PE8000G i9-14900/T

Description

ASUS IoT PE8000G is a powerful edge AI computer that supports multiple GPU cards for high performance – and expertly engineered to handle rugged conditions with resistance to extreme temperatures, vibration and variable voltage. PE8000G is powered by formidable Intel® Core™ processors (14th, 13th and 12th gen) and the Intel R680E chipset to deliver high-octane processing power and efficiency.

With its advanced architecture, PE8000G excels at running multiple neural network modules simultaneously in real-time – and represents a significant leap forward in edge AI computing. With its robust design, exceptional performance and wide range of features, PE8000G series is poised to revolutionize AI-driven applications across multiple industries, elevating edge AI computing to new heights and enabling organizations to tackle mission-critical tasks with confidence and to achieve unprecedented levels of productivity and innovation.

Dual-GPU power for seamless AI inferencing and imbued with industrial strength

PE8000G can support two graphics cards that each draw up to 450 watts, enabling redundancy, efficient high-throughput computing, seamless real-time AI inferencing and accelerated computing at the edge. In addition, PE8000G is able to handle an 8—48V DC-input range and offers built-in ignition power control and monitoring, for flexible power options in diverse deployment scenarios. Plus, it is engineered to adhere to exacting MIL-STD-810H military specifications for resistance to vibration and jolting. The fail-safe mechanism empowered by dual GPUs helps to provide accurate inference results even in challenging conditions, for reliability and confidence in AI-driven decision-making.

Optimized for computer vision and perception, and ready for the road

PE8000G is optimized for in-vehicle environments, featuring integrated ignition power control and power monitoring capabilities. It also excels in AI-driven factory automation, intelligent video analytics (IVA) and deployments in rugged environments such as roadside units (RSUs) and autonomous driving. Efficient pre-processing and perception capabilities can optimize data preparation and enhance the accuracy of AI inferencing.