AI & Machine Learning

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How Do You Protect a GPU-Backed Workload When Snapshots and Mobility Have Limits?

TL;DR A GPU-backed workload is not recoverable merely because its virtual disks were copied or its Kubernetes manifests were committed to Git. vGPU, MIG-backed vGPU, PCI passthrough, Enhanced DirectPath, RDMA, and bare-metal GPU configurations expose different snapshot, suspend, migration, and failover boundaries. Some support controlled mobility within a narrow compatibility matrix. Others deliberately trade those […]

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From Approval Button to Control Plane: Implementing Human Review for AI Agents

Human approval is not useful just because a workflow pauses. It becomes useful when the system can prove what was proposed, why review was required, who reviewed it, what evidence they saw, what scope they approved, what executed, and whether the outcome matched the approved intent. That is the difference between an approval button and

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Whole GPU, Passthrough, vGPU, MIG, or Time Slicing? The Enterprise GPU Allocation Decision Matrix

Introduction Enterprise GPU design becomes confused when several different decisions are compressed into one question: “How should we share the GPU?” That question mixes hardware assignment, virtualization, Kubernetes scheduling, tenant isolation, business priority, and service-level commitments. The result is often a platform that advertises many GPU “slices” but cannot explain what each slice guarantees. A

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