The next phase of enterprise AI will not be built inside one provider’s platform. Models, accelerated compute, enterprise data, storage and network services increasingly reside in different environments and may be operated by different companies. The challenge is therefore shifting from selecting individual providers to assembling a distributed AI system that allows those providers to work together. We can already see this architecture taking shape: Model developers are moving datasets and checkpoints into neocloud GPU environments for training and fine-tuning. Software-defined storage providers are deploying services adjacent to those GPU clusters to reduce data-access bottlenecks. AI providers are connecting with network service providers…

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