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
Table of contents
The Current State: Traditional GPU AllocationWhy This Model is Misaligned with AI WorkloadsReality Check : An ExampleThe Opportunity: Rethinking GPU AllocationConclusionSort: