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 global GPU for Deep Learning market was valued at xx million US$ in 2018 and will reach xx million US$ by the end of 2025, growing at a CAGR of xx% during 2019-2025.
This report focuses on GPU for Deep Learning volume and value at global level, regional level and company level. From a global perspective, this report represents overall GPU for Deep Learning market size by analyzing historical data and future prospect.
Regionally, this report categorizes the production, apparent consumption, export and import of GPU for Deep Learning in North America, Europe, China, Japan, Southeast Asia and India.
For each manufacturer covered, this report analyzes their GPU for Deep Learning manufacturing sites, capacity, production, ex-factory price, revenue and market share in global market.
The following manufacturers are covered:
Segment by Regions
Segment by Type
RAM 4~8 GB
Segment by Application