Modern AI workloads, ranging from large-scale training to real-time inference, demand dynamic access to powerful GPUs. However, Kubernetes environments have…
Modern AI workloads, ranging from large-scale training to real-time inference, demand dynamic access to powerful GPUs. However, Kubernetes environments have limited native support for GPU management, which leads to challenges such as inefficient GPU utilization, lack of workload prioritization and preemption, limited visibility into GPU consumption, and difficulty enforcing governance and quota…
