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Microsoft is addressing AI data center bottlenecks by shifting from isolated server memory to a composable architecture that dynamically pools resources across Azure workloads. This approach aims to eliminate idle GPU capacity and optimize memory allocation as supply constraints tighten, treating memory as a shared utility rather than a static component tied to specific processors.

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Microsoft looks to composable memory to ease AI infrastructure constraints

SiliconANGLEOctober 14, 20265 min read

Memory has become the scarcest resource in the AI data center, and hyperscalers are rethinking how they hand it out. Composable memory, which pools capacity beyond individual servers and assigns it to workloads on demand, is emerging as one of the most practical answers to that squeeze. The pressure is visible across AI factories where […] The post Microsoft looks to composable memory to ease AI infrastructure constraints appeared first on SiliconANGLE.

Memory has become the scarcest resource in the AI data center, and hyperscalers are rethinking how they hand it out. Composable memory, which pools capacity beyond individual servers and assigns it to workloads on demand, is emerging as one of the most practical answers to that squeeze. The pressure is visible across AI factories where graphics processing units sit idle waiting on memory and storage. Rather than leaving memory locked inside each server, Microsoft Corp.’s pathfinding team is scouting system-level technologies that can turn it into a…

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