diff --git a/examples/onnx_ptq/README.md b/examples/onnx_ptq/README.md
index 47058d361..483dde359 100644
--- a/examples/onnx_ptq/README.md
+++ b/examples/onnx_ptq/README.md
@@ -26,6 +26,13 @@ Model Optimizer enables highly performant quantization formats including NVFP4,
 
 Please use the TensorRT docker image (e.g., `nvcr.io/nvidia/tensorrt:25.08-py3`) or visit our [installation docs](https://nvidia.github.io/TensorRT-Model-Optimizer/getting_started/2_installation.html) for more information.
 
+Set the following environment variables inside the TensorRT docker.
+
+```bash
+export CUDNN_LIB_DIR=/usr/lib/x86_64-linux-gnu/
+export LD_LIBRARY_PATH="${CUDNN_LIB_DIR}:${LD_LIBRARY_PATH}"
+```
+
 Also follow the installation steps below to upgrade to the latest version of Model Optimizer and install example-specific dependencies.
 
 ### Local Installation
diff --git a/modelopt/onnx/autocast/convert.py b/modelopt/onnx/autocast/convert.py
index 5083df34d..a4b5d67fc 100644
--- a/modelopt/onnx/autocast/convert.py
+++ b/modelopt/onnx/autocast/convert.py
@@ -179,6 +179,7 @@ def convert_to_f16(
     sanitizer.find_custom_nodes()
     sanitizer.convert_opset()
     sanitizer.ensure_graph_name_exists()
+    sanitizer.convert_fp64_to_fp32()
     model = sanitizer.model
 
     # Setup internal mappings
diff --git a/modelopt/onnx/autocast/graphsanitizer.py b/modelopt/onnx/autocast/graphsanitizer.py
index 83e9df89a..49b78c749 100644
--- a/modelopt/onnx/autocast/graphsanitizer.py
+++ b/modelopt/onnx/autocast/graphsanitizer.py
@@ -65,6 +65,27 @@ def sanitize(self) -> None:
         self.replace_custom_domain_nodes()
         self.cleanup_model()
         self.set_ir_version(self.max_ir_version)
+        self.convert_fp64_to_fp32()
+
+    def convert_fp64_to_fp32(self) -> None:
+        """Convert FP64 initializers, I/O types, and specific nodes to FP32."""
+        modified = False
+
+        # Convert initializers
+        if self._convert_fp64_initializers():
+            modified = True
+
+        # Convert input/output types
+        if self._convert_fp64_io_types():
+            modified = True
+
+        # Convert specific node types: Cast, ConstantOfShape, Constant
+        if self._convert_fp64_nodes():
+            modified = True
+
+        if modified:
+            logger.info("Converted FP64 initializers, I/O types, and nodes to FP32")
+            self.model = onnx_utils.infer_shapes(self.model, strict_mode=True)
 
     def find_custom_nodes(self) -> None:
         """Find custom nodes in the model.
@@ -405,6 +426,85 @@ def _get_initializer_value(self, name: str, return_array: bool = False) -> np.nd
                 return value if return_array else value.item()
         return None
 
