There does seem to be a way to get this to work, at least in a hacky way, as described here.

The knative Service configuration file does seem to accept and respect the limits: nvidia.com/gpu: 1 argument. While the Cloud Run interface does not allow us to specify this argument ourselves, we can use the kubectl CLI to manually deploy knative services defined by a yaml file which contains this argument.

First, we need to create a GKE cluster with a cpu node pool, a gpu node pool, and Cloud Run for Anthos enabled. This can be accomplished by going to Kubernetes Engine -> Create Cluster -> Selecting "GPU Accelerated Computing" on the left cluster templates bar -> Checking the "Enable Cloud Run for Anthos". Once the cluster is created, we can click the "connect" button and start a cloud shell. Here, we can create a service.yaml file which defines our knative service. For example, we can adapt the service.yaml file from the knative documentation but specify that this service requires a GPU:

# service.yaml
apiVersion: serving.knative.dev/v1 # Current version of Knative
kind: Service
metadata:
  name: helloworld-go # The name of the app
  namespace: default # The namespace the app will use
spec:
  template:
    spec:
      containers:
        - image: gcr.io/knative-samples/helloworld-go # The URL to the image of the app
          env:
            - name: TARGET # The environment variable printed out by the sample app
              value: "Go Sample v1"
          resources:
            limits:
              nvidia.com/gpu: 1 # The service must be run on a machine with at least one GPU

We can deploy this service using:

kubectl apply -f service.yaml

and check its status using:

kubectl get ksvc helloworld-go

The helloworld-go service should only be scheduled on nodes containing a GPU. The service should show up on the Cloud Run dashboard just like the other Cloud Run for Anthos services.