diff --git a/docs/admin/kubernetes_deployment.md b/docs/admin/kubernetes_deployment.md
index de2631ad2..a4f8c14c1 100644
--- a/docs/admin/kubernetes_deployment.md
+++ b/docs/admin/kubernetes_deployment.md
@@ -1,6 +1,9 @@
# Kubernetes Deployment Installation Guide
KServe supports `RawDeployment` mode to enable `InferenceService` deployment with Kubernetes resources [`Deployment`](https://kubernetes.io/docs/concepts/workloads/controllers/deployment), [`Service`](https://kubernetes.io/docs/concepts/services-networking/service), [`Ingress`](https://kubernetes.io/docs/concepts/services-networking/ingress) and [`Horizontal Pod Autoscaler`](https://kubernetes.io/docs/tasks/run-application/horizontal-pod-autoscale). Comparing to serverless deployment it unlocks Knative limitations such as mounting multiple volumes, on the other hand `Scale down and from Zero` is not supported in `RawDeployment` mode.
+** Starting with Kserve vx.xx release `InferenceGraph` as well supports `RawDeployment` mode
+See release notes
+
Kubernetes 1.22 is the minimally required version and please check the following recommended Istio versions for the corresponding
Kubernetes version.
diff --git a/docs/blog/articles/2024-XX-XX-KServe-X.XX-release.md b/docs/blog/articles/2024-XX-XX-KServe-X.XX-release.md
new file mode 100644
index 000000000..825b81a72
--- /dev/null
+++ b/docs/blog/articles/2024-XX-XX-KServe-X.XX-release.md
@@ -0,0 +1,88 @@
+# Announcing: KServe vx.xx
+
+We are excited to announce the release of KServe x.xx, in this release we made enhancements to the KServe control plane, especially brining RawDeployment for `InferenceGraph` as well. Previously `RawDeployment` existed only for `InferenceService`
+
+Here is a summary of the key changes:
+
+## KServe Core Inference Enhancements
+
+- Inference Graph enhancements for supporting `RawDeployment` along with Auto Scaling configuration right within the `InferenceGraphSpec`
+
+IG `RawDeployment` makes the deployment light weight using native k8s resources. See the comparison below
+
+
+
+
+
+AutoScaling configuration fields were introduced to support scaling needs in
+`RawDeployment` mode. These fields are optional and when added effective only when this annotation `serving.kserve.io/autoscalerClass` not pointing to `external`
+ see the following example with Auto scaling fields `MinReplicas`, `MaxReplicas`, `ScaleTarget` and `ScaleMetric`:
+
+ ```yaml
+ apiVersion: serving.kserve.io/v1alpha1
+ kind: InferenceGraph
+ metadata:
+ name: graph_with_switch_node
+ annotations:
+ serving.kserve.io/deploymentMode: "RawDeployment"
+ spec:
+ nodes:
+ root:
+ routerType: Sequence
+ steps:
+ - name: "rootStep1"
+ nodeName: node1
+ dependency: Hard
+ - name: "rootStep2"
+ serviceName: {{ success_200_isvc_id }}
+ node1:
+ routerType: Switch
+ steps:
+ - name: "node1Step1"
+ serviceName: {{ error_404_isvc_id }}
+ condition: "[@this].#(decision_picker==ERROR)"
+ dependency: Hard
+ MinReplicas: 5
+ MaxReplicas: 10
+ ScaleTarget: 50
+ ScaleMetric: "cpu"
+ ```
+ For more details please refer to the [issue](https://github.com/kserve/kserve/issues/2454).
+
+-
+
+### Enhanced Python SDK Dependency Management
+
+-
+-
+
+### KServe Python Runtimes Improvements
+-
+
+### LLM Runtimes
+
+#### TorchServe LLM Runtime
+
+#### vLLM Runtime
+
+## ModelMesh Updates
+
+### Storing Models on Kubernetes Persistent Volumes (PVC)
+
+### Horizontal Pod Autoscaling (HPA)
+
+### Model Metrics, Metrics Dashboard, Payload Event Logging
+
+## What's Changed? :warning:
+
+## Join the community
+
+- Visit our [Website](https://kserve.github.io/website/) or [GitHub](https://github.com/kserve)
+- Join the Slack ([#kserve](https://kubeflow.slack.com/?redir=%2Farchives%2FCH6E58LNP))
+- Attend our community meeting by subscribing to the [KServe calendar](https://wiki.lfaidata.foundation/display/kserve/calendars).
+- View our [community github repository](https://github.com/kserve/community) to learn how to make contributions. We are excited to work with you to make KServe better and promote its adoption!
+
+
+Thanks for all the contributors who have made the commits to 0.11 release!
+
+The KServe Working Group
diff --git a/docs/images/2024-xx-xx-Kserve-x-xx-release/ig_knative_deployment.png b/docs/images/2024-xx-xx-Kserve-x-xx-release/ig_knative_deployment.png
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index 000000000..4ff121bf0
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diff --git a/docs/images/2024-xx-xx-Kserve-x-xx-release/ig_raw_deployment.png b/docs/images/2024-xx-xx-Kserve-x-xx-release/ig_raw_deployment.png
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index 000000000..21fd35e6f
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diff --git a/docs/reference/api.md b/docs/reference/api.md
index 0fe45d299..08eb7e8aa 100644
--- a/docs/reference/api.md
+++ b/docs/reference/api.md
@@ -524,6 +524,63 @@ Kubernetes core/v1.Affinity
(Optional)
+
+
+
+minReplicas
+
+int
+
+ |
+
+(Optional)
+ Minimum number of replicas, defaults to 1 but can be set to 0 to enable scale-to-zero.
+ |
+
+
+
+maxReplicas
+
+int
+
+ |
+
+(Optional)
+ Maximum number of replicas for autoscaling.
+ |
+
+
+
+scaleTarget
+
+int
+
+ |
+
+(Optional)
+ ScaleTarget specifies the integer target value of the metric type the Autoscaler watches for.
+concurrency and rps targets are supported by Knative Pod Autoscaler
+(https://knative.dev/docs/serving/autoscaling/autoscaling-targets/).
+ |
+
+
+
+scaleMetric
+
+
+ScaleMetric
+
+
+ |
+
+(Optional)
+ ScaleMetric defines the scaling metric type watched by autoscaler
+possible values are concurrency, rps, cpu, memory. concurrency, rps are supported via
+Knative Pod Autoscaler(https://knative.dev/docs/serving/autoscaling/autoscaling-metrics).
+ |
+
+
+
InferenceGraphStatus