Tech Field Day. Shows private AI as layered infrastructure: controlled compute, isolated environments, Kubernetes, inference containers, model governance, self-service provisioning, GPU sharing and monitoring. That maps directly to the article's warning that privacy depends on deployment boundaries, logs, access and operations, not on the word "local."
This is a VMware/Broadcom-specific architecture, so do not treat it as the only answer. Use it to understand the enterprise pattern, then compare it against VPC endpoints, enterprise SaaS, simpler self-hosted stacks and local-device workflows for the actual SME use case.
Evaluate private AI as an infrastructure and governance decision instead of defaulting to either SaaS or self-hosting by instinct.
Basic understanding of cloud or on-prem infrastructure, Kubernetes, GPUs and why confidential data changes AI architecture.
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