A graphics processing unit (GPU) is a specialized electronic circuit designed to rapidly manipulate and alter memory to accelerate the creation of images in a frame buffer intended for output to a display device. GPUs are used in embedded systems, mobile phones, personal computers, workstations, and game consoles. Modern GPUs are very efficient at manipulating computer graphics and image processing, and their highly parallel structure makes them more efficient than general-purpose CPUs for algorithms where the processing of large blocks of data is done in parallel. In a personal computer, a GPU can be present on a video card, or it can be embedded on the motherboard or—in certain CPUs—on the CPU die.
The GPU for Deep Learning market was valued at xx Million US$ in 2018 and is projected to reach xx Million US$ by 2025, at a CAGR of xx% during the forecast period. In this study, 2018 has been considered as the base year and 2019 to 2025 as the forecast period to estimate the market size for GPU for Deep Learning.
This report presents the worldwide GPU for Deep Learning market size (value, production and consumption), splits the breakdown (data status 2014-2019 and forecast to 2025), by manufacturers, region, type and application.
This study also analyzes the market status, market share, growth rate, future trends, market drivers, opportunities and challenges, risks and entry barriers, sales channels, distributors and Porter's Five Forces Analysis.
The following manufacturers are covered in this report:
GPU for Deep Learning Breakdown Data by Type
RAM 4~8 GB
GPU for Deep Learning Breakdown Data by Application
GPU for Deep Learning Production by Region
GPU for Deep Learning Consumption by Region
Rest of Europe
Central & South America
Rest of South America
Middle East & Africa
Rest of Middle East & Africa
The study objectives are:
To analyze and research the global GPU for Deep Learning status and future forecast，involving, production, revenue, consumption, historical and forecast.
To present the key GPU for Deep Learning manufacturers, production, revenue, market share, and recent development.
To split the breakdown data by regions, type, manufacturers and applications.
To analyze the global and key regions market potential and advantage, opportunity and challenge, restraints and risks.
To identify significant trends, drivers, influence factors in global and regions.
To analyze competitive developments such as expansions, agreements, new product launches, and acquisitions in the market.
In this study, the years considered to estimate the market size of GPU for Deep Learning :
History Year: 2014 - 2018
Base Year: 2018
Estimated Year: 2019
Forecast Year: 2019 - 2025
This report includes the estimation of market size for value (million USD) and volume (K Units). Both top-down and bottom-up approaches have been used to estimate and validate the market size of GPU for Deep Learning market, to estimate the size of various other dependent submarkets in the overall market. Key players in the market have been identified through secondary research, and their market shares have been determined through primary and secondary research. All percentage shares, splits, and breakdowns have been determined using secondary sources and verified primary sources.
For the data information by region, company, type and application, 2018 is considered as the base year. Whenever data information was unavailable for the base year, the prior year has been considered.