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Why Data, Not Models, Determines AI Success
Strong models alone are not enough, and this article shows why data readiness, accessibility, and governance often determine whether AI succeeds in production.
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Because placement decisions affect cost, performance, and control, this piece examines how data gravity and latency shape where AI workloads should run.
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In this eSpeaks conversation, Vrashank Jain explains why fragmented environments, pipeline complexity, and data bottlenecks continue to slow enterprise AI progress.
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NVIDIA drip-feeds us more info on next-gen GF100 Fermi GPU
Die shot
Block diagram
SMs in detail
Raster and polymorph engines
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Adrian Kingsley-Hughes
