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Guide The data center landscape is transforming due to AI and ML. This necessitates a new back-end network in cloud and large enterprise data centers, designed for HPC workloads like AI training.
Guide About this Document This document is a generic design document for building network infrastructure for high-performance AI clusters.
Guide Server configurations like PCIe Optimized or HGX systems cater to different scales and performance needs. NVIDIA BlueField data processing units (DPUs) are essential for creating high
Guide Supporting this growth requires a fundamental shift in hardware design, as the architectural needs of AI differ significantly from those of conventional enterprise applications. The fundamental difference
Guide This guidance includes principles and design guides that influence AI and machine learning workloads across the five architecture pillars. Implement those recommendations in the
Guide AI/ML demands are reshaping servers. Explore how CPUs, GPUs, FPGAs and AI accelerators drive performance for workloads like deep learning and predictive analytics.
Guide Learn how AI workloads are reshaping server architecture with accelerators, CXL memory pooling, high-speed interconnects, and advanced cooling.
Guide AI server architecture combines specialized processors, high-speed connections, and intelligent design to handle AI''s computing demands.
Guide This paper proposes a comprehensive framework for designing AI engineering systems, addressing critical components such as data pipelines, computer architectures, model serving, distributed
Guide Explore key considerations for AI servers and how to design them to support AI workloads optimally.
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