diff --git a/nncf/experimental/torch/fx/nncf_graph_builder.py b/nncf/experimental/torch/fx/nncf_graph_builder.py
index 36dc3b46e6e..3d162f27400 100644
--- a/nncf/experimental/torch/fx/nncf_graph_builder.py
+++ b/nncf/experimental/torch/fx/nncf_graph_builder.py
@@ -197,6 +197,9 @@ def get_edge_params(
                 tensor = source_node.meta["val"]
             if isinstance(tensor, torch.Tensor):
                 tensor_shape = tuple(tensor.shape)
+                tensor_shape = tuple(str(i) if isinstance(i, torch.SymInt) else i for i in tensor_shape)
+            if isinstance(tensor, torch.SymInt):
+                tensor_shape = (str(tensor),)
 
         if tensor_shape is None:
             # TODO(dlyakhov): Refactor algorithms to always have knowns edges shapes.
diff --git a/tests/torch/data/reference_graphs/fx/dynamic_shapes/post_quantization_compressed/synthetic_transformer.dot b/tests/torch/data/reference_graphs/fx/dynamic_shapes/post_quantization_compressed/synthetic_transformer.dot
new file mode 100644
index 00000000000..f9d6b83d2ed
--- /dev/null
+++ b/tests/torch/data/reference_graphs/fx/dynamic_shapes/post_quantization_compressed/synthetic_transformer.dot
@@ -0,0 +1,49 @@
+strict digraph  {
+"0 wte_weight" [id=0, type=get_attr];
+"1 linear_bias" [id=1, type=get_attr];
+"2 lm_head_bias" [id=2, type=get_attr];
+"3 input_ids" [id=3, type=input];
+"4 embedding" [id=4, type=embedding];
+"5 embedding_0_0_nncf_smooth_quant_0" [id=5, type=call_module];
+"6 quantize_per_tensor_default" [id=6, type=quantize_per_tensor];
+"7 dequantize_per_tensor_default" [id=7, type=dequantize_per_tensor];
+"8 scale_updated_constant0" [id=8, type=get_attr];
+"9 compressed_weight_updated_constant0" [id=9, type=get_attr];
+"10 mul_tensor" [id=10, type=mul];
+"11 zero_point_updated_constant0" [id=11, type=get_attr];
+"12 sub_tensor" [id=12, type=sub];
+"13 linear" [id=13, type=linear];
+"14 linear_0_0_nncf_smooth_quant_0" [id=14, type=call_module];
+"15 quantize_per_tensor_default_1" [id=15, type=quantize_per_tensor];
+"16 dequantize_per_tensor_default_1" [id=16, type=dequantize_per_tensor];
+"17 scale_updated_constant1" [id=17, type=get_attr];
+"18 compressed_weight_updated_constant1" [id=18, type=get_attr];
+"19 mul_tensor_1" [id=19, type=mul];
+"20 zero_point_updated_constant1" [id=20, type=get_attr];
+"21 sub_tensor_1" [id=21, type=sub];
+"22 linear_1" [id=22, type=linear];
+"23 output" [id=23, type=output];
+"0 wte_weight" -> "4 embedding"  [label="(10, 5)", style=solid];
+"1 linear_bias" -> "13 linear"  [label="(5,)", style=solid];
+"2 lm_head_bias" -> "22 linear_1"  [label="(10,)", style=solid];
+"3 input_ids" -> "4 embedding"  [label="('s0',)", style=solid];
+"4 embedding" -> "5 embedding_0_0_nncf_smooth_quant_0"  [label="('s0', 5)", style=solid];
+"5 embedding_0_0_nncf_smooth_quant_0" -> "6 quantize_per_tensor_default"  [label="('s0', 5)", style=solid];
+"6 quantize_per_tensor_default" -> "7 dequantize_per_tensor_default"  [label="('s0', 5)", style=solid];
+"7 dequantize_per_tensor_default" -> "13 linear"  [label="('s0', 5)", style=solid];
+"8 scale_updated_constant0" -> "10 mul_tensor"  [label="(5, 1)", style=solid];
+"9 compressed_weight_updated_constant0" -> "10 mul_tensor"  [label="(5, 5)", style=solid];
+"10 mul_tensor" -> "12 sub_tensor"  [label="(5, 5)", style=solid];
+"11 zero_point_updated_constant0" -> "12 sub_tensor"  [label="(5, 1)", style=solid];
+"12 sub_tensor" -> "13 linear"  [label="(5, 5)", style=solid];
+"13 linear" -> "14 linear_0_0_nncf_smooth_quant_0"  [label="('s0', 5)", style=solid];
+"14 linear_0_0_nncf_smooth_quant_0" -> "15 quantize_per_tensor_default_1"  [label="('s0', 5)", style=solid];
+"15 quantize_per_tensor_default_1" -> "16 dequantize_per_tensor_default_1"  [label="('s0', 5)", style=solid];
+"16 dequantize_per_tensor_default_1" -> "22 linear_1"  [label="('s0', 5)", style=solid];
+"17 scale_updated_constant1" -> "19 mul_tensor_1"  [label="(10, 1)", style=solid];
+"18 compressed_weight_updated_constant1" -> "19 mul_tensor_1"  [label="(10, 5)", style=solid];
+"19 mul_tensor_1" -> "21 sub_tensor_1"  [label="(10, 5)", style=solid];
+"20 zero_point_updated_constant1" -> "21 sub_tensor_1"  [label="(10, 1)", style=solid];
+"21 sub_tensor_1" -> "22 linear_1"  [label="(10, 5)", style=solid];
+"22 linear_1" -> "23 output"  [label="('s0', 10)", style=solid];
+}
