diff --git a/Dockerfile.sdk b/Dockerfile.sdk
index e919b3f50d..1804041101 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:25.09-py3-min
+ARG BASE_IMAGE=nvcr.io/nvidia/tritonserver:25.10-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 7be70a9ecc..cdd14ee53e 100644
--- a/README.md
+++ b/README.md
@@ -29,8 +29,8 @@
 
 >[!WARNING]
 >You are currently on the `main` branch which tracks under-development progress
->towards the next release. The current release is version [2.61.0](https://github.com/triton-inference-server/server/releases/latest)
->and corresponds to the 25.09 container release on NVIDIA GPU Cloud (NGC).
+>towards the next release. The current release is version [2.62.0](https://github.com/triton-inference-server/server/releases/latest)
+>and corresponds to the 25.10 container release on NVIDIA GPU Cloud (NGC).
 
 # Triton Inference Server
 
@@ -90,16 +90,16 @@ Inference Server with the
 
 ```bash
 # Step 1: Create the example model repository
-git clone -b r25.09 https://github.com/triton-inference-server/server.git
+git clone -b r25.10 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:25.09-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:25.10-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:25.09-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:25.10-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':
diff --git a/build.py b/build.py
index 03c84a7876..0f42ab0589 100755
--- a/build.py
+++ b/build.py
@@ -73,12 +73,12 @@
 DEFAULT_TRITON_VERSION_MAP = {
     "release_version": "2.63.0dev",
     "triton_container_version": "25.11dev",
-    "upstream_container_version": "25.09",
+    "upstream_container_version": "25.10",
     "ort_version": "1.23.1",
     "ort_openvino_version": "2025.3.0",
     "standalone_openvino_version": "2025.3.0",
     "dcgm_version": "4.4.0-1",
-    "vllm_version": "0.10.1.1",
+    "vllm_version": "0.10.2",
     "rhel_py_version": "3.12.3",
 }
 
@@ -660,6 +660,10 @@ def pytorch_cmake_args(images):
         cargs.append(
             cmake_backend_enable("pytorch", "TRITON_ENABLE_NVTX", FLAGS.enable_nvtx)
         )
+        if target_platform() == "igpu":
+            cargs.append(
+                cmake_backend_enable("pytorch", "TRITON_PYTORCH_NVSHMEM", False)
+            )
     return cargs
 
 
@@ -1278,11 +1282,6 @@ def create_dockerfile_linux(
     pip3 install --no-cache-dir  grpcio-tools==1.64.0 && \\
     pip3 uninstall -y setuptools
 ENV LD_LIBRARY_PATH=/usr/local/tensorrt/lib/:/opt/tritonserver/backends/tensorrtllm:$LD_LIBRARY_PATH
-
-# There are some ucc issues when spawning mpi processes with ompi v4.1.7a1.
-# Downgrade to ompi v4.1.5rc2 to avoid the issue.
-RUN rm -fr /opt/hpcx/ompi
-COPY --from=nvcr.io/nvidia/tritonserver:24.02-py3-min /opt/hpcx/ompi /opt/hpcx/ompi
 """
     with open(os.path.join(ddir, dockerfile_name), "w") as dfile:
         dfile.write(df)
@@ -1461,14 +1460,6 @@ def dockerfile_prepare_container_linux(argmap, backends, enable_gpu, target_mach
             virtualenv \\
       && rm -rf /var/lib/apt/lists/*
 """
-    if "tensorrtllm" in backends:
-        df += """
-# Updating the openssh-client to fix for the CVE-2024-6387. This can be removed when trtllm uses a later CUDA container(12.5 or later)
-RUN apt-get update \\
-    && apt-get install -y --no-install-recommends \\
-        openssh-client \\
-    && rm -rf /var/lib/apt/lists/*
-    """
 
     if "vllm" in backends:
         df += f"""
@@ -1514,6 +1505,25 @@ def dockerfile_prepare_container_linux(argmap, backends, enable_gpu, target_mach
 ENV PYTHONPATH=/opt/tritonserver/backends/dali/wheel/dali:$PYTHONPATH
 """
 
+    if (
+        target_platform() not in ["igpu", "windows", "rhel"]
+        and "tensorrtllm" not in backends
+    ):
+        repo_arch = "sbsa" if target_machine == "aarch64" else "x86_64"
+        df += f"""
