Declarative Infrastructure Management for AI Clusters
Managing heterogeneous GPU hardware (NVIDIA H100, B200, AMD MI300) across large Kubernetes clusters requires specialized control loops that understand tensor parallelism, driver versions, and CUDA runtimes. InferCluster provides a Kubernetes-native operator that abstracts hardware complexity behind declarative Custom Resource Definitions (CRDs).
Deep Telemetry Collection via NVIDIA DCGM and eBPF
InferCluster operators integrate deeply with NVIDIA Data Center GPU Manager (DCGM) and Linux eBPF probes. The operator collects high-frequency telemetry—including SM occupancy, PCIe bandwidth saturation, NVLink error counters, and HBM temperature—streaming metrics into Prometheus with zero performance overhead.
Automated Node Cordoning and Hardware Failure Isolation
Hardware degradation in dense GPU clusters (such as single-bit ECC memory errors or failing NVLink transceivers) can cause silent job corruption or sudden node crashes. InferCluster telemetry monitors error thresholds continuously; when a GPU exhibits pre-failure signatures, the operator cordons the node automatically, drains active inference sessions, and alerts platform engineers.
Seamless Integration with GitOps and ArgoCD
InferCluster CRDs enable platform teams to define entire AI inference clusters as code. Using ArgoCD or Flux, engineering teams synchronize cluster configurations, model deployments, and autoscaling policies directly from Git repositories, ensuring complete configuration consistency.
