Kubernetes faces challenges managing specialized hardware failures in AI/ML workloads. These workloads heavily rely on GPUs and other accelerators, and failures significantly impact performance. The existing Kubernetes model assumes static resources, lacking robust support for hardware failures. AI/ML workloads, including training and inference, differ significantly from traditional applications in their resource needs and failure implications. Training jobs are resource-intensive and require coordinated restarts upon failure, while inference tasks demand continuous operation. Existing Kubernetes assumptions, like easy resource replacement and simple pod scaling, are insufficient for these complex scenarios. Despite these challenges, Kubernetes remains the preferred platform due to its maturity and ecosystem. Current solutions involve manual workarounds like custom health controllers and pod watchers to manage device failures. These solutions have limitations and require privileged access, highlighting a need for improved native Kubernetes support. The Kubernetes project is actively working to improve device failure handling, aiming for better reliability and resource management.