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A CNCF article argues that operating enterprise AI infrastructure is not about deploying a single model or cluster, but about managing a shared fleet of GPU units for multiple teams while balancing utilization, isolation, and cost. The article presents technical layers covering provisioning, allocation, scheduling, networking, storage, monitoring, and billing.
A CNCF analysis concludes that Dynamic Resource Allocation does not make HAMi redundant. Instead, it absorbs part of GPU quota scheduling while HAMi-core remains responsible for enforcing limits inside containers. The article presents the architecture of HAMi-DRA, its operating requirements, and the limitations to consider before migrating to it.