diff --git a/Dockerfile.sdk b/Dockerfile.sdk
index 36de500eed..f06dcb2501 100644
--- a/Dockerfile.sdk
+++ b/Dockerfile.sdk
@@ -29,7 +29,7 @@
 #
 
 # Base image on the minimum Triton container
-ARG BASE_IMAGE=nvcr.io/nvidia/tritonserver:26.01-py3-min
+ARG BASE_IMAGE=nvcr.io/nvidia/tritonserver:26.02-py3-min
 
 ARG TRITON_CLIENT_REPO_SUBDIR=clientrepo
 ARG TRITON_REPO_ORGANIZATION=http://github.com/triton-inference-server
diff --git a/README.md b/README.md
index 37f7a38251..ba1df4a423 100644
--- a/README.md
+++ b/README.md
@@ -27,11 +27,6 @@
 -->
 [![License](https://img.shields.io/badge/License-BSD3-lightgrey.svg)](https://opensource.org/licenses/BSD-3-Clause)
 
->[!WARNING]
->You are currently on the `main` branch which tracks under-development progress
->towards the next release. The current release is version [2.65.0](https://github.com/triton-inference-server/server/releases/latest)
->and corresponds to the 26.01 container release on NVIDIA GPU Cloud (NGC).
-
 # Triton Inference Server
 
