alter the memory for accelerating the creation of images in a frame buffer intended for output to a display device. GPU-as-a-Service is gaining momentum due to the increasing number of applications and use cases on hardware, which include deep learning, speech recognition, natural language processing, data mining, and real-time information. These applications benefit from scaling to multi-GPU servers and then to GPU server clusters with network interconnections.

GPUs require programming accessibility from developers to ensure broad access to high-performance computers. However, the regular CPU based servers deployed for on-premise hosting have certain limitations. Thus, providing them with on-demand cloud access will make them accessible to various applications. In cloud environments, GPUs will work with server CPUs to accelerate applications and improve their performance. A CPU will offload the compute-intensive portions of applications to a GPU, which will process large blocks of data simultaneously and improve the performance of the server.

An increasing demand for high-graphics processing capabilities from the gaming industry and the rapid adoption of GPUs for cryptocurrency mining are the major factors driving the growth of the GPU-as-a-Service market. Also, the increasing inclination toward cloud platforms for scalable & flexible computing infrastructure is expected to facilitate the adoption of GPU-as-a-Service solutions over the forecasted timeframe. According to Internet World Facts, internet penetration was around 51% in 2019, coupled with the proliferation of smartphones, which is driving the gaming market in the Asia Pacific region. The region comprises of eight countries in the top 20 gaming markets worldwide.

The key industry participants in the market focus on technological advancements to develop improved GPU solutions that are capable of handling intensive workloads for modern computing applications such as AI, deep learning, and machine learning. The prominent providers focus on collaborating with cloud platform providers to extend their presence in the GPU as a Service market. With an increasing demand for advanced GPUs from sectors, such as automobile and healthcare, for developing autonomous vehicles and advanced medical imaging technologies, the competition in the GPU as a Service market is likely to intensify over the forecast timeline.

The GPU-as-a-Service market can be segmented into the following categories – By Product, By Deployment Model, By Service Model, By Application, and By Region

Global GPU-as-a-Service Market By Product

Based on the product, the market can be segmented into software and services. The software segment is projected to propel the market share due to increasing demand for GPU solutions for applications such as gaming, design & manufacture, real estate, and others. Professional users, such as engineers and CAD designers, have more stringent requirements for hardware performance than those of gamers and average users. Therefore, faster & advanced GPU software enables greater performance and stability, which will significantly support the GPU-as-a-Service market over the projected timeframe.

Global GPU-as-a-Service Market By Deployment Model

Based on the deployment model, the market can be segmented into public cloud, private cloud, and hybrid cloud. The public cloud segment is anticipated to hold a major share and showcase significant growth over the coming years. The public cloud model helps SMEs to significantly reduce the costs involved in the procurement & maintenance of software infrastructure. Some of the typical benefits of a public cloud model include low costs, low maintenance, high reliability, and near-unlimited scalability. Public clouds allow users to easily add or reduce the capacity and are accessible from internet-connected devices.

Global GPU-as-a-Service Market By Service Model

Based on the platform, the market can be segmented into SaaS (Software-as-a-Service), PaaS (Platform-as-a-Service), and IaaS (Infrastructure-as-a-Service). The SaaS service delivery model is the most popularly used model by customers in the GPU-as-a-Service market. As the model enables lower upfront costs due to its subscription-based availability, users can significantly reduce the capital expenditure required for purchasing or developing and managing complex software solutions for their operational processes. Also, hardware and software updates can be easily deployed to hosted applications in a centralized manner in the SaaS model.

Global GPU-as-a-Service Market By Application

Based on the platform, the market can be segmented into gaming, cryptocurrency mining, design & manufacturing, automotive, real estate, healthcare, and others.

The gaming sector is anticipated to showcase the highest growth due to the large-scale utility of GPUs in games that demand a high graphical output. The advanced computing capabilities of GPUs enable smoother gaming experiences while enabling excellent graphical output to alleviate the overall gaming experience. The prominent cloud service providers have started providing cloud gaming services where they deploy high-end GPUs and users can harness the GPU processing power from the subscribed cloud gaming platform. For instance, in January 2017, the company launched its online gaming platform, NVIDIA GeForce Now, enabling desktop gamers to play games remotely.

Global GPU-as-a-Service Market By Region

Based on the region, the market can be segmented into North America, Europe, Asia Pacific, and the Rest of the World (RoW). The Asia Pacific region is projected to remain the fastest-growing region over the forecast timeline as there is a burgeoning data analytics market in this region with major countries contributing to this growth including South Korea, Singapore, Japan, Australia, India, and China. The growing advent of cryptocurrency mining in China has resulted in high demands for GPUs as they are required to process the blockchain.

The U.S. dominates the North American GPU as a service market as it is home to leading GPU provider companies including NVIDIA, AMD, and Intel. These players are also entering into strategic partnerships and undertaking new product developments to establish their presence in the GPU-as-a-Service market. For instance, in November 2017, Intel and AMD partnered to produce notebook computer chips that pair an Intel CPU with an AMD GPU, which can handle heavy graphics required by top-tier video games.

Global GPU-as-a-Service Market Prominent Players

Some of the major players present in the GPU-as-a-Service market include Advanced Micro Devices (AMD), Inc., Autodesk, Amazon Web Services, Inc., PTC, Dassault Systems, Inc., Google Inc., IBM Corporation, Intel Corporation, Microsoft Corporation, and NVIDIA Corporation. These companies leverage their long-standing expertise in computer hardware manufacturing to develop highly sophisticated processors capable of handling intensive processing applications. These companies focus on securing the competitive advantage in the market by investing heavily in R&D to incorporate enhanced processing capabilities in their processors. With an increasing demand for GPUs due to rising uptake from cryptocurrency miners, these companies are aggressively focusing on increasing their production to meet the rising demand. The key market players are also focusing on strategic acquisitions and mergers to strengthen their product portfolio. Cloud service providers, including Microsoft and Amazon, are partnering with GPU providers, such as NVIDIA and Intel, to provide deep-learning-specific GPUaaS to end-use sectors. For instance, in March 2018, Microsoft launched the WinML AI framework for Windows, demonstrating computer vision capabilities, which run on Intel's GPU.