Industry Newsgpugpu as a servicekubernetesresource utilization
Organizations Adopt Shared GPU-as-a-Service Platforms For ML Workloads
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On Feb. 10, 2026, DZone reports that organizations increasingly shift from dedicated GPUs to shared GPU-as-a-Service platforms. The article says dedicated GPU solutions are becoming infeasible and expensive, driving adoption of shared Kubernetes clusters that allow multiple teams to consume GPU resources. This trend aims to improve utilization and lower costs for diverse ML training, inference, analytics, and simulation workloads.