 Triton Inference Server is an open source inference serving software that
@@ -61,7 +56,7 @@ Major features include:
 - Provides [Backend API](https://github.com/triton-inference-server/backend) that
   allows adding custom backends and pre/post processing operations
 - Supports writing custom backends in python, a.k.a.
-  [Python-based backends.](https://github.com/triton-inference-server/backend/blob/main/docs/python_based_backends.md#python-based-backends)
+  [Python-based backends.](https://github.com/triton-inference-server/backend/blob/r26.02/docs/python_based_backends.md#python-based-backends)
 - Model pipelines using
   [Ensembling](docs/user_guide/architecture.md#ensemble-models) or [Business
   Logic Scripting
@@ -90,16 +85,16 @@ Inference Server with the
 
 ```bash
 # Step 1: Create the example model repository
-git clone -b r26.01 https://github.com/triton-inference-server/server.git
+git clone -b r26.02 https://github.com/triton-inference-server/server.git
 cd server/docs/examples
 ./fetch_models.sh
 
 # Step 2: Launch triton from the NGC Triton container
-docker run --gpus=1 --rm --net=host -v ${PWD}/model_repository:/models nvcr.io/nvidia/tritonserver:26.01-py3 tritonserver --model-repository=/models --model-control-mode explicit --load-model densenet_onnx
+docker run --gpus=1 --rm --net=host -v ${PWD}/model_repository:/models nvcr.io/nvidia/tritonserver:26.02-py3 tritonserver --model-repository=/models --model-control-mode explicit --load-model densenet_onnx
 
 # Step 3: Sending an Inference Request
 # In a separate console, launch the image_client example from the NGC Triton SDK container
-docker run -it --rm --net=host nvcr.io/nvidia/tritonserver:26.01-py3-sdk /workspace/install/bin/image_client -m densenet_onnx -c 3 -s INCEPTION /workspace/images/mug.jpg
+docker run -it --rm --net=host nvcr.io/nvidia/tritonserver:26.02-py3-sdk /workspace/install/bin/image_client -m densenet_onnx -c 3 -s INCEPTION /workspace/images/mug.jpg
 
 # Inference should return the following
 Image '/workspace/images/mug.jpg':
@@ -172,10 +167,10 @@ configuration](docs/user_guide/model_configuration.md) for the model.
   [Python](https://github.com/triton-inference-server/python_backend), and more
 - Not all the above backends are supported on every platform supported by Triton.
   Look at the
-  [Backend-Platform Support Matrix](https://github.com/triton-inference-server/backend/blob/main/docs/backend_platform_support_matrix.md)
+  [Backend-Platform Support Matrix](https://github.com/triton-inference-server/backend/blob/r26.02/docs/backend_platform_support_matrix.md)
   to learn which backends are supported on your target platform.
 - Learn how to [optimize performance](docs/user_guide/optimization.md) using the
-  [Performance Analyzer](https://github.com/triton-inference-server/perf_analyzer/blob/main/README.md)
+  [Performance Analyzer](https://github.com/triton-inference-server/perf_analyzer/blob/r26.02/README.md)
   and
   [Model Analyzer](https://github.com/triton-inference-server/model_analyzer)
 - Learn how to [manage loading and unloading models](docs/user_guide/model_management.md) in
@@ -189,14 +184,14 @@ A Triton *client* application sends inference and other requests to Triton. The
 [Python and C++ client libraries](https://github.com/triton-inference-server/client)
 provide APIs to simplify this communication.
 
-- Review client examples for [C++](https://github.com/triton-inference-server/client/blob/main/src/c%2B%2B/examples),
-  [Python](https://github.com/triton-inference-server/client/blob/main/src/python/examples),
-  and [Java](https://github.com/triton-inference-server/client/blob/main/src/java/src/main/java/triton/client/examples)
+- Review client examples for [C++](https://github.com/triton-inference-server/client/blob/r26.02/src/c%2B%2B/examples),
+  [Python](https://github.com/triton-inference-server/client/blob/r26.02/src/python/examples),
+  and [Java](https://github.com/triton-inference-server/client/blob/r26.02/src/java/src/main/java/triton/client/examples)
 - Configure [HTTP](https://github.com/triton-inference-server/client#http-options)
   and [gRPC](https://github.com/triton-inference-server/client#grpc-options)
   client options
 - Send input data (e.g. a jpeg image) directly to Triton in the [body of an HTTP
-  request without any additional metadata](https://github.com/triton-inference-server/server/blob/main/docs/protocol/extension_binary_data.md#raw-binary-request)
