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GPU Scheduling Is a Business Policy Problem: Designing NVIDIA Run:ai Quotas, Fairness, and Preemption

Introduction A GPU cluster does not know which product launch is contractually committed, which research experiment can wait until tomorrow, or which inference endpoint supports a revenue-producing application. Kubernetes sees pods, resource requests, labels, and scheduling constraints. The business sees customers, deadlines, budgets, risk, and service commitments. That gap is where many shared GPU platforms […]

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Stay on VMware Cloud Foundation or Replatform? A CIO Decision Framework for Private Cloud Modernization

TL;DR The decision to stay on VMware Cloud Foundation or replatform should not be reduced to a licensing reaction, a hypervisor feature comparison, or a vendor preference. It is a private cloud operating-model decision involving workload compatibility, staff capability, migration risk, ecosystem dependencies, automation maturity, lifecycle economics, and future exit complexity. For many enterprises, the

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Azure Local Has Two SDN Operating Models: Arc-Managed Networking Versus Full On-Premises SDN

Introduction Azure Local networking becomes confusing when the same words appear in several different product contexts. Logical network, virtual network, network security group, load balancer, gateway, and Network Controller all sound familiar to anyone who has worked with Azure or VMware NSX. The names create an understandable expectation that the underlying capabilities and operating models

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On-Prem Private AI Series: VMware Cloud Foundation 9.1 as the Private AI Operating Model

TL;DR VMware Cloud Foundation 9.1 matters for private AI because it does not treat AI as a separate island. It pulls AI workloads into the same private cloud operating model many enterprises already use for virtual machines, Kubernetes, storage, networking, security, lifecycle, and operations. That is the strength of VMware’s approach, but it is also

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