Google has added GPU and TPU support to managed DRANET on Google Kubernetes Engine Autopilot clusters, extending automated network resource allocation for accelerator-based workloads. GKE Autopilot users can now set up Pods that request network interfaces for TPUs and Remote Direct Memory Access, or RDMA, without managing the underlying nodes directly. The feature works through a combination of Autopilot clusters, custom ComputeClass definitions and ResourceClaimTemplate objects. The Autopilot configuration starts with a Virtual Private Cloud network and a regional cluster. For GPU deployments, the example setup uses an Nvidia B200 configuration on an a4-highgpu-8g machine type with eight GPUs and an automatic accelerator network profile. For TPU deployments, the example ComputeClass uses a TPU v6e slice with a count of eight and a 2x4 topology in a specified zone. GPU workloads using RDMA reference the device class mrdma.google.com, while TPU workloads use the non-RDMA device class netdev.google.com. The GPU deployment example shows a two-replica application serving the Gemma 4 31B model with vLLM, requesting 10 CPUs, 1000Gi of memory, 1Ti of ephemeral storage and eight Nvidia GPUs per container. According to Google, launching the deployment triggers a scale-up operation in GKE Autopilot.