+  request without any additional metadata](https://github.com/triton-inference-server/server/blob/r26.02/docs/protocol/extension_binary_data.md#raw-binary-request)
 
 ### Extend Triton
 
@@ -205,7 +200,7 @@ designed for modularity and flexibility
 
 - [Customize Triton Inference Server container](docs/customization_guide/compose.md) for your use case
 - [Create custom backends](https://github.com/triton-inference-server/backend)
-  in either [C/C++](https://github.com/triton-inference-server/backend/blob/main/README.md#triton-backend-api)
+  in either [C/C++](https://github.com/triton-inference-server/backend/blob/r26.02/README.md#triton-backend-api)
   or [Python](https://github.com/triton-inference-server/python_backend)
 - Create [decoupled backends and models](docs/user_guide/decoupled_models.md) that can send
   multiple responses for a request or not send any responses for a request
diff --git a/TRITON_VERSION b/TRITON_VERSION
index 189a1ca31a..3d6ac35b13 100644
--- a/TRITON_VERSION
+++ b/TRITON_VERSION
@@ -1 +1 @@
-2.66.0dev
+2.66.0
diff --git a/build.py b/build.py
index 3ab1e781ba..6cd189a094 100755
--- a/build.py
+++ b/build.py
@@ -71,14 +71,14 @@
 #
 
 DEFAULT_TRITON_VERSION_MAP = {
-    "release_version": "2.66.0dev",
-    "triton_container_version": "26.02dev",
-    "upstream_container_version": "26.01",
+    "release_version": "2.66.0",
+    "triton_container_version": "26.02",
+    "upstream_container_version": "26.02",
     "ort_version": "1.24.1",
     "ort_openvino_version": "2025.4.1",
     "standalone_openvino_version": "2025.4.1",
     "dcgm_version": "4.5.2-1",
-    "vllm_version": "0.13.0",
+    "vllm_version": "0.15.1",
     "rhel_py_version": "3.12.3",
 }
 
diff --git a/deploy/aws/values.yaml b/deploy/aws/values.yaml
index 45cda89942..cea66962df 100644
--- a/deploy/aws/values.yaml
+++ b/deploy/aws/values.yaml
@@ -27,7 +27,7 @@
 replicaCount: 1
 
 image:
-  imageName: nvcr.io/nvidia/tritonserver:26.01-py3
+  imageName: nvcr.io/nvidia/tritonserver:26.02-py3
   pullPolicy: IfNotPresent
   modelRepositoryPath: s3://triton-inference-server-repository/model_repository
   numGpus: 1
diff --git a/deploy/fleetcommand/Chart.yaml b/deploy/fleetcommand/Chart.yaml
index 8760515775..d098482d50 100644
--- a/deploy/fleetcommand/Chart.yaml
+++ b/deploy/fleetcommand/Chart.yaml
@@ -26,7 +26,7 @@
 
 apiVersion: v1
 # appVersion is the Triton version; update when changing release
-appVersion: 2.65.0
+appVersion: 2.66.0
 description: Triton Inference Server (Fleet Command)
 name: triton-inference-server
 # version is the Chart version; update when changing anything in the chart
diff --git a/deploy/fleetcommand/values.yaml b/deploy/fleetcommand/values.yaml
index bc6fb0b759..f5563dc3a1 100644
--- a/deploy/fleetcommand/values.yaml
+++ b/deploy/fleetcommand/values.yaml
@@ -27,7 +27,7 @@
 replicaCount: 1
 
 image:
-  imageName: nvcr.io/nvidia/tritonserver:26.01-py3
+  imageName: nvcr.io/nvidia/tritonserver:26.02-py3
   pullPolicy: IfNotPresent
   numGpus: 1
   serverCommand: tritonserver
@@ -47,13 +47,13 @@ image:
     #
     # To set model control mode, uncomment and configure below
     # TODO: Fix the following url, it is invalid
-    # See https://github.com/triton-inference-server/server/blob/r26.01/docs/user_guide/model_management.md
+    # See https://github.com/triton-inference-server/server/blob/r26.02/docs/user_guide/model_management.md
     #  for more details
     #- --model-control-mode=explicit|poll|none
     #
     # Additional server args
     #
-    # see https://github.com/triton-inference-server/server/blob/r26.01/README.md
+    # see https://github.com/triton-inference-server/server/blob/r26.02/README.md
     #  for more details
 
 service:
diff --git a/deploy/gcp/values.yaml b/deploy/gcp/values.yaml
index 4612dd4ae6..6ede77cf57 100644
--- a/deploy/gcp/values.yaml
+++ b/deploy/gcp/values.yaml
@@ -27,7 +27,7 @@
 replicaCount: 1
 
 image:
-  imageName: nvcr.io/nvidia/tritonserver:26.01-py3
+  imageName: nvcr.io/nvidia/tritonserver:26.02-py3
   pullPolicy: IfNotPresent
   modelRepositoryPath: gs://triton-inference-server-repository/model_repository
   numGpus: 1
diff --git a/deploy/gke-marketplace-app/benchmark/perf-analyzer-script/triton_client.yaml b/deploy/gke-marketplace-app/benchmark/perf-analyzer-script/triton_client.yaml
index 3d5abafa3b..149f72e63f 100644
--- a/deploy/gke-marketplace-app/benchmark/perf-analyzer-script/triton_client.yaml
+++ b/deploy/gke-marketplace-app/benchmark/perf-analyzer-script/triton_client.yaml
