diff --git a/examples/post_training_quantization/onnx/mobilenet_v2/main.py b/examples/post_training_quantization/onnx/mobilenet_v2/main.py
index 007b537ceb6..5f9850f8cbe 100755
--- a/examples/post_training_quantization/onnx/mobilenet_v2/main.py
+++ b/examples/post_training_quantization/onnx/mobilenet_v2/main.py
@@ -16,7 +16,7 @@
 
 import numpy as np
 import onnx
-import openvino.runtime as ov
+import openvino as ov
 import torch
 from fastdownload import FastDownload
 from fastdownload import download_url
diff --git a/examples/post_training_quantization/openvino/anomaly_stfpm_quantize_with_accuracy_control/main.py b/examples/post_training_quantization/openvino/anomaly_stfpm_quantize_with_accuracy_control/main.py
index daaa491e4ba..88393421801 100644
--- a/examples/post_training_quantization/openvino/anomaly_stfpm_quantize_with_accuracy_control/main.py
+++ b/examples/post_training_quantization/openvino/anomaly_stfpm_quantize_with_accuracy_control/main.py
@@ -19,7 +19,7 @@
 from typing import Any, Dict, Iterable, List, Optional, Tuple
 
 import numpy as np
-import openvino.runtime as ov
+import openvino as ov
 import torch
 from anomalib.data.mvtec import MVTec
 from anomalib.data.utils import download
@@ -165,12 +165,16 @@ def transform_fn(data_item):
 # Benchmark performance, calculate compression rate and validate accuracy
 
 fp32_ir_path = f"{ROOT}/stfpm_fp32.xml"
-ov.serialize(ov_model, fp32_ir_path)
+ov.save_model(ov_model, fp32_ir_path, compress_to_fp16=False)
 print(f"[1/7] Save FP32 model: {fp32_ir_path}")
 fp32_size = get_model_size(fp32_ir_path, verbose=True)
 
+# To avoid an accuracy drop when saving a model due to compression of unquantized
+# weights to FP16, compress_to_fp16=False should be used. This is necessary because
+# nncf.quantize_with_accuracy_control(...) keeps the most impactful operations within
+# the model in the original precision to achieve the specified model accuracy.
 int8_ir_path = f"{ROOT}/stfpm_int8.xml"
-ov.serialize(ov_quantized_model, int8_ir_path)
+ov.save_model(ov_quantized_model, int8_ir_path, compress_to_fp16=False)
 print(f"[2/7] Save INT8 model: {int8_ir_path}")
 int8_size = get_model_size(int8_ir_path, verbose=True)
 
diff --git a/examples/post_training_quantization/openvino/mobilenet_v2/main.py b/examples/post_training_quantization/openvino/mobilenet_v2/main.py
index 2cc6ab0329f..d6533bd61d8 100644
--- a/examples/post_training_quantization/openvino/mobilenet_v2/main.py
+++ b/examples/post_training_quantization/openvino/mobilenet_v2/main.py
@@ -16,7 +16,7 @@
 from typing import List, Optional
 
 import numpy as np
-import openvino.runtime as ov
+import openvino as ov
 import torch
 from fastdownload import FastDownload
 from sklearn.metrics import accuracy_score
@@ -137,12 +137,12 @@ def transform_fn(data_item):
 # Benchmark performance, calculate compression rate and validate accuracy
 
 fp32_ir_path = f"{ROOT}/mobilenet_v2_fp32.xml"
-ov.serialize(ov_model, fp32_ir_path)
+ov.save_model(ov_model, fp32_ir_path, compress_to_fp16=False)
 print(f"[1/7] Save FP32 model: {fp32_ir_path}")
 fp32_model_size = get_model_size(fp32_ir_path, verbose=True)
 
 int8_ir_path = f"{ROOT}/mobilenet_v2_int8.xml"
-ov.serialize(ov_quantized_model, int8_ir_path)
+ov.save_model(ov_quantized_model, int8_ir_path, compress_to_fp16=False)
 print(f"[2/7] Save INT8 model: {int8_ir_path}")
 int8_model_size = get_model_size(int8_ir_path, verbose=True)
 
diff --git a/examples/post_training_quantization/openvino/yolov8/main.py b/examples/post_training_quantization/openvino/yolov8/main.py
index f20730970f6..8280d9391b4 100644
--- a/examples/post_training_quantization/openvino/yolov8/main.py
+++ b/examples/post_training_quantization/openvino/yolov8/main.py
@@ -14,7 +14,7 @@
 from typing import Any, Dict, Tuple
 
 import numpy as np
-import openvino.runtime as ov
+import openvino as ov
 import torch
 from tqdm import tqdm
 from ultralytics.cfg import get_cfg
@@ -158,7 +158,7 @@ def main():
     # Quantize mode in OpenVINO representation
     quantized_model = quantize(ov_model, data_loader, validator)
     quantized_model_path = Path(f"{ROOT}/{MODEL_NAME}_openvino_model/{MODEL_NAME}_quantized.xml")
-    ov.serialize(quantized_model, str(quantized_model_path))
+    ov.save_model(quantized_model, str(quantized_model_path), compress_to_fp16=False)
 
