Rethinking GPU Allocation in Kubernetes

Traditional Kubernetes GPU allocation treats GPUs as indivisible units, forcing workloads to consume entire GPUs regardless of actual needs. This approach leads to significant underutilization, especially for inference jobs that only need a fraction of GPU resources. The current model lacks topology awareness for distributed training and prevents efficient GPU sharing among mixed workloads. Advanced GPU allocation strategies including fractional assignments, topology awareness, and dynamic scaling can dramatically improve resource efficiency and reduce infrastructure costs.