diff --git a/tests/torch/data/reference_graphs/fx/dynamic_shapes/quantized/synthetic_transformer.dot b/tests/torch/data/reference_graphs/fx/dynamic_shapes/quantized/synthetic_transformer.dot
new file mode 100644
index 00000000000..0a0ad795c9a
--- /dev/null
+++ b/tests/torch/data/reference_graphs/fx/dynamic_shapes/quantized/synthetic_transformer.dot
@@ -0,0 +1,53 @@
+strict digraph  {
+"0 wte_weight" [id=0, type=get_attr];
+"1 linear_bias" [id=1, type=get_attr];
+"2 lm_head_bias" [id=2, type=get_attr];
+"3 input_ids" [id=3, type=input];
+"4 embedding" [id=4, type=embedding];
+"5 embedding_0_0_nncf_smooth_quant_0" [id=5, type=call_module];
+"6 quantize_per_tensor_default" [id=6, type=quantize_per_tensor];
+"7 dequantize_per_tensor_default" [id=7, type=dequantize_per_tensor];
+"8 linear_scale_0" [id=8, type=get_attr];
+"9 linear_zero_point_0" [id=9, type=get_attr];
+"10 compressed_weight_updated_constant0" [id=10, type=get_attr];
+"11 quantize_per_channel_default" [id=11, type=quantize_per_channel];
+"12 dequantize_per_channel_default" [id=12, type=dequantize_per_channel];
+"13 linear" [id=13, type=linear];
+"14 linear_0_0_nncf_smooth_quant_0" [id=14, type=call_module];
+"15 quantize_per_tensor_default_1" [id=15, type=quantize_per_tensor];
+"16 dequantize_per_tensor_default_1" [id=16, type=dequantize_per_tensor];
+"17 linear_1_scale_0" [id=17, type=get_attr];
+"18 linear_1_zero_point_0" [id=18, type=get_attr];
+"19 compressed_weight_updated_constant1" [id=19, type=get_attr];
+"20 quantize_per_channel_default_1" [id=20, type=quantize_per_channel];
+"21 dequantize_per_channel_default_1" [id=21, type=dequantize_per_channel];
+"22 linear_1" [id=22, type=linear];
+"23 output" [id=23, type=output];
+"0 wte_weight" -> "4 embedding"  [label="(10, 5)", style=solid];
+"1 linear_bias" -> "13 linear"  [label="(5,)", style=solid];
+"2 lm_head_bias" -> "22 linear_1"  [label="(10,)", style=solid];
+"3 input_ids" -> "4 embedding"  [label="('s0',)", style=solid];
+"4 embedding" -> "5 embedding_0_0_nncf_smooth_quant_0"  [label="('s0', 5)", style=solid];
+"5 embedding_0_0_nncf_smooth_quant_0" -> "6 quantize_per_tensor_default"  [label="('s0', 5)", style=solid];
+"6 quantize_per_tensor_default" -> "7 dequantize_per_tensor_default"  [label="('s0', 5)", style=solid];
+"7 dequantize_per_tensor_default" -> "13 linear"  [label="('s0', 5)", style=solid];
+"8 linear_scale_0" -> "11 quantize_per_channel_default"  [label="(5,)", style=solid];
+"8 linear_scale_0" -> "12 dequantize_per_channel_default"  [label="(5,)", style=solid];
+"9 linear_zero_point_0" -> "11 quantize_per_channel_default"  [label="(5,)", style=solid];
+"9 linear_zero_point_0" -> "12 dequantize_per_channel_default"  [label="(5,)", style=solid];
+"10 compressed_weight_updated_constant0" -> "11 quantize_per_channel_default"  [label="(5, 5)", style=solid];
+"11 quantize_per_channel_default" -> "12 dequantize_per_channel_default"  [label="(5, 5)", style=solid];
+"12 dequantize_per_channel_default" -> "13 linear"  [label="(5, 5)", style=solid];
+"13 linear" -> "14 linear_0_0_nncf_smooth_quant_0"  [label="('s0', 5)", style=solid];
+"14 linear_0_0_nncf_smooth_quant_0" -> "15 quantize_per_tensor_default_1"  [label="('s0', 5)", style=solid];
+"15 quantize_per_tensor_default_1" -> "16 dequantize_per_tensor_default_1"  [label="('s0', 5)", style=solid];
+"16 dequantize_per_tensor_default_1" -> "22 linear_1"  [label="('s0', 5)", style=solid];
+"17 linear_1_scale_0" -> "20 quantize_per_channel_default_1"  [label="(10,)", style=solid];
+"17 linear_1_scale_0" -> "21 dequantize_per_channel_default_1"  [label="(10,)", style=solid];
+"18 linear_1_zero_point_0" -> "20 quantize_per_channel_default_1"  [label="(10,)", style=solid];
+"18 linear_1_zero_point_0" -> "21 dequantize_per_channel_default_1"  [label="(10,)", style=solid];
+"19 compressed_weight_updated_constant1" -> "20 quantize_per_channel_default_1"  [label="(10, 5)", style=solid];
+"20 quantize_per_channel_default_1" -> "21 dequantize_per_channel_default_1"  [label="(10, 5)", style=solid];
+"21 dequantize_per_channel_default_1" -> "22 linear_1"  [label="(10, 5)", style=solid];
+"22 linear_1" -> "23 output"  [label="('s0', 10)", style=solid];
+}
diff --git a/tests/torch/fx/helpers.py b/tests/torch/fx/helpers.py
index b51f6da16f4..9e599b38bdb 100644
--- a/tests/torch/fx/helpers.py
+++ b/tests/torch/fx/helpers.py
@@ -124,7 +124,7 @@ def visualize_fx_model(model: torch.fx.GraphModule, output_svg_path: str):
 
