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全球 GPU 即服务市场规模、份额、成长分析(按产品、服务模式、交付模式) - 2024-2031 年产业预测Global GPU As a Service Market Size, Share, Growth Analysis, By Product(Software, CAD/CAM), By Service Model(SaaS, PaaS), By Delivery Model(Public Cloud, Private Cloud) - Industry Forecast 2024-2031 |
2022年全球GPU即服务市场规模将达到50亿美元,预测期内(2024-2031年)复合年增长率为32%,从2023年的66亿美元增长到2031年的60.8亿美元。预计将增长至3000万美元。
各行业对卓越处理能力的需求不断增长,推动了全球 GPU 即服务 (GPUaaS) 市场的快速成长。这款尖端的云端处理方法让企业只需为使用的高效能 GPU 付费,即可利用复杂模拟、资料分析和人工智慧 (AI) 训练等资源。资源彙整计划。市场将从改变技术面貌的最新开拓中受益。
GPU 与人工智慧和机器学习 (ML) 框架的融合是当今使用的趋势之一。随着人工智慧的使用变得更加广泛,越来越多的公司依赖 GPU 来加速训练和推理步骤。人工智慧和 GPU 的结合正在为许多领域带来革命性的变化,包括医疗保健(人工智慧有助于诊断)和金融(GPU 可以加速大型资料集的分析)。混合和多重云端GPU 实施的增加也是一个值得注意的发展。企业透过在边缘设备、公共云端和本地基础设施之间有意分配工作负载,最大限度地提高资源利用率、减少延迟并提高效能。
Global GPU As A Service Market size was valued at USD 5 billion in 2022 and is poised to grow from USD 6,60 billion in 2023 to USD 60.83 billion by 2031, growing at a CAGR of 32% in the forecast period (2024-2031).
The increasing need for superior processing power in various industries is propelling the rapid growth of the global GPU as a service (GPUaaS) market. With the help of this cutting-edge cloud computing approach, companies can now pay as they go for high-performance GPUs, giving them the flexibility to take on resource-intensive projects like complicated simulations, data analytics, and artificial intelligence (AI) training. The market is positioned to benefit from the newest developments that are changing the face of technology.
The convergence of GPUs with AI and machine learning (ML) frameworks is one trend that is currently in use. Organizations are depending more and more on GPUs to speed up training and inference procedures as AI use spreads. The combination of AI with GPUs is revolutionizing a number of areas, including healthcare (where AI helps with diagnosis) and finance (where GPUs speed up the analysis of large datasets). The increase in hybrid and multi-cloud GPU implementations is another noteworthy development. Companies are deliberately allocating workloads among edge devices, public clouds, and on-premises infrastructure in order to maximize resource usage and reduce latency for better performance.
Top-down and bottom-up approaches were used to estimate and validate the size of the Global GPU As A Service Market and to estimate the size of various other dependent submarkets. The research methodology used to estimate the market size includes the following details: The key players in the market were identified through secondary research, and their market shares in the respective regions were determined through primary and secondary research. This entire procedure includes the study of the annual and financial reports of the top market players and extensive interviews for key insights from industry leaders such as CEOs, VPs, directors, and marketing executives. All percentage shares split, and breakdowns were determined using secondary sources and verified through Primary sources. All possible parameters that affect the markets covered in this research study have been accounted for, viewed in extensive detail, verified through primary research, and analyzed to get the final quantitative and qualitative data.
Global GPU As A Service Market Segmental Analysis
Based on product, service type, delivery model, application, and region, the global GPU as a service market is divided into many segments. Software, CAD/CAM, simulation, imaging, digital video, modeling & automation, others, service, managed service, updates & maintenance, compliance & security, and others are the product categories into which the market is divided. The market has three segments based on service models: SaaS, PaaS, and IaaS. The market is divided into public cloud, private cloud, and hybrid cloud segments based on the delivery model. The market is divided into several segments based on application, including gaming, design & manufacturing, automotive, real estate, and healthcare. The market is divided into North America, Europe, Latin America, Asia-Pacific, the Middle East, and Africa based on geographic regions.
Drivers of the Global GPU As A Service Market
The market for GPU as a Service (GPUaaS) is being driven in large part by the growing need for high-performance computing to enable AI and machine learning applications. The ability of GPUs to analyze data in parallel improves the speed and efficiency of training and inferring AI models, which is why companies are compelled to use GPUaaS in order to use this computational capacity.
Restraints in the Global GPU As A Service Market
Low latency is necessary for real-time applications that largely rely on GPUs, including gaming and AR/VR. But network latency can affect GPUaaS application performance, which can be problematic for apps that need fast response times.
Market Trends of the Global GPU As A Service Market
Edge GPUaaS: There is a growing movement to bring GPUaaS capabilities to the periphery. Edge GPUaaS lowers latency and improves responsiveness by enabling real-time processing for IoT, edge AI, and remote monitoring applications.
Specialized GPU Instances: Vendors are supplying GPU instances that are tailored to particular tasks, such rendering, scientific simulations, and AI training. Users can now choose GPU configurations based on their own requirements thanks to this trend.
Hybrid Cloud Deployments: Companies are implementing hybrid cloud strategies that integrate public and private cloud services with on-premises infrastructure. By allocating GPU resources optimally, this method addresses both data security issues and a range of computational demands.