How to deploy AI workloads across multiple GPU providers without rewriting your config every time Tags： gpu devops machinelearning infrastructure

this took me longer to figure out than it should have the problem: i wanted to run GPU workloads across multiple providers for availability and cost reasons, but every time i moved a workload or added a provider i was rebuilding deployment config from scratch. not because the workload changed, but because the config was hardcoded to one provider’s infrastructure. approaches i tried that didn’t solve it Kubernetes with provider-specific node pools handles orchestration inside a cluste...