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NetApp and Nvidia are co-engineering a new storage architecture that decouples heavy data movement from transactional metadata to prevent GPU idling during AI training. This shift addresses the unique demands of "AI factories," where shared infrastructure must simultaneously handle sequential checkpointing and rapid metadata updates without resource contention. By optimizing this data flow, the partnership aims to significantly improve hardware efficiency and reduce wasted compute power in large-scale AI deployments.

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NetApp and Nvidia rethink storage for AI factories

SiliconANGLEOctober 1, 20265 min read

Storage architecture is being rewritten for artificial intelligence factories. Traditional enterprise storage was designed around workloads that scaled in relatively predictable ways. AI changes that equation by combining heavy data movement with transactional metadata activity, often on shared infrastructure. NetApp Inc. is addressing that pressure through its work with Nvidia Corp., a partnership that dates […] The post NetApp and Nvidia rethink storage for AI factories appeared first on SiliconANGLE.

Storage architecture is being rewritten for artificial intelligence factories. Traditional enterprise storage was designed around workloads that scaled in relatively predictable ways. AI changes that equation by combining heavy data movement with transactional metadata activity, often on shared infrastructure. NetApp Inc. is addressing that pressure through its work with Nvidia Corp., a partnership that dates back more than a decade and now includes co-engineering around AI infrastructure. NetApp’s Novus architecture separates data and metadata functions so each can scale independently based on different workload demands while keeping GPU resources supplied with data, according…

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