Edera GPUs is a workload isolation platform designed for GPU infrastructure management. The platform enables multiple workloads to securely share GPU resources through virtualization and partitioning capabilities. The system divides GPU memory and compute resources into right-sized virtual GPUs (vGPUs), allowing multiple applications to run on a single physical GPU with guaranteed isolation. This approach addresses GPU utilization challenges by improving resource efficiency from typical 20-30% utilization rates to 60-80%. The platform includes an intelligent scheduler that automates GPU resource allocation across workloads. It supports all major AI/ML frameworks without requiring code modifications and operates across different infrastructure types including cloud VMs, on-premises VMs, and bare metal environments. Key capabilities include dynamic resource allocation that shifts GPU resources between workloads based on changing priorities, multi-tenant support for running multiple teams or projects on shared infrastructure, and the ability to pool and schedule GPU resources for distributed training across multi-node clusters. The platform is designed for use cases including dense inferencing where multiple inference models are co-located on single GPUs, model development with fractional GPU access for research, distributed training jobs, and centralized GPU-as-a-service platforms. Edera integrates with Kubernetes environments and is available on AWS GovCloud.