diff --git a/.github/workflows/nightly_tests.yml b/.github/workflows/nightly_tests.yml
index 416d2af3fc2e..e242b4b57cb0 100644
--- a/.github/workflows/nightly_tests.yml
+++ b/.github/workflows/nightly_tests.yml
@@ -341,7 +341,7 @@ jobs:
             additional_deps: ["peft", "kernels"]
           - backend: "torchao"
             test_location: "torchao"
-            additional_deps: []
+            additional_deps: [mslk-cuda]
           - backend: "optimum_quanto"
             test_location: "quanto"
             additional_deps: []
diff --git a/tests/models/testing_utils/quantization.py b/tests/models/testing_utils/quantization.py
index 0f1fbde72485..ec74422741c3 100644
--- a/tests/models/testing_utils/quantization.py
+++ b/tests/models/testing_utils/quantization.py
@@ -177,6 +177,11 @@ def _test_quantization_inference(self, config_kwargs):
         model_quantized.to(torch_device)
 
         inputs = self.get_dummy_inputs()
+        model_dtype = next(model_quantized.parameters()).dtype
+        inputs = {
+            k: v.to(dtype=model_dtype) if torch.is_tensor(v) and torch.is_floating_point(v) else v
+            for k, v in inputs.items()
+        }
         output = model_quantized(**inputs, return_dict=False)[0]
 
         assert output is not None, "Model output is None"
@@ -930,6 +935,7 @@ def test_torchao_device_map(self):
         """Test that device_map='auto' works correctly with quantization."""
         self._test_quantization_device_map(TorchAoConfigMixin.TORCHAO_QUANT_TYPES["int8wo"])
 
+    @pytest.mark.xfail(reason="dequantize is not implemented in torchao")
     def test_torchao_dequantize(self):
         """Test that dequantize() works correctly."""
         self._test_dequantize(TorchAoConfigMixin.TORCHAO_QUANT_TYPES["int8wo"])