diff --git a/.github/scripts/install-torch-tensorrt.sh b/.github/scripts/install-torch-tensorrt.sh
index c567a6f172..94de5f022a 100755
--- a/.github/scripts/install-torch-tensorrt.sh
+++ b/.github/scripts/install-torch-tensorrt.sh
@@ -18,7 +18,7 @@ pip install --pre -r ${PWD}/tests/py/requirements.txt
 # eg. timm will install the latest torchvision, however we want to use the torchvision from nightly
 # reinstall torch torchvisionto make sure we have the correct version
 pip uninstall -y torch torchvision
-pip install --force-reinstall --pre ${TORCHVISION} --index-url ${INDEX_URL} --no-deps
+pip install --force-reinstall --pre ${TORCHVISION} --index-url ${INDEX_URL}
 pip install --force-reinstall --pre ${TORCH} --index-url ${INDEX_URL}
 
 
diff --git a/MODULE.bazel b/MODULE.bazel
index 1ecaebba28..59196b85be 100644
--- a/MODULE.bazel
+++ b/MODULE.bazel
@@ -52,7 +52,7 @@ new_local_repository(
 new_local_repository(
     name = "cuda_win",
     build_file = "@//third_party/cuda:BUILD",
-    path = "C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v12.8/",
+    path = "C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v12.9/",
 )
 
 http_archive = use_repo_rule("@bazel_tools//tools/build_defs/repo:http.bzl", "http_archive")
@@ -66,7 +66,7 @@ http_archive(
     name = "libtorch",
     build_file = "@//third_party/libtorch:BUILD",
     strip_prefix = "libtorch",
-    urls = ["https://download.pytorch.org/libtorch/nightly/cu128/libtorch-shared-with-deps-latest.zip"],
+    urls = ["https://download.pytorch.org/libtorch/nightly/cu129/libtorch-shared-with-deps-latest.zip"],
 )
 
 # in aarch64 platform you can get libtorch via either local or wheel file
@@ -84,7 +84,7 @@ http_archive(
     name = "libtorch_win",
     build_file = "@//third_party/libtorch:BUILD",
     strip_prefix = "libtorch",
-    urls = ["https://download.pytorch.org/libtorch/nightly/cu128/libtorch-win-shared-with-deps-latest.zip"],
+    urls = ["https://download.pytorch.org/libtorch/nightly/cu129/libtorch-win-shared-with-deps-latest.zip"],
 )
 
 http_archive(
diff --git a/setup.py b/setup.py
index 59e2babfcd..fb96d85453 100644
--- a/setup.py
+++ b/setup.py
@@ -215,7 +215,6 @@ def build_libtorchtrt_cxx11_abi(
         print("Jetpack build")
 
     if IS_SBSA:
-        cmd.append("--platforms=//toolchains:sbsa")
         print("SBSA build")
 
     if CI_BUILD:
diff --git a/tests/modules/hub.py b/tests/modules/hub.py
index 962a8b0309..80dd37cd4a 100644
--- a/tests/modules/hub.py
+++ b/tests/modules/hub.py
@@ -1,16 +1,14 @@
 import importlib
+import importlib.util
 import json
 import os
 
 import custom_models as cm
 import torch
 
-if importlib.util.find_spec("torchvision"):
-    import timm
-    import torchvision.models as models
-
 torch.hub._validate_not_a_forked_repo = lambda a, b, c: True
 
+
 torch_version = torch.__version__
 
 # Detect case of no GPU before deserialization of models on GPU
@@ -22,7 +20,71 @@
 # Downloads all model files again if manifest file is not present
 MANIFEST_FILE = "model_manifest.json"
 
-to_test_models = {}
+to_test_models = {
+    "pooling": {"model": cm.Pool(), "path": "trace"},
+    "module_fallback": {"model": cm.ModuleFallbackMain(), "path": "script"},
+    "loop_fallback_eval": {"model": cm.LoopFallbackEval(), "path": "script"},
+    "loop_fallback_no_eval": {"model": cm.LoopFallbackNoEval(), "path": "script"},
+    "conditional": {"model": cm.FallbackIf(), "path": "script"},
+    "inplace_op_if": {"model": cm.FallbackInplaceOPIf(), "path": "script"},
+    "standard_tensor_input": {"model": cm.StandardTensorInput(), "path": "script"},
+    "tuple_input": {"model": cm.TupleInput(), "path": "script"},
+    "list_input": {"model": cm.ListInput(), "path": "script"},
+    "tuple_input_output": {"model": cm.TupleInputOutput(), "path": "script"},
+    "list_input_output": {"model": cm.ListInputOutput(), "path": "script"},
+    "list_input_tuple_output": {
+        "model": cm.ListInputTupleOutput(),
+        "path": "script",
+    },
+    # "bert_base_uncased": {"model": cm.BertModule(), "path": "trace"},
+}
+
+if importlib.util.find_spec("torchvision"):
+    import timm
+    import torchvision.models as models
+
+    torchvision_models = {
+        "alexnet": {"model": models.alexnet(pretrained=True), "path": "both"},
+        "vgg16": {"model": models.vgg16(pretrained=True), "path": "both"},
+        "squeezenet": {"model": models.squeezenet1_0(pretrained=True), "path": "both"},
+        "densenet": {"model": models.densenet161(pretrained=True), "path": "both"},
+        "inception_v3": {"model": models.inception_v3(pretrained=True), "path": "both"},
+        "shufflenet": {
+            "model": models.shufflenet_v2_x1_0(pretrained=True),
+            "path": "both",
+        },
+        "mobilenet_v2": {"model": models.mobilenet_v2(pretrained=True), "path": "both"},
+        "resnext50_32x4d": {
+            "model": models.resnext50_32x4d(pretrained=True),
+            "path": "both",
+        },
+        "wideresnet50_2": {
+            "model": models.wide_resnet50_2(pretrained=True),
+            "path": "both",
+        },
+        "mnasnet": {"model": models.mnasnet1_0(pretrained=True), "path": "both"},
+        "resnet18": {
+            "model": torch.hub.load(
+                "pytorch/vision:v0.9.0", "resnet18", pretrained=True
+            ),
+            "path": "both",
+        },
+        "resnet50": {
+            "model": torch.hub.load(
+                "pytorch/vision:v0.9.0", "resnet50", pretrained=True
+            ),
+            "path": "both",
+        },
+        "efficientnet_b0": {
+            "model": timm.create_model("efficientnet_b0", pretrained=True),
+            "path": "script",
+        },
+        "vit": {
+            "model": timm.create_model("vit_base_patch16_224", pretrained=True),
+            "path": "script",
+        },
+    }
+    to_test_models.update(torchvision_models)
 