+RUN curl -o /tmp/cuda-keyring.deb \\
+        https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2404/{repo_arch}/cuda-keyring_1.1-1_all.deb \\
+      && apt install /tmp/cuda-keyring.deb \\
+      && rm /tmp/cuda-keyring.deb \\
+      && apt update -qq \\
+      && apt install --yes --no-install-recommends libnvshmem3-cuda-13 \\
+      && rm -rf /var/lib/apt/lists/* \\
+      && dpkg -L libnvshmem3-cuda-13 | grep libnvshmem_host.so | sed -e 's/libnvshmem_host.*//g' | sort -u > /etc/ld.so.conf.d/libnvshmem3-cuda-13.conf \\
+      && ldconfig
+""".format(
+            repo_arch=repo_arch
+        )
+
     df += """
 WORKDIR /opt/tritonserver
 RUN rm -fr /opt/tritonserver/*
diff --git a/deploy/aws/values.yaml b/deploy/aws/values.yaml
index 27bec3dd10..7c782b1ed9 100644
--- a/deploy/aws/values.yaml
+++ b/deploy/aws/values.yaml
@@ -27,7 +27,7 @@
 replicaCount: 1
 
 image:
-  imageName: nvcr.io/nvidia/tritonserver:25.09-py3
+  imageName: nvcr.io/nvidia/tritonserver:25.10-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 8b0eed9bae..2cbc1cbb34 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: 2.61.0"
+appVersion: 2.62.0"
 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 aa2e45e192..830407c9fe 100644
--- a/deploy/fleetcommand/values.yaml
+++ b/deploy/fleetcommand/values.yaml
@@ -27,7 +27,7 @@
 replicaCount: 1
 
 image:
-  imageName: nvcr.io/nvidia/tritonserver:25.09-py3
+  imageName: nvcr.io/nvidia/tritonserver:25.10-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/r25.09/docs/user_guide/model_management.md
+    # See https://github.com/triton-inference-server/server/blob/r25.10/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/r25.09/README.md
+    # see https://github.com/triton-inference-server/server/blob/r25.10/README.md
     #  for more details
 
 service:
diff --git a/deploy/gcp/values.yaml b/deploy/gcp/values.yaml
index c4c2ff9951..b66268bce2 100644
--- a/deploy/gcp/values.yaml
+++ b/deploy/gcp/values.yaml
@@ -27,7 +27,7 @@
 replicaCount: 1
 
 image:
-  imageName: nvcr.io/nvidia/tritonserver:25.09-py3
+  imageName: nvcr.io/nvidia/tritonserver:25.10-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 4b21697c29..d65a50862f 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:25.09-py3-sdk
+  - image: nvcr.io/nvidia/tritonserver:25.10-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 b960b8ff94..9e2c400146 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=2.61
-export MINOR_VERSION=2.61.0
-export NGC_VERSION=25.09-py3
+export MAJOR_VERSION=2.62
+export MINOR_VERSION=2.62.0
+export NGC_VERSION=25.10-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 e86c2b6a21..87822f7953 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: "2.60"
 description: Triton Inference Server
 name: triton-inference-server
-version: 2.61.0
+version: 2.62.0
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 9f87933228..3d8a3d14a2 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/25.09
-publishedVersion: '2.61.0'
+modelRepositoryPath: gs://triton_sample_models/25.10
+publishedVersion: '2.62.0'
 gcpMarketplace: true
 
 image:
   registry: gcr.io
   repository: nvidia-ngc-public/tritonserver
-  tag: 25.09-py3
+  tag: 25.10-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 107c3ce161..75af2cf0b7 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: '2.61.0'
+  publishedVersion: '2.62.0'
   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 4bc5fd29e6..1e61d21839 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: '2.61.0'
+  publishedVersion: '2.62.0'
   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/25.09
+    default: gs://triton_sample_models/25.10
   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 f2118a1bc3..eb84e23fda 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:25.09-py3