+    def _convert_fp64_initializers(self) -> bool:
+        """Convert FP64 initializers to FP32.
+
+        Returns:
+            bool: True if any initializers were modified, False otherwise.
+        """
+        modified = False
+
+        for initializer in self.model.graph.initializer:
+            if initializer.data_type == onnx.TensorProto.DOUBLE:
+                # Convert the data to FP32
+                fp64_data = numpy_helper.to_array(initializer)
+                fp32_data = fp64_data.astype(np.float32)
+
+                # Create new initializer with FP32 data
+                new_initializer = numpy_helper.from_array(fp32_data, name=initializer.name)
+
+                # Replace the old initializer
+                initializer.CopyFrom(new_initializer)
+                modified = True
+                logger.debug(f"Converted initializer {initializer.name} from FP64 to FP32")
+
+        return modified
+
+    def _convert_fp64_io_types(self) -> bool:
+        """Convert FP64 input/output types to FP32.
+
+        Returns:
+            bool: True if any I/O types were modified, False otherwise.
+        """
+        modified = False
+
+        def convert_tensor_list(tensors, tensor_type):
+            nonlocal modified
+            for tensor in tensors:
+                if tensor.type.tensor_type.elem_type == onnx.TensorProto.DOUBLE:
+                    tensor.type.tensor_type.elem_type = onnx.TensorProto.FLOAT
+                    modified = True
+                    logger.debug(f"Converted {tensor_type} {tensor.name} from FP64 to FP32")
+
+        convert_tensor_list(self.model.graph.input, "input")
+        convert_tensor_list(self.model.graph.output, "output")
+        convert_tensor_list(self.model.graph.value_info, "value_info")
+
+        return modified
+
+    def _convert_fp64_nodes(self) -> bool:
+        """Convert specific node types from FP64 to FP32.
+
+        Handles Cast, ConstantOfShape, and Constant nodes that use FP64.
+
+        Returns:
+            bool: True if any nodes were modified, False otherwise.
+        """
+        modified = False
+
+        for node in self.model.graph.node:
+            if node.op_type == "Cast":
+                # Check if casting to FP64, change to FP32
+                for attr in node.attribute:
+                    if attr.name == "to" and attr.i == onnx.TensorProto.DOUBLE:
+                        attr.i = onnx.TensorProto.FLOAT
+                        modified = True
+                        logger.debug(f"Converted Cast node {node.name} from FP64 to FP32")
+
+            elif node.op_type in ["ConstantOfShape", "Constant"]:
+                # Check if the value attribute uses FP64
+                for attr in node.attribute:
+                    if attr.name == "value" and attr.t.data_type == onnx.TensorProto.DOUBLE:
+                        # Convert the tensor value to FP32
+                        fp64_data = numpy_helper.to_array(attr.t)
+                        fp32_data = fp64_data.astype(np.float32)
+                        new_tensor = numpy_helper.from_array(fp32_data)
+                        attr.t.CopyFrom(new_tensor)
+                        modified = True
+                        logger.debug(f"Converted {node.op_type} node {node.name} from FP64 to FP32")
+
+        return modified
+
     def cleanup_model(self) -> None:
         """Use GraphSurgeon to cleanup unused nodes, tensors and initializers."""
         gs_graph = gs.import_onnx(self.model)
diff --git a/tests/unit/onnx/autocast/test_graphsanitizer.py b/tests/unit/onnx/autocast/test_graphsanitizer.py
index e1c447da4..cb487b56b 100644
--- a/tests/unit/onnx/autocast/test_graphsanitizer.py
+++ b/tests/unit/onnx/autocast/test_graphsanitizer.py
@@ -183,3 +183,227 @@ def test_invalid_layernorm_pattern():
 
