gpu & ai platform enablement

problem

gpu enablement creates competing requirements — performance, utilization, maintenance mobility, availability, and scheduling policy — that stakeholders must choose among deliberately.

approach

• —design gpu worker patterns on kubernetes with operator-based enablement and workload placement controls
• —document passthrough vs mobility tradeoffs so maintenance and performance decisions are explicit
• —evaluate hybrid options rather than pretending one architecture satisfies every constraint
• —connect ai/gpu demand to platform lifecycle, capacity, and customer communication

outcomes

• —stakeholder-ready option analysis instead of hidden operational debt
• —clearer path for ai workloads onto shared platforms
• —platform thinking applied to emerging gpu requirements

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