+    -v ~:/scripts nvcr.io/nvidia/tensorrt:25.10-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/25.09/bert/1/model.plan
+gsutil cp bert_large_int8_bs1_s128.engine gs://triton_sample_models/25.10/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/25.09/` 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/25.10/` should be updated accordingly with the correct version.
diff --git a/deploy/k8s-onprem/values.yaml b/deploy/k8s-onprem/values.yaml
index 66f17e7c03..322ee46de2 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:25.09-py3
+  imageName: nvcr.io/nvidia/tritonserver:25.10-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 be4929a0f1..ba42839e92 100644
--- a/deploy/oci/values.yaml
+++ b/deploy/oci/values.yaml
@@ -27,7 +27,7 @@
 replicaCount: 1
 
 image:
-  imageName: nvcr.io/nvidia/tritonserver:25.09-py3
+  imageName: nvcr.io/nvidia/tritonserver:25.10-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/compatibility.md b/docs/introduction/compatibility.md
index 1e1dbb5e3c..c90899361b 100644
--- a/docs/introduction/compatibility.md
+++ b/docs/introduction/compatibility.md
@@ -38,6 +38,7 @@
 
 | Triton release version	 | NGC Tag	 | Python version	 | Torch version | TensorRT version | TensorRT-LLM version | CUDA version | CUDA Driver version | Size |
 | --- | ---  | --- | --- | --- | --- | --- | --- | --- |
+| 25.10 | nvcr.io/nvidia/tritonserver:25.10-trtllm-python-py3 | Python 3.12.3  | 2.8.0a0+5228986c39.nv25.6 | 10.11.0.33 | 1.0.0 | 12.9.1.010 | 575.57.08 | 16.21 GB |
 | 25.09 | nvcr.io/nvidia/tritonserver:25.09-trtllm-python-py3 | Python 3.12.3  | 2.8.0a0+5228986c39.nv25.6 | 10.11.0.33 | 1.0.0 | 12.9.1.010 | 575.57.08 | 16.25 GB |
 | 25.08 | nvcr.io/nvidia/tritonserver:25.08-trtllm-python-py3 | Python 3.12.3  | 2.8.0a0+5228986c39.nv25.5 | 10.11.0.33 | 0.21.0 | 12.9.0.043 | 575.51.03 | 20.49 GB |
 | 25.07 | nvcr.io/nvidia/tritonserver:25.07-trtllm-python-py3 | Python 3.12.3  | 2.7.0a0+79aa17489c.nv25.4 | 10.10.0.31 | 0.20.0 | 12.9.0.036 | 575.51.03 | 18.3G |
@@ -61,6 +62,7 @@
 
 | Triton release version	 | NGC Tag	 | Python version	 | vLLM version | CUDA version | CUDA Driver version | Size |
 | --- | --- | --- | --- | --- | --- | --- |
+| 25.10 | nvcr.io/nvidia/tritonserver:25.10-vllm-python-py3 | Python 3.12.3  | 0.10.2+9dd9ca32.nv25.10.cu130 | 13.0.2.006 | 580.95.05 | 8.34G |
 | 25.09 | nvcr.io/nvidia/tritonserver:25.09-vllm-python-py3 | Python 3.12.3  | 0.10.1.1+381074ae.nv25.9.cu130 | 13.0.1.012 | 580.82.07 | 7.78G |
 | 25.08 | nvcr.io/nvidia/tritonserver:25.08-vllm-python-py3 | Python 3.12.3  | 0.9.2+4ef1e343.nv25.8.post1.cu130 | 13.0.1.012 | 580.82.07 | 8.1G |
 | 25.07 | nvcr.io/nvidia/tritonserver:25.07-vllm-python-py3 | Python 3.12.3  | 0.9.0rc1+1958ee56.nv25.6.cu129 | 12.9.0.043 | 575.51.03 | 10G |
@@ -84,6 +86,7 @@
 
 | Triton release version	 | ONNX Runtime	 |
 | --- | --- |
+| 25.10 | 1.23.1 |
 | 25.09 | 1.23.0 |
 | 25.08 | 1.23.0+1d1712fdaf |
 | 25.07 | 1.22.0 |
diff --git a/docs/introduction/release_notes.md b/docs/introduction/release_notes.md
index 31fa2784c9..156ac4a7af 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 25.09](https://docs.nvidia.com/deeplearning/triton-inference-server/release-notes/rel-25-09.html#rel-25-09)
+# [Triton Inference Server Release 25.10](https://docs.nvidia.com/deeplearning/triton-inference-server/release-notes/rel-25-10.html#rel-25-10)
 
-The Triton Inference Server container image, release 25.09, is available
+The Triton Inference Server container image, release 25.10, 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 5dda869bab..572373390f 100644
--- a/python/openai/README.md
+++ b/python/openai/README.md
@@ -51,7 +51,7 @@
 docker run -it --net=host --gpus all --rm \
   -v ${HOME}/.cache/huggingface:/root/.cache/huggingface \
   -e HF_TOKEN \
-  nvcr.io/nvidia/tritonserver:25.09-vllm-python-py3
+  nvcr.io/nvidia/tritonserver:25.10-vllm-python-py3
 ```
 
