Published: 05 August 2024
Competitive behaviors in cloud AI infrastructure are shifting as new differentiators emerge. This research provides product managers with examples of hyperscalers adopting AI supercomputing architectures, enhancing features to efficiently use GPUs and investing in AI processor hardware ecosystems.
To bring differentiated economies of scale to AI computing, hyperscale cloud providers are introducing AI supercomputing architectures that preintegrate and simplify use of AI accelerators, AI network fabric, performance-optimized storage and other components.
Due to ongoing GPU supply constraints and increasing small inference workloads, hyperscale cloud providers are enhancing infrastructure features to more efficiently use GPUs and AI processors, such as through sharing, reservation and dynamic workload scheduling.
To reduce the dependency on expensive, power-hungry GPUs, hyperscale cloud providers are accelerating the development of their own cloud-optimized AI processors and hardware ecosystems.
Cloud infrastructure as a service (IaaS) product managers responsible
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Analysts:
Wataru Katsurashima, Sid Nag, Evan Zeng
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