 
 def get_torch_fx_model(
-    model: torch.nn.Module, ex_input: Union[torch.Tensor, Tuple[torch.Tensor, ...]]
+    model: torch.nn.Module, ex_input: Union[torch.Tensor, Tuple[torch.Tensor, ...]], dynamic_shapes=None
 ) -> torch.fx.GraphModule:
     """
     Converts given module to GraphModule.
@@ -151,7 +151,7 @@ def get_torch_fx_model(
     model.eval()
     with torch.no_grad():
         with disable_patching():
-            return torch.export.export_for_training(model, args=device_ex_input).module()
+            return torch.export.export_for_training(model, args=device_ex_input, dynamic_shapes=dynamic_shapes).module()
 
 
 def get_torch_fx_model_q_transformed(model: torch.nn.Module, ex_input: torch.Tensor) -> torch.fx.GraphModule:
diff --git a/tests/torch/fx/test_models.py b/tests/torch/fx/test_models.py
index 39538f8b7d9..4b967e2a436 100644
--- a/tests/torch/fx/test_models.py
+++ b/tests/torch/fx/test_models.py
@@ -25,6 +25,7 @@
 import torch.utils.data
 import torch.utils.data.distributed
 import torchvision.models as models
+from torch.export.dynamic_shapes import Dim
 
 import nncf
 from nncf.common.graph.graph import NNCFNodeName
@@ -46,8 +47,12 @@
 from tests.torch.test_models.synthetic import YOLO11N_SDPABlock
 