     # Verify no LayerNorm transformation occurred
     assert not any(node.op_type == "LayerNormalization" for node in sanitizer.model.graph.node)
+
+
+def test_convert_fp64_initializers():
+    """Test conversion of FP64 initializers to FP32."""
+    # Create a model with FP64 initializers
+    x = helper.make_tensor_value_info("X", TensorProto.FLOAT, [2, 3])
+    y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [2, 3])
+
+    # Create FP64 initializers
+    fp64_weights = np.array([[1.5, 2.5, 3.5], [4.5, 5.5, 6.5]], dtype=np.float64)
+    fp64_bias = np.array([0.1, 0.2, 0.3], dtype=np.float64)
+    fp32_weights = np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float32)
+
+    initializers = [
+        numpy_helper.from_array(fp64_weights, name="fp64_weights"),
+        numpy_helper.from_array(fp64_bias, name="fp64_bias"),
+        numpy_helper.from_array(fp32_weights, name="fp32_weights"),
+    ]
+
+    # Verify the FP64 initializers have correct data type
+    assert initializers[0].data_type == TensorProto.DOUBLE
+    assert initializers[1].data_type == TensorProto.DOUBLE
+    assert initializers[2].data_type == TensorProto.FLOAT
+
+    add_node = helper.make_node("Add", ["X", "fp64_weights"], ["Y"])
+
+    graph = helper.make_graph(
+        nodes=[add_node], name="fp64_test", inputs=[x], outputs=[y], initializer=initializers
+    )
+
+    model = helper.make_model(graph)
+    sanitizer = GraphSanitizer(model)
+
+    # Test the conversion
+    result = sanitizer._convert_fp64_initializers()
+    assert result is True
+
+    # Verify all initializers are now FP32
+    for init in sanitizer.model.graph.initializer:
+        if init.name in ["fp64_weights", "fp64_bias"]:
+            assert init.data_type == TensorProto.FLOAT
+            # Verify data integrity
+            converted_data = numpy_helper.to_array(init)
+            assert converted_data.dtype == np.float32
+        elif init.name == "fp32_weights":
+            assert init.data_type == TensorProto.FLOAT
+
+
+def test_convert_fp64_io_types():
+    """Test conversion of FP64 input/output types to FP32."""
+    # Create inputs and outputs with FP64 types
+    x_fp64 = helper.make_tensor_value_info("X_fp64", TensorProto.DOUBLE, [2, 3])
+    y_fp64 = helper.make_tensor_value_info("Y_fp64", TensorProto.DOUBLE, [2, 3])
+    x_fp32 = helper.make_tensor_value_info("X_fp32", TensorProto.FLOAT, [2, 3])
+
+    # Create value_info with FP64 type
+    value_info_fp64 = helper.make_tensor_value_info("intermediate", TensorProto.DOUBLE, [2, 3])
+    value_info_fp32 = helper.make_tensor_value_info("intermediate2", TensorProto.FLOAT, [2, 3])
+
+    add_node = helper.make_node("Add", ["X_fp64", "X_fp32"], ["Y_fp64"])
+
+    graph = helper.make_graph(
+        nodes=[add_node],
+        name="fp64_io_test",
+        inputs=[x_fp64, x_fp32],
+        outputs=[y_fp64],
+        value_info=[value_info_fp64, value_info_fp32],
+    )
+
+    model = helper.make_model(graph)
+    sanitizer = GraphSanitizer(model)
+
+    # Test the conversion
+    result = sanitizer._convert_fp64_io_types()
+    assert result is True
+
+    # Verify inputs are converted
+    assert sanitizer.model.graph.input[0].type.tensor_type.elem_type == TensorProto.FLOAT
+    assert sanitizer.model.graph.input[1].type.tensor_type.elem_type == TensorProto.FLOAT
+
+    # Verify outputs are converted
+    assert sanitizer.model.graph.output[0].type.tensor_type.elem_type == TensorProto.FLOAT
+
+    # Verify value_info are converted
+    for vi in sanitizer.model.graph.value_info:
+        assert vi.type.tensor_type.elem_type == TensorProto.FLOAT
+
+
+def test_convert_fp64_nodes():
+    """Test conversion of specific node types from FP64 to FP32."""
+    x = helper.make_tensor_value_info("X", TensorProto.FLOAT, [2, 3])
+    y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [2, 3])
+
+    # Create FP64 constant tensor for ConstantOfShape and Constant nodes
+    fp64_value = numpy_helper.from_array(np.array([1.5], dtype=np.float64))
+    fp64_shape_value = numpy_helper.from_array(np.array([2.5], dtype=np.float64))
+
+    # Create nodes that use FP64
+    cast_node = helper.make_node("Cast", ["X"], ["cast_out"], to=TensorProto.DOUBLE)
+    constant_node = helper.make_node("Constant", [], ["const_out"], value=fp64_value)
+    constant_shape_node = helper.make_node(
+        "ConstantOfShape", ["shape"], ["shape_out"], value=fp64_shape_value
+    )
+    add_node = helper.make_node("Add", ["cast_out", "const_out"], ["Y"])
+
+    # Shape input for ConstantOfShape
+    shape_init = numpy_helper.from_array(np.array([2, 3], dtype=np.int64), name="shape")
+
+    graph = helper.make_graph(