     # Validate FP32 model
     fp_stats, total_images, total_objects = validate(ov_model, tqdm(data_loader), validator)
diff --git a/examples/post_training_quantization/openvino/yolov8_quantize_with_accuracy_control/main.py b/examples/post_training_quantization/openvino/yolov8_quantize_with_accuracy_control/main.py
index a6e17830289..f56bbed26bf 100644
--- a/examples/post_training_quantization/openvino/yolov8_quantize_with_accuracy_control/main.py
+++ b/examples/post_training_quantization/openvino/yolov8_quantize_with_accuracy_control/main.py
@@ -15,7 +15,7 @@
 from typing import Any, Dict, Tuple
 
 import numpy as np
-import openvino.runtime as ov
+import openvino as ov
 import torch
 from tqdm import tqdm
 from ultralytics.cfg import get_cfg
@@ -222,7 +222,7 @@ def main():
     quantized_model = quantize_ac(ov_model, data_loader, validator)
 
     quantized_model_path = Path(f"{ROOT}/{MODEL_NAME}_openvino_model/{MODEL_NAME}_quantized.xml")
-    ov.serialize(quantized_model, str(quantized_model_path))
+    ov.save_model(quantized_model, str(quantized_model_path), compress_to_fp16=False)
 
     # Validate FP32 model
     fp_stats, total_images, total_objects = validate(ov_model, tqdm(data_loader), validator)
diff --git a/examples/post_training_quantization/torch/mobilenet_v2/main.py b/examples/post_training_quantization/torch/mobilenet_v2/main.py
index 9297d5cf94f..35f9b35c06c 100644
--- a/examples/post_training_quantization/torch/mobilenet_v2/main.py
+++ b/examples/post_training_quantization/torch/mobilenet_v2/main.py
@@ -16,7 +16,7 @@
 from typing import List, Optional
 
 import numpy as np
-import openvino.runtime as ov
+import openvino as ov
 import torch
 from fastdownload import FastDownload
 from openvino.tools import mo
@@ -173,12 +173,12 @@ def transform_fn(data_item):
 ov_quantized_model = mo.convert_model(int8_onnx_path)
 
 fp32_ir_path = f"{ROOT}/mobilenet_v2_fp32.xml"
-ov.serialize(ov_model, fp32_ir_path)
+ov.save_model(ov_model, fp32_ir_path, compress_to_fp16=False)
 print(f"[1/7] Save FP32 model: {fp32_ir_path}")
 fp32_model_size = get_model_size(fp32_ir_path, verbose=True)
 
 int8_ir_path = f"{ROOT}/mobilenet_v2_int8.xml"
-ov.serialize(ov_quantized_model, int8_ir_path)
+ov.save_model(ov_quantized_model, int8_ir_path, compress_to_fp16=False)
 print(f"[2/7] Save INT8 model: {int8_ir_path}")
 int8_model_size = get_model_size(int8_ir_path, verbose=True)
 
diff --git a/examples/post_training_quantization/torch/ssd300_vgg16/main.py b/examples/post_training_quantization/torch/ssd300_vgg16/main.py
index c90ee304e2c..6c495ec03ce 100644
--- a/examples/post_training_quantization/torch/ssd300_vgg16/main.py
+++ b/examples/post_training_quantization/torch/ssd300_vgg16/main.py
@@ -18,7 +18,7 @@
 import nncf
 from nncf.torch import disable_tracing
 
-import openvino.runtime as ov
+import openvino as ov
 import torch
 import torchvision
 from fastdownload import FastDownload
@@ -163,12 +163,12 @@ def main():
     ov_quantized_model = mo.convert_model(int8_onnx_path)
 
     fp32_ir_path = f"{ROOT}/ssd300_vgg16_fp32.xml"
-    ov.serialize(ov_model, fp32_ir_path)
+    ov.save_model(ov_model, fp32_ir_path, compress_to_fp16=False)
     print(f"[1/7] Save FP32 model: {fp32_ir_path}")
     fp32_model_size = get_model_size(fp32_ir_path, verbose=True)
 
     int8_ir_path = f"{ROOT}/ssd300_vgg16_int8.xml"
-    ov.serialize(ov_quantized_model, int8_ir_path)
+    ov.save_model(ov_quantized_model, int8_ir_path, compress_to_fp16=False)
     print(f"[2/7] Save INT8 model: {int8_ir_path}")
     int8_model_size = get_model_size(int8_ir_path, verbose=True)