diff --git a/applications/ray/TPU_guide.md b/applications/ray/TPU_guide.md
index 73e2f7e95..b05eef15e 100644
--- a/applications/ray/TPU_guide.md
+++ b/applications/ray/TPU_guide.md
@@ -1,106 +1 @@
-# TPU User Guide
-
-This page contains instructions for how to set up Ray on GKE with TPUs. 
-
-For general setup instructions please refer to the [README](https://github.com/GoogleCloudPlatform/ai-on-gke/blob/main/applications/ray/README.md)
-file. 
-
-For more information about TPUs on GKE, see [this page](https://cloud.google.com/kubernetes-engine/docs/concepts/tpus).
-
-
-### Installing the GKE Platform
-
-1. If needed, git clone https://github.com/GoogleCloudPlatform/ai-on-gke
-
-2. `cd ai-on-gke/infrastructure`
-
-3. Edit `platform.tfvars` with your GCP settings.
-
-4. Change the region or zone to one where TPUs are available (see [this link](https://cloud.google.com/tpu/docs/regions-zones) for details.
-For v4 TPUs (the default type), the region should be set to `us-central2` or `us-central2-b`.
-
-5. Set the following flags (note that TPUs are currently only supported on GKE standard):
-
-```
-autopilot_cluster = false
-...
-enable_tpu = true
-```
- 
-6. Change the following lines in the `tpu_pools` configuration to match your desired [TPU accelerator](https://cloud.google.com/tpu/docs/supported-tpu-configurations#using-accelerator-type).
-```
-accelerator_count      = 2
-accelerator_type       = "nvidia-tesla-t4"
-```
-
-7. Run `terraform init && terraform apply -var-file platform.tfvars`
-
-
-### Installing the TPU Initialization Webhook
-
-The TPU Initialization Webhook automatically injects the `TPU_WORKER_ID`, `TPU_NAME`, and `TPU_WORKER_HOSTNAMES` environment variables necessary for multi-host TPU clusters. The webhook needs to be installed once per GKE cluster and requires a Kuberay Operator running v1.1 and GKE cluster version of 1.28+. The instructions can be found [here](https://github.com/GoogleCloudPlatform/ai-on-gke/blob/main/applications/ray/kuberay-tpu-webhook).
-
-### Creating the Kuberay Cluster
-
-1. Get the GKE cluster name and location/region from `infrastructure/platform.tfvars`.
-   Run `gcloud container clusters get-credentials %gke_cluster_name% --location=%location%`.
-   Configuring `gcloud` [instructions](https://cloud.google.com/sdk/docs/initializing)
-
-2. `cd ../applications/ray`
-
-3. Edit `workloads.tfvars` with your GCP settings. Replace `<your project ID>` and `<your cluster name>` with the names you used in `platform.tfvars`.
-
-4. Run `terraform init && terraform apply -var-file workloads.tfvars`
-
-This should deploy a Kuberay cluster with a single TPU worker node (v4 TPU with `2x2x1` topology). 
-
-To deploy a multi-host Ray Cluster, modify the `worker` spec [here](https://github.com/GoogleCloudPlatform/ai-on-gke/blob/main/modules/kuberay-cluster/kuberay-tpu-values.yaml) by changing the `cloud.google.com/gke-tpu-topology` `nodeSelector` to a multi-host topology. Set the `numOfHosts` field in the `worker` spec to the number of hosts specified by your chosen topology. For v4 TPUs, each TPU VM has access to 4 TPU chips. Therefore, you can calculate the number of TPU VM hosts by taking the product of the topology and dividing by 4 (i.e. a 2x2x4 TPU podslice will have 4 TPU VM hosts).
-
-### Running Sample Workloads
-
-Install JupyterHub according to the instructions in the [README](https://github.com/GoogleCloudPlatform/ai-on-gke/blob/main/applications/jupyter/README.md).
-
-A basic JAX program can be found [here](https://github.com/GoogleCloudPlatform/ai-on-gke/blob/main/applications/ray/example_notebooks/jax-tpu.ipynb).
-
-For a more advanced workload running Stable Diffusion on TPUs, see [here](https://github.com/GoogleCloudPlatform/ai-on-gke/blob/main/applications/ray/example_notebooks/stable-diffusion-tpu.ipynb).
-
-
-### (Optional) Initializing JAX Environment Manually
-
-To manually set JAX environment variables for `TPU_WORKER_ID` and `TPU_WORKER_HOSTNAMES` before initializing JAX without using the webhook, run the following sample code:
-
-```
-@ray.remote(resources={"google.com/tpu": 4})
-def get_hostname():
-    import time
-    time.sleep(1)
-    return ray.util.get_node_ip_address()
-
-
-@ray.remote(resources={"google.com/tpu": 4})
-def init_tpu_env_from_ray(id_hostname_map):
-    import os
-    import time
-    
-    time.sleep(1)
-    hostname = ray.util.get_node_ip_address()
-    worker_id = id_hostname_map[hostname]
-    
-    os.environ["TPU_WORKER_ID"] = str(worker_id)
-    os.environ["TPU_WORKER_HOSTNAMES"] = ",".join(list(id_hostname_map))
-
-    return "TPU_WORKER_ID: " + os.environ["TPU_WORKER_ID"] + " TPU_WORKER_HOSTNAMES: " + os.environ["TPU_WORKER_HOSTNAMES"]
-
-
-def init_jax_from_ray(num_workers: int):
-    results = ray.get([get_hostname.remote() for x in range(num_workers)])
-    id_hostname_map = {
-        hostname: worker_id for worker_id, hostname in enumerate(set(results))}
-
-    result = [init_tpu_env_from_ray.remote(id_hostname_map) for _ in range(num_workers)]
-    print(ray.get(result))
-
-
-init_jax_from_ray(num_workers=2)
-
-``` 
+Please see the TPU guide [here](https://github.com/GoogleCloudPlatform/ai-on-gke/blob/main/ray-on-gke/guides/tpu/README.md).