 2. Launch the OpenAI-compatible Triton Inference Server:
diff --git a/qa/L0_shared_memory/shared_memory_test.py b/qa/L0_shared_memory/shared_memory_test.py
index 6f8ba94337..5cb23d9869 100755
--- a/qa/L0_shared_memory/shared_memory_test.py
+++ b/qa/L0_shared_memory/shared_memory_test.py
@@ -301,7 +301,13 @@ def test_too_big_shm(self):
 
     def test_large_shm_register_offset(self):
         # Test for out of bounds read vulnerability when registering system shared memory with large offset
-        for platform in ["python", "onnx", "libtorch", "plan", "openvino"]:
+
+        platforms = (
+            ["python", "onnx", "libtorch", "plan", "openvino"]
+            if os.environ.get("BACKENDS") is None
+            else os.environ.get("BACKENDS").split()
+        )
+        for platform in platforms:
             model_name = f"{platform}_int32_int32_int32"
 
             # Test for large offset
diff --git a/qa/L0_shared_memory/test.sh b/qa/L0_shared_memory/test.sh
index 3f5758bf36..e33c9e3756 100755
--- a/qa/L0_shared_memory/test.sh
+++ b/qa/L0_shared_memory/test.sh
@@ -160,7 +160,7 @@ done
 # Test large system shared memory offset
 rm -rf models/*
 # prepare add_sub model of various backends
-BACKENDS="python onnx libtorch plan openvino"
+BACKENDS=${BACKENDS:-"python onnx libtorch plan openvino"}
 for backend in ${BACKENDS} ; do
     model="${backend}_int32_int32_int32"
     model_dir="models/${model}"
diff --git a/qa/common/gen_jetson_trt_models b/qa/common/gen_jetson_trt_models
index 9b652a6977..693d233f91 100755
--- a/qa/common/gen_jetson_trt_models
+++ b/qa/common/gen_jetson_trt_models
@@ -34,7 +34,7 @@
 # Make all generated files accessible outside of container
 umask 0000
 # Set the version of the models
-TRITON_VERSION=${TRITON_VERSION:=25.09}
+TRITON_VERSION=${TRITON_VERSION:=25.10}
 # Set the CUDA device to use
 CUDA_DEVICE=${RUNNER_ID:=0}
 # Set TensorRT image
diff --git a/qa/common/gen_qa_custom_ops b/qa/common/gen_qa_custom_ops
index 1d7f6db05a..a196fb3f8e 100755
--- a/qa/common/gen_qa_custom_ops
+++ b/qa/common/gen_qa_custom_ops
@@ -37,7 +37,7 @@
 ##
 ############################################################################
 
