diff --git a/src/nncf/quantization/algorithms/weight_compression/algorithm.py b/src/nncf/quantization/algorithms/weight_compression/algorithm.py
index 1fb2f343f72..d0407a1eff4 100644
--- a/src/nncf/quantization/algorithms/weight_compression/algorithm.py
+++ b/src/nncf/quantization/algorithms/weight_compression/algorithm.py
@@ -769,15 +769,28 @@ def is_weight_compression_supported(
 
         return is_supported_dtype and not no_bit_reduction
 
-    def apply(
+    def get_weight_compression_parameters(
         self,
         model: TModel,
         graph: NNCFGraph,
         statistic_points: Optional[StatisticPointsContainer] = None,
         dataset: Optional[Dataset] = None,
-    ) -> TModel:
-        self.set_backend_entity(model)
+    ) -> tuple[list[WeightCompressionParameters], Optional[dict[str, WCTensorStatistic]]]:
+        """
+        Generates a list of weight compression parameters based on the Weight Compression algorithm
+        configuration. Determines the appropriate quantization parameters for each node eligible for
+        weight compression. Also, Generates a mapping of target node names to the collected statistics
+        based on the provided statistic_points. If statistic_points is None, collects required
+        compression statistics on the given dataset.
 
+        :param model: Backend-specific input model.
+        :param graph: NNCFGraph instance.
+        :param statistic_points: Optional pre-collected statistic points.
+        :param dataset: Optional dataset for statistics collection.
+        :return: A tuple consisting of a list of weight compression parameters, based on the Weight
+            Compression algorithm configuration, and a mapping of target node names to the
+            collected statistics.
+        """
         nodes_to_compress = self.get_nodes_to_compress(graph)
 
         all_weight_params: list[WeightCompressionParameters] = []
@@ -787,12 +800,13 @@ def apply(
         is_last_layer_skipped = False
         n = len(nodes_to_compress)
         ignored_names = self.get_ignored_node_names(graph)
+
         for i, node in enumerate(nodes_to_compress):
             is_target_node = should_consider_scope(node.node_name, ignored_names)
             for weight_name, weight_port_id in self._backend_entity.get_weight_names_and_port_ids(node, graph):
                 is_last_layer = i == n - 1
                 if weight_name in weight_names:
-                    # If the last layer has shared weights then skiped
+                    # If the last layer has shared weights then skip it
                     # to avoid processing the same weight more than once
                     is_last_layer_skipped = is_last_layer
                     continue
@@ -828,6 +842,7 @@ def apply(
                         )
                         if self.is_weight_compression_supported(weight_dtype, mode):
                             wc_config = WeightCompressionConfig(mode=mode)
+
                     weight_params = WeightCompressionParameters(
                         weight_name, node, weight_port_id, weight_dtype, weight_shape, reduction_axes, wc_config
                     )
@@ -884,6 +899,20 @@ def apply(
         # Filter all_weight_params and by excluding nodes that should remain in their original floating-point precision
         all_weight_params = list(filter(lambda w_params: w_params.compression_config is not None, all_weight_params))
 
+        return all_weight_params, statistics
+
+    def apply(
+        self,
+        model: TModel,
+        graph: NNCFGraph,
+        statistic_points: Optional[StatisticPointsContainer] = None,
+        dataset: Optional[Dataset] = None,
+    ) -> TModel:
+        self.set_backend_entity(model)
+
+        # Get processed weight compression parameters ready for compression
+        all_weight_params, statistics = self.get_weight_compression_parameters(model, graph, statistic_points, dataset)
+
         if self._awq:
             model = self.awq_algo.apply(model, graph, all_weight_params, statistics, self._backend_entity)
             # After applying AWQ we need to update statistics since AWQ alters the activations