VMware Private AI Foundation Capabilities and Features Update from Broadcom
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."
AI Expert note
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.
What you should get from this
Evaluate private AI as an infrastructure and governance decision instead of defaulting to either SaaS or self-hosting by instinct.
Watch or know first
Basic understanding of cloud or on-prem infrastructure, Kubernetes, GPUs and why confidential data changes AI architecture.
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