diff --git a/nncf/experimental/common/quantization/algorithms/quantizer/openvino_quantizer.py b/nncf/experimental/quantization/quantizers/openvino_quantizer.py
similarity index 94%
rename from nncf/experimental/common/quantization/algorithms/quantizer/openvino_quantizer.py
rename to nncf/experimental/quantization/quantizers/openvino_quantizer.py
index 9f9d203e747..fc3f8584940 100644
--- a/nncf/experimental/common/quantization/algorithms/quantizer/openvino_quantizer.py
+++ b/nncf/experimental/quantization/quantizers/openvino_quantizer.py
@@ -17,7 +17,7 @@
 from torch.ao.quantization.observer import PerChannelMinMaxObserver
 from torch.ao.quantization.quantizer.quantizer import QuantizationAnnotation as InductorQAnotation
 from torch.ao.quantization.quantizer.quantizer import QuantizationSpec as InductorQuantizationSpec
-from torch.ao.quantization.quantizer.quantizer import Quantizer
+from torch.ao.quantization.quantizer.quantizer import Quantizer as TorchAOQuantizer
 
 from nncf.common.graph.graph import NNCFGraph
 from nncf.common.quantization.quantizer_propagation.solver import QuantizerPropagationRule
@@ -26,7 +26,7 @@
 from nncf.common.quantization.structs import QuantizationPreset
 from nncf.common.quantization.structs import QuantizationScheme
 from nncf.common.quantization.structs import QuantizerConfig as NNCFQuantizerConfig
-from nncf.experimental.quantization.algorithms.quantizer.base_quantizer import Quantizer as NNCFQuantizer
+from nncf.experimental.quantization.quantizers.quantizer import Quantizer
 from nncf.experimental.torch.fx.nncf_graph_builder import GraphConverter
 from nncf.experimental.torch.fx.node_utils import get_graph_node_by_name
 from nncf.experimental.torch.fx.transformations import fold_constant_except_qdq
@@ -42,7 +42,12 @@
 QUANT_ANNOTATION_KEY = "quantization_annotation"
 
 
-class OpenVINOQuantizer(Quantizer):
+class OpenVINOQuantizer(TorchAOQuantizer):
+    """
+    Implementation of the Torch AO quantizer which annotates models with quantization annotations
+    optimally for the inference via OpenVINO.
+    """
+
     def __init__(
         self,
         mode: Optional[QuantizationMode] = None,
@@ -171,9 +176,12 @@ def transform_for_annotation(self, model: torch.fx.GraphModule) -> torch.fx.Grap
         return model
 
 
-class OpenVINOQuantizerAdapter(NNCFQuantizer):
+class OpenVINOQuantizerAdapter(Quantizer):
     def __init__(self, quantizer: OpenVINOQuantizer):
         self._quantizer = quantizer
 
+    def transform_prior_quantization(self, model: torch.fx.GraphModule) -> torch.fx.GraphModule:
+        return self._quantizer.transform_for_annotation(model)
+
     def get_quantization_setup(self, model: torch.fx.GraphModule, nncf_graph: NNCFGraph) -> SingleConfigQuantizerSetup:
         return self._quantizer.get_quantization_setup(model, nncf_graph)
diff --git a/nncf/experimental/torch/fx/quantization/quantize_pt2e.py b/nncf/experimental/torch/fx/quantization/quantize_pt2e.py
index fb8db0da52e..048bf730f90 100644
--- a/nncf/experimental/torch/fx/quantization/quantize_pt2e.py
+++ b/nncf/experimental/torch/fx/quantization/quantize_pt2e.py
@@ -26,8 +26,9 @@
 from nncf.common.factory import NNCFGraphFactory
 from nncf.common.logging import nncf_logger
 from nncf.data import Dataset
-from nncf.experimental.common.quantization.algorithms.quantizer.openvino_quantizer import OpenVINOQuantizerAdapter
 from nncf.experimental.quantization.algorithms.post_training.algorithm import ExperimentalPostTrainingQuantization
+from nncf.experimental.quantization.quantizers.openvino_quantizer import OpenVINOQuantizer
+from nncf.experimental.quantization.quantizers.openvino_quantizer import OpenVINOQuantizerAdapter
 from nncf.experimental.quantization.quantizers.torch_ao_adapter import TorchAOQuantizerAdapter
 from nncf.experimental.torch.fx.constant_folding import constant_fold
 from nncf.experimental.torch.fx.transformations import QUANTIZE_NODE_TARGETS
@@ -92,7 +93,11 @@ def quantize_pt2e(
         model = deepcopy(model)
 
     _fuse_conv_bn_(model)
-    quantizer = TorchAOQuantizerAdapter(quantizer)
+    if isinstance(quantizer, OpenVINOQuantizer):
+        quantizer = OpenVINOQuantizerAdapter(quantizer)
+    else:
+        quantizer = TorchAOQuantizerAdapter(quantizer)
+
     # Call transform_prior_quantization before the NNCFGraph creation
     transformed_model = quantizer.transform_prior_quantization(model)
 
diff --git a/tests/torch/fx/test_quantizer.py b/tests/torch/fx/test_quantizer.py
index 5209cdf854f..7ac25e9c523 100644
--- a/tests/torch/fx/test_quantizer.py
+++ b/tests/torch/fx/test_quantizer.py
@@ -27,7 +27,7 @@
 from torch.ao.quantization.quantizer.x86_inductor_quantizer import get_default_x86_inductor_quantization_config
 
 import nncf
-from nncf.experimental.common.quantization.algorithms.quantizer.openvino_quantizer import OpenVINOQuantizer
+from nncf.experimental.quantization.quantizers.openvino_quantizer import OpenVINOQuantizer
 from nncf.experimental.torch.fx.nncf_graph_builder import GraphConverter
 from nncf.experimental.torch.fx.quantization.quantize_pt2e import quantize_pt2e
 from tests.torch import test_models