-TRITON_VERSION=${TRITON_VERSION:=25.09}
+TRITON_VERSION=${TRITON_VERSION:=25.10}
 NVIDIA_UPSTREAM_VERSION=${NVIDIA_UPSTREAM_VERSION:=$TRITON_VERSION}
 PYTORCH_IMAGE=${PYTORCH_IMAGE:=nvcr.io/nvidia/pytorch:$NVIDIA_UPSTREAM_VERSION-py3}
 UBUNTU_IMAGE=${UBUNTU_IMAGE:=ubuntu:24.04}
diff --git a/qa/common/gen_qa_image_models.py b/qa/common/gen_qa_image_models.py
index a32ba92f1b..970d08662c 100755
--- a/qa/common/gen_qa_image_models.py
+++ b/qa/common/gen_qa_image_models.py
@@ -1,6 +1,6 @@
 #!/usr/bin/env python3
 
-# Copyright 2024, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# Copyright 2024-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 #
 # Redistribution and use in source and binary forms, with or without
 # modification, are permitted provided that the following conditions
@@ -99,7 +99,8 @@ def export_vgg19(models_dir, model_name="model.onnx"):
     model.eval()
     dummy_input = torch.randn(1, 3, 224, 224)  # (batch, channels, height, width)
 
-    # Export the model to ONNX format
+    # Use legacy TorchScript-based ONNX export
+    # TODO: Update to use new torch.export-based ONNX exporter (default dynamo=True)
     torch.onnx.export(
         model,
         dummy_input,
@@ -107,6 +108,7 @@ def export_vgg19(models_dir, model_name="model.onnx"):
         input_names=["input"],
         output_names=["output"],
         dynamic_axes={"input": {0: "batch_size"}, "output": {0: "batch_size"}},
+        dynamo=False,
     )
 
     print(f"VGG19 model exported to: {model_path}")
@@ -129,7 +131,8 @@ def export_resnet152(models_dir, model_name="model.onnx"):
     model.eval()
     dummy_input = torch.randn(1, 3, 224, 224)  # (batch, channels, height, width)
 
-    # Export the model to ONNX format
+    # Use legacy TorchScript-based ONNX export
+    # TODO: Update to use new torch.export-based ONNX exporter (default dynamo=True)
     torch.onnx.export(
         model,
         dummy_input,
@@ -137,6 +140,7 @@ def export_resnet152(models_dir, model_name="model.onnx"):
         input_names=["input"],
         output_names=["output"],
         dynamic_axes={"input": {0: "batch_size"}, "output": {0: "batch_size"}},
+        dynamo=False,
     )
 
     print(f"ResNet-152 model exported to: {model_path}")
@@ -159,7 +163,8 @@ def export_resnet50(models_dir, model_name="model.onnx"):
     model.eval()
     dummy_input = torch.randn(1, 3, 224, 224)  # (batch, channels, height, width)
 
-    # Export the model to ONNX format
+    # Use legacy TorchScript-based ONNX export
+    # TODO: Update to use new torch.export-based ONNX exporter (default dynamo=True)
     torch.onnx.export(
         model,
         dummy_input,
@@ -167,6 +172,7 @@ def export_resnet50(models_dir, model_name="model.onnx"):
         input_names=["input"],
         output_names=["output"],
         dynamic_axes={"input": {0: "batch_size"}, "output": {0: "batch_size"}},
+        dynamo=False,
     )
 
     print(f"ResNet-50 model exported to: {model_path}")
diff --git a/qa/common/gen_qa_model_repository b/qa/common/gen_qa_model_repository
index 9ac88218e5..753f6a672d 100755
--- a/qa/common/gen_qa_model_repository
+++ b/qa/common/gen_qa_model_repository
@@ -48,7 +48,7 @@
 ##
 ############################################################################
 
-TRITON_VERSION=${TRITON_VERSION:=25.09}
+TRITON_VERSION=${TRITON_VERSION:=25.10}
 
 # ONNX. Use ONNX_OPSET 0 to use the default for ONNX version
 ONNX_VERSION=1.16.1