GPU-Ready Private Cloud Architecture: Design Choices That Matter
GPU workloads can break infrastructure assumptions built for CPU-first virtualization. If the platform, network, and storage layers are not designed for accelerator behavior, utilization drops and reliability risk rises. 1. Design for workload classes, not generic GPU pools Separate workloads by behavior: interactive inference batch inference model training experimentation and research Each class has different requirements for latency, throughput, tenancy isolation, and preemption policy. 2. Choose the right allocation model Common allocation modes: ...