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In this guide, we outline considerations and best practices for designing such a heterogeneous infrastructure including how to leverage different GPU models, high-speed storage, and networking to maximize performance for both training and inference workloads. WHY HETEROGENEOUS. Modern AI models are data-hungry, computation-heavy beasts that need specialized hardware just to function, let alone perform at their best. That's the job of an AI server—a custom-built system that keeps AI applications fast, scalable, and efficient. An AI server's architecture is all about. There are gigawatt-scale data centers to be built in the coming years, primarily to support AI workloads. Intel's advanced, heterogeneous hardware capabilities combined with Wipro's consulting and software integration expertise is. Heterogeneous computing addresses these challenges by combining various specialized processors into a unified system, enhancing overall efficiency.
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