 FX_DIR_NAME = Path("fx")
-FX_QUANTIZED_DIR_NAME = Path("fx") / "quantized"
-FX_QUANTIZED_COMPRESSED_DIR_NAME = Path("fx") / "post_quantization_compressed"
+FX_QUANTIZED_DIR_NAME = FX_DIR_NAME / "quantized"
+FX_QUANTIZED_COMPRESSED_DIR_NAME = FX_DIR_NAME / "post_quantization_compressed"
+
+FX_DYNAMIC_DIR = FX_DIR_NAME / "dynamic_shapes"
+FX_DYNAMIC_QUANTIZED_DIR_NAME = FX_DYNAMIC_DIR / "quantized"
+FX_DYNAMIC_QUANTIZED_COMPRESSED_DIR_NAME = FX_DYNAMIC_DIR / "post_quantization_compressed"
 
 
 @dataclass
@@ -171,18 +176,29 @@ def test_model(test_case: ModelCase):
 )
 
 
+@pytest.mark.parametrize("enable_dynamic_shapes", [True, False])
 @pytest.mark.parametrize("compress_weights", [True, False])
 @pytest.mark.parametrize(
     ("model_case", "quantization_parameters", "compress_n_qdq"),
     TEST_MODELS_QUANIZED,
     ids=[m[0].model_id for m in TEST_MODELS_QUANIZED],
 )
-def test_quantized_model(model_case: ModelCase, quantization_parameters, compress_weights: bool, compress_n_qdq: int):
+def test_quantized_model(
+    model_case: ModelCase,
+    quantization_parameters,
+    compress_weights: bool,
+    compress_n_qdq: int,
+    enable_dynamic_shapes: bool,
+):
     model = model_case.model_builder()
     dtype = torch.int32 if model_case.model_id == "synthetic_transformer" else torch.float32
     example_input = torch.ones(model_case.input_shape, dtype=dtype)
+    dynamic_shapes = None
+    enable_dynamic_shapes = model_case.model_id == "synthetic_transformer" and enable_dynamic_shapes
+    if enable_dynamic_shapes:
+        dynamic_shapes = [(Dim.AUTO,)]
 
-    fx_model = get_torch_fx_model(model, example_input)
+    fx_model = get_torch_fx_model(model, example_input, dynamic_shapes=dynamic_shapes)
 
     def transform_fn(data_item):
         return data_item.to("cpu")
@@ -198,7 +214,11 @@ def transform_fn(data_item):
     # Uncomment to visualize torch fx graph
     # from tests.torch.fx.helpers import visualize_fx_model
     # visualize_fx_model(quantized_model, f"{model_case.model_id}_int8.svg")
-    save_dir = FX_QUANTIZED_COMPRESSED_DIR_NAME if compress_weights else FX_QUANTIZED_DIR_NAME
+    if dynamic_shapes:
+        save_dir = FX_DYNAMIC_QUANTIZED_COMPRESSED_DIR_NAME if compress_weights else FX_DYNAMIC_QUANTIZED_DIR_NAME
+    else:
+        save_dir = FX_QUANTIZED_COMPRESSED_DIR_NAME if compress_weights else FX_QUANTIZED_DIR_NAME
+
     nncf_graph = GraphConverter.create_nncf_graph(quantized_model)
     check_graph(nncf_graph, get_dot_filename(model_case.model_id), save_dir, extended=True)
     q_nodes, dq_nodes = count_q_dq(quantized_model)
@@ -208,6 +228,28 @@ def transform_fn(data_item):
     check_compressed_post_quantized(quantized_model)
 
 
+def test_dynamic_edge():
+    model = MultiBranchesConnectedModel()
+    ex_inputs = torch.ones((1, 3, 3, 3))
+    dynamic_shapes = [
+        (
+            Dim.AUTO,
+            Dim.AUTO,
+            Dim.AUTO,
+            Dim.AUTO,
+        )
+    ]
+    fx_model = get_torch_fx_model(model, ex_inputs, dynamic_shapes=dynamic_shapes)
+    nncf_graph = GraphConverter.create_nncf_graph(fx_model)
+
+    for edge in nncf_graph.get_all_edges():
+        edge_shape = edge.tensor_shape
+        assert isinstance(edge_shape, tuple)
+        for dim in edge_shape:
+            assert isinstance(dim, (int, str))
+            assert not isinstance(dim, torch.SymInt)
+
+
 def check_fq_values(quantized_model):
     for node in quantized_model.graph.nodes:
         if node.target not in DEQUANTIZE_NODE_TARGETS: