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Dual Full-length GPU Expandable Computers  (GP-3000 Series)

Dual Full-length GPU Expandable Computers (GP-3000 Series)

GP-3000 is an expandable high-performance GPU computer. An Intel® workstation-grade processor and up to two 250W full-length graphic cards provide unparalleled performance within a total 720W power budget. GP-3000 features the unique GPU Expansion Box (GEB) design, whereby a single- or dual-GPU GEB is attached to the main embedded system to add GPU processing power. The additional GPU performance massively accelerates complex industrial AI and machine vision tasks.

GP-3000 is engineered to deliver superior performance, fast data storage, and high-speed I/O connectivity. The combination of high-quality mechanical design and industrial ruggedness (MIL-STD-810G compliance) brings new possibilities and innovative transformations for AI applications at the edge. #GPU computer

Dual Full-length GPU Expandable Computers (GP-3000 Series)

GP-3000 is an expandable high-performance GPU computer. An Intel® workstation-grade processor and up to two 250W full-length graphic cards provide unparalleled performance within a total 720W power budget. GP-3000 features the unique GPU Expansion Box (GEB) design, whereby a single- or dual-GPU GEB is attached to the main embedded system to add GPU processing power. The additional GPU performance massively accelerates complex industrial AI and machine vision tasks.

GP-3000 is engineered to deliver superior performance, fast data storage, and high-speed I/O connectivity. The combination of high-quality mechanical design and industrial ruggedness (MIL-STD-810G compliance) brings new possibilities and innovative transformations for AI applications at the edge.
 
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GPU Expansion Box

GP-3000 embedded computer supports the expansion of up to two 250 W 328 mm full-length graphics cards as well as other PCIe add-on cards through GPU Expansion Box (GEB). The powerful GPU-based acceleration can be incorporated into edge computing solutions to provide intensive processing parallelism and performance-driven analytics.
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