+        nodes=[cast_node, constant_node, constant_shape_node, add_node],
+        name="fp64_nodes_test",
+        inputs=[x],
+        outputs=[y],
+        initializer=[shape_init],
+    )
+
+    model = helper.make_model(graph)
+    sanitizer = GraphSanitizer(model)
+
+    # Test the conversion
+    result = sanitizer._convert_fp64_nodes()
+    assert result is True
+
+    # Verify Cast node is converted
+    cast_nodes = [n for n in sanitizer.model.graph.node if n.op_type == "Cast"]
+    assert len(cast_nodes) == 1
+    cast_attr = next(attr for attr in cast_nodes[0].attribute if attr.name == "to")
+    assert cast_attr.i == TensorProto.FLOAT
+
+    # Verify Constant node is converted
+    constant_nodes = [n for n in sanitizer.model.graph.node if n.op_type == "Constant"]
+    assert len(constant_nodes) == 1
+    const_attr = next(attr for attr in constant_nodes[0].attribute if attr.name == "value")
+    assert const_attr.t.data_type == TensorProto.FLOAT
+
+    # Verify ConstantOfShape node is converted
+    const_shape_nodes = [n for n in sanitizer.model.graph.node if n.op_type == "ConstantOfShape"]
+    assert len(const_shape_nodes) == 1
+    shape_attr = next(attr for attr in const_shape_nodes[0].attribute if attr.name == "value")
+    assert shape_attr.t.data_type == TensorProto.FLOAT
+
+
+def test_convert_fp64_to_fp32_integration():
+    """Test the main convert_fp64_to_fp32 method with mixed FP64/FP32 content."""
+    # Create a model with mixed FP64 and FP32 content
+    x_fp64 = helper.make_tensor_value_info("X", TensorProto.DOUBLE, [2, 3])
+    y_fp32 = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [2, 3])
+
+    # FP64 initializer
+    fp64_weights = numpy_helper.from_array(
+        np.array([[1.5, 2.5], [3.5, 4.5]], dtype=np.float64), name="weights"
+    )
+
+    # FP64 constant value
+    fp64_const_value = numpy_helper.from_array(np.array([0.5], dtype=np.float64))
+
+    # Create nodes
+    cast_node = helper.make_node("Cast", ["X"], ["cast_out"], to=TensorProto.DOUBLE)
+    constant_node = helper.make_node("Constant", [], ["const_out"], value=fp64_const_value)
+    add_node = helper.make_node("Add", ["cast_out", "const_out"], ["Y"])
+
+    graph = helper.make_graph(
+        nodes=[cast_node, constant_node, add_node],
+        name="mixed_fp64_test",
+        inputs=[x_fp64],
+        outputs=[y_fp32],
+        initializer=[fp64_weights],
+    )
+
+    model = helper.make_model(graph)
+    sanitizer = GraphSanitizer(model)
+
+    # Test the main conversion method
+    sanitizer.convert_fp64_to_fp32()
+
+    # Verify all FP64 content has been converted
+    # Check input types
+    assert sanitizer.model.graph.input[0].type.tensor_type.elem_type == TensorProto.FLOAT
+
+    # Check initializers
+    for init in sanitizer.model.graph.initializer:
+        assert init.data_type == TensorProto.FLOAT
+
+    # Check Cast node
+    cast_nodes = [n for n in sanitizer.model.graph.node if n.op_type == "Cast"]
+    cast_attr = next(attr for attr in cast_nodes[0].attribute if attr.name == "to")
+    assert cast_attr.i == TensorProto.FLOAT
+
+    # Check Constant node
+    constant_nodes = [n for n in sanitizer.model.graph.node if n.op_type == "Constant"]
+    const_attr = next(attr for attr in constant_nodes[0].attribute if attr.name == "value")
+    assert const_attr.t.data_type == TensorProto.FLOAT
+
+
+def test_convert_fp64_no_changes_needed():
+    """Test that conversion methods return False when no FP64 content exists."""
+    # Create a model with only FP32 content
+    x = helper.make_tensor_value_info("X", TensorProto.FLOAT, [2, 3])
+    y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [2, 3])
+
+    fp32_weights = numpy_helper.from_array(
+        np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float32), name="weights"
+    )
+    fp32_const_value = numpy_helper.from_array(np.array([0.5], dtype=np.float32))
+
+    cast_node = helper.make_node("Cast", ["X"], ["cast_out"], to=TensorProto.FLOAT)
+    constant_node = helper.make_node("Constant", [], ["const_out"], value=fp32_const_value)
+    add_node = helper.make_node("Add", ["cast_out", "const_out"], ["Y"])
+
+    graph = helper.make_graph(
+        nodes=[cast_node, constant_node, add_node],
+        name="fp32_only_test",
+        inputs=[x],
+        outputs=[y],
+        initializer=[fp32_weights],
+    )
+
+    model = helper.make_model(graph)
+    sanitizer = GraphSanitizer(model)
+
+    # Test that no conversions are needed
+    assert sanitizer._convert_fp64_initializers() is False
+    assert sanitizer._convert_fp64_io_types() is False
+    assert sanitizer._convert_fp64_nodes() is False