@@ -33,7 +33,7 @@ metadata:
   namespace: default
 spec:
   containers:
-  - image: nvcr.io/nvidia/tritonserver:26.01-py3-sdk
+  - image: nvcr.io/nvidia/tritonserver:26.02-py3-sdk
     imagePullPolicy: Always
     name: nv-triton-client
     securityContext:
diff --git a/deploy/gke-marketplace-app/server-deployer/build_and_push.sh b/deploy/gke-marketplace-app/server-deployer/build_and_push.sh
index 0121d266fb..a0ec4cb855 100755
--- a/deploy/gke-marketplace-app/server-deployer/build_and_push.sh
+++ b/deploy/gke-marketplace-app/server-deployer/build_and_push.sh
@@ -27,9 +27,9 @@
 
 export REGISTRY=gcr.io/$(gcloud config get-value project | tr ':' '/')
 export APP_NAME=tritonserver
-export MAJOR_VERSION=2.65
-export MINOR_VERSION=2.65.0
-export NGC_VERSION=26.01-py3
+export MAJOR_VERSION=2.66
+export MINOR_VERSION=2.66.0
+export NGC_VERSION=26.02-py3
 
 docker pull nvcr.io/nvidia/$APP_NAME:$NGC_VERSION
 
diff --git a/deploy/gke-marketplace-app/server-deployer/chart/triton/Chart.yaml b/deploy/gke-marketplace-app/server-deployer/chart/triton/Chart.yaml
index ddbc69c63c..683c55e47a 100644
--- a/deploy/gke-marketplace-app/server-deployer/chart/triton/Chart.yaml
+++ b/deploy/gke-marketplace-app/server-deployer/chart/triton/Chart.yaml
@@ -28,4 +28,4 @@ apiVersion: v1
 appVersion: "2.65"
 description: Triton Inference Server
 name: triton-inference-server
-version: 2.65.0
+version: 2.66.0
diff --git a/deploy/gke-marketplace-app/server-deployer/chart/triton/values.yaml b/deploy/gke-marketplace-app/server-deployer/chart/triton/values.yaml
index 15387d650d..f3e6454bba 100644
--- a/deploy/gke-marketplace-app/server-deployer/chart/triton/values.yaml
+++ b/deploy/gke-marketplace-app/server-deployer/chart/triton/values.yaml
@@ -31,14 +31,14 @@ maxReplicaCount: 3
 tritonProtocol: HTTP
 # HPA GPU utilization autoscaling target
 HPATargetAverageValue: 85
-modelRepositoryPath: gs://triton_sample_models/26.01
-publishedVersion: '2.65.0'
+modelRepositoryPath: gs://triton_sample_models/26.02
+publishedVersion: '2.66.0'
 gcpMarketplace: true
 
 image:
   registry: gcr.io
   repository: nvidia-ngc-public/tritonserver
-  tag: 26.01-py3
+  tag: 26.02-py3
   pullPolicy: IfNotPresent
   # modify the model repository here to match your GCP storage bucket
   numGpus: 1
diff --git a/deploy/gke-marketplace-app/server-deployer/data-test/schema.yaml b/deploy/gke-marketplace-app/server-deployer/data-test/schema.yaml
index c63bcbc2d5..63ac8d7664 100644
--- a/deploy/gke-marketplace-app/server-deployer/data-test/schema.yaml
+++ b/deploy/gke-marketplace-app/server-deployer/data-test/schema.yaml
@@ -27,7 +27,7 @@
 x-google-marketplace:
   schemaVersion: v2
   applicationApiVersion: v1beta1
-  publishedVersion: '2.65.0'
+  publishedVersion: '2.66.0'
   publishedVersionMetadata:
     releaseNote: >-
       Initial release.
diff --git a/deploy/gke-marketplace-app/server-deployer/schema.yaml b/deploy/gke-marketplace-app/server-deployer/schema.yaml
index f9c31d268a..2cdff30385 100644
--- a/deploy/gke-marketplace-app/server-deployer/schema.yaml
+++ b/deploy/gke-marketplace-app/server-deployer/schema.yaml
@@ -27,7 +27,7 @@
 x-google-marketplace:
   schemaVersion: v2
   applicationApiVersion: v1beta1
-  publishedVersion: '2.65.0'
+  publishedVersion: '2.66.0'
   publishedVersionMetadata:
     releaseNote: >-
       Initial release.
@@ -89,7 +89,7 @@ properties:
   modelRepositoryPath:
     type: string
     title: Bucket where models are stored. Please make sure the user/service account to create the GKE app has permission to this GCS bucket. Read Triton documentation on configs and formatting details, supporting TensorRT, TensorFlow, Pytorch, Onnx ... etc.
-    default: gs://triton_sample_models/26.01
+    default: gs://triton_sample_models/26.02
   image.ldPreloadPath:
     type: string
     title: Leave this empty by default. Triton allows users to create custom layers for backend such as TensorRT plugin, the compiled shared library must be provided via LD_PRELOAD environment variable.
diff --git a/deploy/gke-marketplace-app/trt-engine/README.md b/deploy/gke-marketplace-app/trt-engine/README.md
index 6f33ac0a1c..afbde3ff9f 100644
--- a/deploy/gke-marketplace-app/trt-engine/README.md
+++ b/deploy/gke-marketplace-app/trt-engine/README.md
@@ -33,7 +33,7 @@
 ```
 docker run --gpus all -it --network host \
     --shm-size=1g --ulimit memlock=-1 --ulimit stack=67108864 \
-    -v ~:/scripts nvcr.io/nvidia/tensorrt:26.01-py3
+    -v ~:/scripts nvcr.io/nvidia/tensorrt:26.02-py3
 
