diff --git a/src/transformers/quantizers/quantizer_torchao.py b/src/transformers/quantizers/quantizer_torchao.py
index a76f73aeb562..fd117b08023b 100644
--- a/src/transformers/quantizers/quantizer_torchao.py
+++ b/src/transformers/quantizers/quantizer_torchao.py
@@ -184,6 +184,7 @@ def get_weight_conversions(self):
                     source_patterns=[
                         "_weight_qdata",
                         "_weight_scale_and_zero",
+                        "_weight_per_tensor_scale",
                         "_weight_scale",
                         "_weight_zero_point",
                         "_weight_act_pre_scale",
diff --git a/tests/quantization/torchao_integration/test_torchao.py b/tests/quantization/torchao_integration/test_torchao.py
index ebcc08816d95..b188b4f9a0c3 100644
--- a/tests/quantization/torchao_integration/test_torchao.py
+++ b/tests/quantization/torchao_integration/test_torchao.py
@@ -39,6 +39,7 @@
     from torchao.dtypes import (
         AffineQuantizedTensor,
     )
+    from torchao.prototype.mx_formats import NVFP4DynamicActivationNVFP4WeightConfig
     from torchao.quantization import (
         Float8DynamicActivationFloat8WeightConfig,
         Float8Tensor,
@@ -587,13 +588,14 @@ class TorchAoSerializationTest(unittest.TestCase):
 
     test_params = (
         [
-            (Int8WeightOnlyConfig(version=2), ALL_DEVICES_COMMON),
-            (Int8DynamicActivationInt8WeightConfig(version=2), ALL_DEVICES_COMMON),
-            (Float8DynamicActivationFloat8WeightConfig(), Expectations({("cuda", None): "What are we having for dinner?\n\nJess: (smiling) I", ("xpu", None): "What are we having for dinner?\n\nJess: (smiling) I"})),
-            (Float8WeightOnlyConfig(), Expectations({("cuda", None): COMMON_OUTPUT, ("xpu", None): COMMON_OUTPUT})),
-            (Int4WeightOnlyConfig(int4_packing_format="tile_packed_to_4d"), Expectations({("cuda", None): "What are we having for dinner?\nRed, white, and green beans,", ("xpu", None): COMMON_OUTPUT})),
-            (Int8DynamicActivationIntxWeightConfig(), Expectations({("cpu", None): COMMON_OUTPUT, ("cuda", 9): COMMON_OUTPUT, ("cuda", 8): "What are we having for dinner?\n\nJEN: (smiling) I", ("xpu", None): COMMON_OUTPUT})),
-            (IntxWeightOnlyConfig(), ALL_DEVICES_COMMON),
+            ("Int8WeightOnlyConfig", Int8WeightOnlyConfig(version=2), ALL_DEVICES_COMMON),
+            ("Int8DynamicActivationInt8WeightConfig", Int8DynamicActivationInt8WeightConfig(version=2), ALL_DEVICES_COMMON),
+            ("Float8DynamicActivationFloat8WeightConfig", Float8DynamicActivationFloat8WeightConfig(), Expectations({("cuda", None): COMMON_OUTPUT, ("xpu", None): "What are we having for dinner?\n\nJess: (smiling) I"})),
+            ("Float8WeightOnlyConfig", Float8WeightOnlyConfig(), Expectations({("cuda", None): COMMON_OUTPUT, ("xpu", None): COMMON_OUTPUT})),
+            ("Int4WeightOnlyConfig", Int4WeightOnlyConfig(int4_packing_format="tile_packed_to_4d"), Expectations({("cuda", None): "What are we having for dinner?\nRed, white, and green beans,", ("xpu", None): COMMON_OUTPUT})),
+            ("Int8DynamicActivationIntxWeightConfig", Int8DynamicActivationIntxWeightConfig(), Expectations({("cpu", None): COMMON_OUTPUT, ("cuda", 9): COMMON_OUTPUT, ("cuda", 8): "What are we having for dinner?\n\nJEN: (smiling) I", ("xpu", None): COMMON_OUTPUT})),
+            ("IntxWeightOnlyConfig", IntxWeightOnlyConfig(), ALL_DEVICES_COMMON),
+            ("NVFP4DynamicActivationNVFP4WeightConfig", NVFP4DynamicActivationNVFP4WeightConfig(), Expectations({("cuda", None): "What are we having for dinner?\n\n10. Avoid using \"I"})),
         ]
         if is_torchao_available()
         else []
@@ -609,8 +611,12 @@ def _check_serialization(self, device, config, expected_output):
         if isinstance(config, (Float8DynamicActivationFloat8WeightConfig, Float8WeightOnlyConfig)):
             if torch.cuda.is_available() and torch.cuda.get_device_capability() < (8, 9):
                 self.skipTest(f"{type(config).__name__} requires CUDA capability >= (8, 9)")
+        if isinstance(config, NVFP4DynamicActivationNVFP4WeightConfig):
+            if torch.cuda.is_available() and torch.cuda.get_device_capability() < (10, 0):
+                self.skipTest(f"{type(config).__name__} requires CUDA capability >= (10, 0) (SM100)")
         quant_config = TorchAoConfig(config)
-        dtype = torch.bfloat16 if isinstance(config, Int4WeightOnlyConfig) else "auto"
+        needs_bfloat16 = isinstance(config, Int4WeightOnlyConfig | NVFP4DynamicActivationNVFP4WeightConfig)
+        dtype = torch.bfloat16 if needs_bfloat16 else "auto"
         quantized_model = AutoModelForCausalLM.from_pretrained(
             self.model_name,
             dtype=dtype,
@@ -629,7 +635,7 @@ def _check_serialization(self, device, config, expected_output):
             self.assertEqual(tokenizer.decode(output[0], skip_special_tokens=True), expected_output)
 
     @parameterized.expand(test_params, skip_on_empty=True)
-    def test_serialization_cpu(self, config, expected_outputs):
+    def test_serialization_cpu(self, _name, config, expected_outputs):
         try:
             expected = expected_outputs.find_expectation(("cpu", None, None))
         except ValueError:
@@ -638,7 +644,7 @@ def test_serialization_cpu(self, config, expected_outputs):
 
     @parameterized.expand(test_params, skip_on_empty=True)
     @require_torch_accelerator
-    def test_serialization_accelerator(self, config, expected_outputs):
+    def test_serialization_accelerator(self, _name, config, expected_outputs):
         try:
             expected = expected_outputs.get_expectation()
         except ValueError: