NEW YORK, Oct. 4, 2022 /PRNewswire/ -- The Insight Partners published latest research study on "GPU Database Market Size, Share, Revenue, Growth Strategy, Industry Trends and Forecast to 2028 - COVID-19 Impact and Global Analysis - by Component, Deployment, Application, and Industry Vertical," the global GPU database market size is projected to grow from $388.81 million in 2022 to $1,112.11 million by 2028; it is estimated to grow at a CAGR of 19.1% from 2022 to 2028.
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Global GPU Database Market Report Scope, Segmentations, Regional & Country Scope:
Report Coverage | Details |
Market Size Value in | US$ 388.81 Million in 2022 |
Market Size Value by | US$ 1,112.11 Million by 2028 |
Growth rate | CAGR of 19.1% from 2022 to 2028 |
Forecast Period | 2022-2028 |
Base Year | 2022 |
No. of Pages | 192 |
No. of Tables | 115 |
No. of Charts & Figures | 98 |
Historical data available | Yes |
Segments covered | Component, Deployment, Application, and Industry Vertical |
Regional scope | North America; Europe; Asia Pacific; Latin America; MEA |
Country scope | US, UK, Canada, Germany, France, Italy, Australia, Russia, China, Japan, South Korea, Saudi Arabia, Brazil, Argentina |
Report coverage | Revenue forecast, company ranking, competitive landscape, growth factors, and trends |
Companies Covered | BRYTLYT; GRAPHISTRY; H2O.ai; Jedox; Kinetica DB Inc; Neo4J, INC; SQream Technologies; ZILLIZ; NVIDIA CORPORATION; HEAVY.AI |
Key Research Capabilities | Global Market Assessment, Business Development Strategies, Competitive Landscape, Opportunity Analysis, Regional and Country Level Market Analysis, Market Entry Strategies, Market Dynamics, Risk and Return Assessments, Pricing Analysis, Market Size and Forecasting, Company Profiling, Value Chain Analysis, Expansion Strategies, SWOT Analysis, New Product Development |
Global GPU Database Market Forecast to 2028 - Latest COVID-19 Analysis | https://www.theinsightpartners.com/covid-analysis-sample/TIPRE00006161/ |
Browse key market insights spread across 192 pages with 115 list of tables & 98 list of figures from the report, "GPU Database Market Forecast to 2028 - COVID-19 Impact and Global Analysis By Component (Tool and Service), Deployment (Cloud Based and On-Premise), by Application [Fraud Detection & Prevention, Governance Risk & Compliance (GRC), Customer Experience Management, Predictive Maintenance, Supply Chain Management, Threat Intelligence, and Others], Industrial Vertical (BFSI, IT & Telecom, Retail & E-Commerce, Healthcare, Transportation & Logistics, Government & Defense, and Others) and Geography" in detail along with the table of contents: https://www.theinsightpartners.com/reports/gpu-database-market
Global GPU Database Market: Competitive Landscape and Key Developments
BRYTLYT; GRAPHISTRY; H2O.ai; Jedox; Kinetica DB Inc; Neo4J, INC; SQream Technologies; ZILLIZ; NVIDIA CORPORATION; HEAVY.AI are the leading players operating in the GPU database industry. Several other essential players were analyzed during the GPU database market analysis for a holistic view of the GPU database market and its ecosystem. The report provides detailed GPU database market insights, which help the key players strategize their growth. A few developments are mentioned below:
In 2022, Zilliz announced its idea of raising a US$ 60 million extension to its initial $43 million Series B. Prosperity7 Ventures, the diversified growth fund under Aramco Ventures, led the round, with participation from existing investors Temasek's Pavilion Capital, Hillhouse Capital, 5Y Capital, and Yunqi Capital. This initiative brings total investment in the company to US$ 113 million. Following its Silicon Valley headquarters opening, Zilliz will use this funding influx to expand engineering and go-to-market teams. This initiative will enable Zilliz to double down on its continuous commitment to open source and enhance its managed cloud offering.
In 2022, H2o.ai launched its AI Hybrid Cloud, which allows users to manage an AI platform on-premise and in the cloud. With the new features, the company launched a fully-managed cloud named "H2O AI Cloud," which helps app developers and data to rapidly build AI models and apps by providing end-to-end solutions for the infrastructure.
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GPU-Accelerated Computing Would Continue to Enhance Machine Learning and Deep Learning Use-Cases:
The GPU-accelerated deep learning frameworks offer flexibility to design and train custom deep neural networks and provide interfaces to commonly used programming languages, which include C/C++ and Python. Deep learning is the preferable field of machine learning due to its ability to produce amazing results, which helps to achieve accurate and precise decisions in industries. The uptake of GPU in machine learning and deep learning is holding potential demand for GPU in the coming years. The GPU can have hundreds and thousands of cores. For instance, a standard high-performing CPU has 8-16 cores, whereas the NVIDIA GPU GeForce GTX TITAN Z has 5760 cores.
The GPU has a high memory bandwidth, allowing it to move massive data transferability between the memories. Since the GPU has a large number of cores and a large memory bandwidth, it can be used to perform high-speed parallel processing on any task that can be broken down for parallel computing. The GPU can perform high-speed parallel processing on any task that can be broken down for parallel computing. The GPU used in machine learning helps in the mathematical matrice operations of the neural network and falls into the parallel category. Thus, GPU can effortlessly break down the matrice operation of an extensive neural network, load a huge chunk of matrice data into memory due to high memory bandwidth, and can perform fast parallel processing with its thousands of cores.
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With the GPU's growing popularity for deep learning and machine learning, NVIDIA released CuDNN in 2014, a wrapper library built on CUDA for Deep Learning functions, such as convolution, backpropagation, pooling, and others. This made life easier for people to increase GPU adoption for deep learning without going through the low-level complexities of CUDA. In the coming years, all the popular deep learning libraries such as PyTorch, Matlab, Tensorflow, and MXNet will start incorporating CuDNN directly in their framework to give a seamless experience to users. Hence using a GPU for deep learning and machine learning has increased the flexibility compared to earlier days. Thus, such factors are trending the adoption of GPU database across various application that helps drive the GPU database market growth.
GPU Database Market: Industry Overview
Based on the GPU database market report, the GPU database market is segmented by component, deployment, application, industry vertical, and geography. Based on components, the GPU database market is bifurcated into tools and services. In terms of deployment, the GPU database market is segmented into on-premises and cloud-based. Based on application, the GPU database market is segmented into fraud detection & prevention, governance risk & compliance (GRC), customer experience management, predictive maintenance, supply chain management, threat intelligence, and others. Based on industry vertical, the GPU database market is segmented into BFSI, IT & telecom, retail & e-commerce, healthcare, transportation & logistics, government & defense, and others. Based on geography, the GPU database market is segmented into North America (the US, Canada, and Mexico), Europe (Germany, France, Italy, UK, Russia, and the Rest of Europe), and Asia Pacific (Australia, China, India, Japan, South Korea, and the Rest of APAC), the Middle East & Africa (South Africa, Saudi Arabia, the UAE, and the Rest of the MEA), and South America and Central America (Brazil, Argentina, and the Rest of SAM).
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