 
 def get(n, m, manifest):
@@ -77,67 +139,6 @@ def download_models(version_matches, manifest):
 
 
 def main():
-    if not importlib.util.find_spec("torchvision"):
-        print(f"torchvision is not installed, skip models download")
-        return
-
-    to_test_models = {
-        "alexnet": {"model": models.alexnet(pretrained=True), "path": "both"},
-        "vgg16": {"model": models.vgg16(pretrained=True), "path": "both"},
-        "squeezenet": {"model": models.squeezenet1_0(pretrained=True), "path": "both"},
-        "densenet": {"model": models.densenet161(pretrained=True), "path": "both"},
-        "inception_v3": {"model": models.inception_v3(pretrained=True), "path": "both"},
-        "shufflenet": {
-            "model": models.shufflenet_v2_x1_0(pretrained=True),
-            "path": "both",
-        },
-        "mobilenet_v2": {"model": models.mobilenet_v2(pretrained=True), "path": "both"},
-        "resnext50_32x4d": {
-            "model": models.resnext50_32x4d(pretrained=True),
-            "path": "both",
-        },
-        "wideresnet50_2": {
-            "model": models.wide_resnet50_2(pretrained=True),
-            "path": "both",
-        },
-        "mnasnet": {"model": models.mnasnet1_0(pretrained=True), "path": "both"},
-        "resnet18": {
-            "model": torch.hub.load(
-                "pytorch/vision:v0.9.0", "resnet18", pretrained=True
-            ),
-            "path": "both",
-        },
-        "resnet50": {
-            "model": torch.hub.load(
-                "pytorch/vision:v0.9.0", "resnet50", pretrained=True
-            ),
-            "path": "both",
-        },
-        "efficientnet_b0": {
-            "model": timm.create_model("efficientnet_b0", pretrained=True),
-            "path": "script",
-        },
-        "vit": {
-            "model": timm.create_model("vit_base_patch16_224", pretrained=True),
-            "path": "script",
-        },
-        "pooling": {"model": cm.Pool(), "path": "trace"},
-        "module_fallback": {"model": cm.ModuleFallbackMain(), "path": "script"},
-        "loop_fallback_eval": {"model": cm.LoopFallbackEval(), "path": "script"},
-        "loop_fallback_no_eval": {"model": cm.LoopFallbackNoEval(), "path": "script"},
-        "conditional": {"model": cm.FallbackIf(), "path": "script"},
-        "inplace_op_if": {"model": cm.FallbackInplaceOPIf(), "path": "script"},
-        "standard_tensor_input": {"model": cm.StandardTensorInput(), "path": "script"},
-        "tuple_input": {"model": cm.TupleInput(), "path": "script"},
-        "list_input": {"model": cm.ListInput(), "path": "script"},
-        "tuple_input_output": {"model": cm.TupleInputOutput(), "path": "script"},
-        "list_input_output": {"model": cm.ListInputOutput(), "path": "script"},
-        "list_input_tuple_output": {
-            "model": cm.ListInputTupleOutput(),
-            "path": "script",
-        },
-        # "bert_base_uncased": {"model": cm.BertModule(), "path": "trace"},
-    }
 
     manifest = None
     version_matches = False
diff --git a/tests/py/dynamo/automatic_plugin/test_flashinfer_rmsnorm.py b/tests/py/dynamo/automatic_plugin/test_flashinfer_rmsnorm.py
index cf803c5ffa..389063ecd8 100644
--- a/tests/py/dynamo/automatic_plugin/test_flashinfer_rmsnorm.py
+++ b/tests/py/dynamo/automatic_plugin/test_flashinfer_rmsnorm.py
@@ -1,8 +1,7 @@
-import pytest
-
-flashinfer = pytest.importorskip("flashinfer")
+import importlib
 import unittest
 
+import pytest
 import torch
 import torch.nn as nn
 import torch_tensorrt
@@ -12,6 +11,9 @@
 
 from ..conversion.harness import DispatchTestCase
 
+if importlib.util.find_spec("flashinfer"):
+    import flashinfer
+
 
 @torch.library.custom_op("flashinfer::rmsnorm", mutates_args=())  # type: ignore[misc]
 def flashinfer_rmsnorm(
@@ -31,6 +33,7 @@ def _(input: torch.Tensor, weight: torch.Tensor, b: float = 1e-6) -> torch.Tenso
 
 
 @unittest.skip("Not Available")
+@unittest.skipIf(not importlib.util.find_spec("flashinfer"), "flashinfer not installed")
 class TestAutomaticPlugin(DispatchTestCase):
     @parameterized.expand(
         [