diff --git a/.circleci/config.yml b/.circleci/config.yml
index b0e4d0e7f9a..5773dae3957 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -146,7 +146,7 @@ commands:
         default: true
     steps:
       - pip_install:
-          args: --pre torch --extra-index-url https://download.pytorch.org/whl/nightly/cpu
+          args: --pre torch==1.13.0.dev20220618 --extra-index-url https://download.pytorch.org/whl/nightly/cpu
           descr: Install PyTorch from nightly releases
       - pip_install:
           args: --no-build-isolation <<# parameters.editable >> --editable <</ parameters.editable >> .
diff --git a/.circleci/config.yml.in b/.circleci/config.yml.in
index 514f1c563aa..432338e9d91 100644
--- a/.circleci/config.yml.in
+++ b/.circleci/config.yml.in
@@ -146,7 +146,7 @@ commands:
         default: true
     steps:
       - pip_install:
-          args: --pre torch --extra-index-url https://download.pytorch.org/whl/nightly/cpu
+          args: --pre torch==1.13.0.dev20220618 --extra-index-url https://download.pytorch.org/whl/nightly/cpu
           descr: Install PyTorch from nightly releases
       - pip_install:
           args: --no-build-isolation <<# parameters.editable >> --editable <</ parameters.editable >> .
diff --git a/.circleci/unittest/windows/scripts/install.sh b/.circleci/unittest/windows/scripts/install.sh
index cfdff3da6ba..dd414286588 100644
--- a/.circleci/unittest/windows/scripts/install.sh
+++ b/.circleci/unittest/windows/scripts/install.sh
@@ -34,7 +34,8 @@ else
 fi
 
 printf "Installing PyTorch with %s\n" "${cudatoolkit}"
-conda install -y -c "pytorch-${UPLOAD_CHANNEL}" -c nvidia "pytorch-${UPLOAD_CHANNEL}"::pytorch[build="*${version}*"] "${cudatoolkit}"
+# conda install -y -c "pytorch-${UPLOAD_CHANNEL}" -c nvidia "pytorch-${UPLOAD_CHANNEL}"::pytorch[build="*${version}*"] "${cudatoolkit}"
+pip install --pre torch==1.13.0.dev20220618+cpu  --extra-index-url https://download.pytorch.org/whl/nightly/cpu
 
 torch_cuda=$(python -c "import torch; print(torch.cuda.is_available())")
 echo torch.cuda.is_available is $torch_cuda
diff --git a/test/test_models.py b/test/test_models.py
index 0acef4dcef6..d864cb161c0 100644
--- a/test/test_models.py
+++ b/test/test_models.py
@@ -603,8 +603,8 @@ def test_classification_model(model_fn, dev):
         "input_shape": (1, 3, 224, 224),
     }
     model_name = model_fn.__name__
-    if dev == "cuda" and SKIP_BIG_MODEL and model_name in skipped_big_models:
-        pytest.skip("Skipped to reduce memory usage. Set env var SKIP_BIG_MODEL=0 to enable test for this model")
+    # if dev == "cuda" and SKIP_BIG_MODEL and model_name in skipped_big_models:
+    #     pytest.skip("Skipped to reduce memory usage. Set env var SKIP_BIG_MODEL=0 to enable test for this model")
     kwargs = {**defaults, **_model_params.get(model_name, {})}
     num_classes = kwargs.get("num_classes")
     input_shape = kwargs.pop("input_shape")
@@ -613,9 +613,15 @@ def test_classification_model(model_fn, dev):
     model.eval().to(device=dev)
     # RNG always on CPU, to ensure x in cuda tests is bitwise identical to x in cpu tests
     x = torch.rand(input_shape).to(device=dev)
-    out = model(x)
+    with torch.inference_mode():
+        out = model(x)
     _assert_expected(out.cpu(), model_name, prec=0.1)
     assert out.shape[-1] == num_classes
+
+    if SKIP_BIG_MODEL and model_name in skipped_big_models:
+        # Skip backprop test only
+        return
+
     _check_jit_scriptable(model, (x,), unwrapper=script_model_unwrapper.get(model_name, None), eager_out=out)
     _check_fx_compatible(model, x, eager_out=out)