 pip install onnx six torch tf2onnx tensorflow
 
@@ -57,7 +57,7 @@ mkdir -p engines
 
 python3 builder.py -m models/fine-tuned/bert_tf_ckpt_large_qa_squad2_amp_128_v19.03.1/model.ckpt -o engines/bert_large_int8_bs1_s128.engine -b 1 -s 128 -c models/fine-tuned/bert_tf_ckpt_large_qa_squad2_amp_128_v19.03.1/ -v models/fine-tuned/bert_tf_ckpt_large_qa_squad2_amp_128_v19.03.1/vocab.txt --int8 --fp16 --strict --calib-num 1 -iln -imh
 
-gsutil cp bert_large_int8_bs1_s128.engine gs://triton_sample_models/26.01/bert/1/model.plan
+gsutil cp bert_large_int8_bs1_s128.engine gs://triton_sample_models/26.02/bert/1/model.plan
 ```
 
-For each Triton upgrade, container version used to generate the model, and the model path in GCS `gs://triton_sample_models/26.01/` should be updated accordingly with the correct version.
+For each Triton upgrade, container version used to generate the model, and the model path in GCS `gs://triton_sample_models/26.02/` should be updated accordingly with the correct version.
diff --git a/deploy/k8s-onprem/values.yaml b/deploy/k8s-onprem/values.yaml
index 347dc5ac42..405316696e 100644
--- a/deploy/k8s-onprem/values.yaml
+++ b/deploy/k8s-onprem/values.yaml
@@ -30,7 +30,7 @@ tags:
   openshift: false
 
 image:
-  imageName: nvcr.io/nvidia/tritonserver:26.01-py3
+  imageName: nvcr.io/nvidia/tritonserver:26.02-py3
   pullPolicy: IfNotPresent
   modelRepositoryServer: < Replace with the IP Address of your file server >
   modelRepositoryPath: /srv/models
diff --git a/deploy/oci/values.yaml b/deploy/oci/values.yaml
index 904c7a2d30..2ce8c749f9 100644
--- a/deploy/oci/values.yaml
+++ b/deploy/oci/values.yaml
@@ -27,7 +27,7 @@
 replicaCount: 1
 
 image:
-  imageName: nvcr.io/nvidia/tritonserver:26.01-py3
+  imageName: nvcr.io/nvidia/tritonserver:26.02-py3
   pullPolicy: IfNotPresent
   modelRepositoryPath: s3://https://<OCI_NAMESPACE>.compat.objectstorage.<OCI_REGION>.oraclecloud.com:443/triton-inference-server-repository
   numGpus: 1
diff --git a/docs/introduction/release_notes.md b/docs/introduction/release_notes.md
index 6aa87882eb..c0410b3a75 100644
--- a/docs/introduction/release_notes.md
+++ b/docs/introduction/release_notes.md
@@ -25,9 +25,9 @@
 # (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
 # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 -->
-# [Triton Inference Server Release 26.01](https://docs.nvidia.com/deeplearning/triton-inference-server/release-notes/rel-26-01.html#rel-26-01)
+# [Triton Inference Server Release 26.02](https://docs.nvidia.com/deeplearning/triton-inference-server/release-notes/rel-26-02.html#rel-26-02)
 
-The Triton Inference Server container image, release 26.01, is available
+The Triton Inference Server container image, release 26.02, is available
 on [NGC](https://ngc.nvidia.com/catalog/containers/nvidia:tritonserver) and
 is open source
 on [GitHub](https://github.com/triton-inference-server/server). Release notes can
diff --git a/python/openai/README.md b/python/openai/README.md
index 931dcb1b4e..945b050be0 100644
--- a/python/openai/README.md
+++ b/python/openai/README.md
@@ -46,7 +46,7 @@
 docker run -it --net=host --gpus all --rm \
   -v ${HOME}/.cache/huggingface:/root/.cache/huggingface \
   -e HF_TOKEN \
-  nvcr.io/nvidia/tritonserver:26.01-vllm-python-py3
+  nvcr.io/nvidia/tritonserver:26.02-vllm-python-py3
 ```
 
