diff --git a/optimum/exporters/executorch/recipes/xnnpack.py b/optimum/exporters/executorch/recipes/xnnpack.py
index 0b811996..c37146f3 100644
--- a/optimum/exporters/executorch/recipes/xnnpack.py
+++ b/optimum/exporters/executorch/recipes/xnnpack.py
@@ -36,7 +36,7 @@
     Seq2SeqLMExportableModule,
 )
 from ..recipe_registry import register_recipe
-
+from torchao.utils import unwrap_tensor_subclass
 
 @register_recipe("xnnpack")
 def export_to_executorch_with_xnnpack(
@@ -104,6 +104,8 @@ def _lower_to_executorch(
             )
         return {pte_name: et_prog}
 
+    model = unwrap_tensor_subclass(model)
+    print("Unwrapped model: ", model)
     exported_progs = model.export()
 
     if (
diff --git a/tests/models/test_modeling_gemma3.py b/tests/models/test_modeling_gemma3.py
index ff507bb6..e6a08449 100644
--- a/tests/models/test_modeling_gemma3.py
+++ b/tests/models/test_modeling_gemma3.py
@@ -279,11 +279,12 @@ def test_gemma3_270m_text_generation_with_custom_sdpa_8da4w_8we(self):
 
         self.assertTrue(check_causal_lm_output_quality(model_id, generated_tokens))
 
-    @slow
-    @pytest.mark.run_slow
-    @pytest.mark.skipif(is_linux_ci, reason="OOM")
+    # @slow
+    # @pytest.mark.run_slow
+    # @pytest.mark.skipif(is_linux_ci, reason="OOM")
     def test_gemma3_image_vision_with_custom_sdpa_kv_cache_8da4w_8we(self):
-        model_id = "google/gemma-3-4b-it"
+        # model_id = "google/gemma-3-4b-it"
+        model_id = "metascroy/gemma-3-4b-it-INT8-INT4"
         tokenizer = AutoTokenizer.from_pretrained(model_id)
         processor = AutoProcessor.from_pretrained(model_id)
         image_url = "https://llava-vl.github.io/static/images/view.jpg"
@@ -307,11 +308,12 @@ def test_gemma3_image_vision_with_custom_sdpa_kv_cache_8da4w_8we(self):
             task="multimodal-text-to-text",
             use_custom_sdpa=True,
             use_custom_kv_cache=True,
-            qlinear="8da4w",
-            qlinear_group_size=32,
+            device_map="cpu",
+            # qlinear="8da4w",
+            # qlinear_group_size=32,
             # Can't quantize the encoder a the moment, hidden dim of 4304 doesn't fit ExecuTorch's
             # XNNPack 32-group size quantized kernels. See https://github.com/pytorch/executorch/issues/14221.
-            qembedding_config="8w",
+            # qembedding_config="8w",
         )
 
         # Generate