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Navigating Liquid Cooling Architectures for Data Centers with AI Workloads
Many AI servers with accelerators (e.g., GPUs) used for training LLMs (large language models) and inference workloads, generate enough heat to necessitate liquid cooling. These servers are equipped with input and output piping and require an ecosystem of manifolds, CDUs (cooling distribution) and outdoor heat rejection. There are six common heat rejection architectures for liquid cooling where we provide guidance on selecting the best one for your AI servers or cluster.
Date:
19 février 2024
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Type :
Livre blanc
Langues:
Anglais
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Version:
V2
Référence du document:
SPD_WP133_EN
Fichiers
Nom du fichier | ||
WP133_V2_EN.pdf
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