 2. Launch the OpenAI-compatible Triton Inference Server:
@@ -355,7 +355,7 @@ Currently, OpenAI-Compatible Frontend supports loading embedding models and embe
 docker run -it --net=host --gpus all --rm \
   -v ${HOME}/.cache/huggingface:/root/.cache/huggingface \
   -e HF_TOKEN \
-  nvcr.io/nvidia/tritonserver:26.01-vllm-python-py3
+  nvcr.io/nvidia/tritonserver:26.02-vllm-python-py3
 ```
 
 2. Launch the OpenAI-compatible Triton Inference Server:
@@ -451,7 +451,7 @@ docker run -it --net=host --gpus all --rm \
   -v ${HOME}/.cache/huggingface:/root/.cache/huggingface \
   -e HF_TOKEN \
   -e TRTLLM_ORCHESTRATOR=1 \
-  nvcr.io/nvidia/tritonserver:26.01-trtllm-python-py3
+  nvcr.io/nvidia/tritonserver:26.02-trtllm-python-py3
 ```
 
 2. Install dependencies inside the container:
diff --git a/qa/common/gen_jetson_trt_models b/qa/common/gen_jetson_trt_models
index af4f648e1c..3fff4e8ba3 100755
--- a/qa/common/gen_jetson_trt_models
+++ b/qa/common/gen_jetson_trt_models
@@ -34,7 +34,7 @@
 # Make all generated files accessible outside of container
 umask 0000
 # Set the version of the models
-TRITON_VERSION=${TRITON_VERSION:=26.01}
+TRITON_VERSION=${TRITON_VERSION:=26.02}
 # Set the CUDA device to use
 CUDA_DEVICE=${RUNNER_ID:=0}
 # Set TensorRT image
diff --git a/qa/common/gen_qa_model_repository b/qa/common/gen_qa_model_repository
index 429d15d736..70e4730843 100755
--- a/qa/common/gen_qa_model_repository
+++ b/qa/common/gen_qa_model_repository
@@ -60,7 +60,7 @@ log_message.status "Changing working directory to the script directory to: " "${
 cd ${TRITON_MDLS_BASE_SCRIPT_DIR}
 
 log_message.status "define: default values"
-TRITON_VERSION=${TRITON_VERSION:=26.01}
+TRITON_VERSION=${TRITON_VERSION:=26.02}
 ONNX_VERSION=1.20.1
 ONNX_OPSET=0
 OPENVINO_VERSION=2024.5.0