diff --git a/examples/__init__.py b/examples/__init__.py
index 22e663ef899..92618e42e43 100644
--- a/examples/__init__.py
+++ b/examples/__init__.py
@@ -8,3 +8,20 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+
+from re import compile
+from subprocess import check_output  # nosec B404: used only for executing benchmark_app
+
+throughput_pattern = compile(r"Throughput\: (.+?) FPS")
+
+
+def execute_benchmark_on_cpu(model_path, time, shape=None):
+    command = ["benchmark_app", "-m", model_path.as_posix(), "-d", "CPU", "-api", "async", "-t", str(time)]
+    if shape is not None:
+        command += ["-shape", str(shape)]
+
+    cmd_output = check_output(command, text=True)  # nosec B603: used only for executing benchmark_app
+    print(*cmd_output.splitlines()[-8:], sep="\n")
+
+    match = throughput_pattern.search(cmd_output)
+    return float(match.group(1))
diff --git a/examples/post_training_quantization/onnx/mobilenet_v2/main.py b/examples/post_training_quantization/onnx/mobilenet_v2/main.py
index ff6bfab4efd..ff167c54ced 100755
--- a/examples/post_training_quantization/onnx/mobilenet_v2/main.py
+++ b/examples/post_training_quantization/onnx/mobilenet_v2/main.py
@@ -9,8 +9,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import re
-import subprocess
 from pathlib import Path
 
 import numpy as np
@@ -25,6 +23,7 @@
 from torchvision import transforms
 
 import nncf
+from examples import execute_benchmark_on_cpu
 
 ROOT = Path(__file__).parent.resolve()
 MODEL_URL = "https://huggingface.co/alexsu52/mobilenet_v2_imagenette/resolve/main/mobilenet_v2_imagenette.onnx"
@@ -61,21 +60,6 @@ def validate(path_to_model: Path, validation_loader: torch.utils.data.DataLoader
     return accuracy_score(predictions, references)
 
 
-def run_benchmark(path_to_model: Path, shape: list[int]) -> float:
-    command = [
-        "benchmark_app",
-        "-m", path_to_model.as_posix(),
-        "-d", "CPU",
-        "-api", "async",
-        "-t", "15",
-        "-shape", str(shape),
-    ]  # fmt: skip
-    cmd_output = subprocess.check_output(command, text=True)  # nosec
-    print(*cmd_output.splitlines()[-8:], sep="\n")
-    match = re.search(r"Throughput\: (.+?) FPS", str(cmd_output))
-    return float(match.group(1))
-
-
 def get_model_size(path: Path, m_type: str = "Mb") -> float:
     model_size = path.stat().st_size
     for t in ["bytes", "Kb", "Mb"]:
@@ -152,9 +136,9 @@ def transform_fn(data_item):
 int8_model_size = get_model_size(int8_model_path)
 
 print("[3/7] Benchmark FP32 model:")
-fp32_fps = run_benchmark(fp32_model_path, shape=[1, 3, 224, 224])
+fp32_fps = execute_benchmark_on_cpu(fp32_model_path, time=15, shape=[1, 3, 224, 224])
 print("[4/7] Benchmark INT8 model:")
-int8_fps = run_benchmark(int8_model_path, shape=[1, 3, 224, 224])
+int8_fps = execute_benchmark_on_cpu(int8_model_path, time=15, shape=[1, 3, 224, 224])
 
 print("[5/7] Validate ONNX FP32 model in OpenVINO:")
 fp32_top1 = validate(fp32_model_path, val_loader)
diff --git a/examples/post_training_quantization/onnx/yolov8_quantize_with_accuracy_control/deploy.py b/examples/post_training_quantization/onnx/yolov8_quantize_with_accuracy_control/deploy.py
index a6dbd582df5..812cfe7075a 100644
--- a/examples/post_training_quantization/onnx/yolov8_quantize_with_accuracy_control/deploy.py
+++ b/examples/post_training_quantization/onnx/yolov8_quantize_with_accuracy_control/deploy.py
@@ -9,8 +9,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import re
-import subprocess
 from pathlib import Path
 
 import openvino as ov
@@ -22,6 +20,7 @@
 from ultralytics.utils import DEFAULT_CFG
 from ultralytics.utils.metrics import ConfusionMatrix
 
+from examples import execute_benchmark_on_cpu
 from examples.post_training_quantization.onnx.yolov8_quantize_with_accuracy_control.main import prepare_validation
 from examples.post_training_quantization.onnx.yolov8_quantize_with_accuracy_control.main import print_statistics
 
@@ -64,20 +63,6 @@ def validate_ov_model(
     return stats, validator.seen, validator.metrics.nt_per_class.sum()
 
 
-def run_benchmark(model_path: Path, config) -> float:
-    command = [
-        "benchmark_app",
-        "-m", model_path.as_posix(),
-        "-d", "CPU",
-        "-api", "async",
-        "-t", "30",
-        "-shape", str([1, 3, config.imgsz, config.imgsz]),
-    ]  # fmt: skip
-    cmd_output = subprocess.check_output(command, text=True)  # nosec
-    match = re.search(r"Throughput\: (.+?) FPS", cmd_output)
-    return float(match.group(1))
-
-
 args = get_cfg(cfg=DEFAULT_CFG)
 args.data = "coco128-seg.yaml"
 
@@ -90,11 +75,11 @@ def run_benchmark(model_path: Path, config) -> float:
 ov.save_model(int8_ov_model, INT8_OV_MODEL_PATH, compress_to_fp16=False)
 
 print("[3/7] Benchmark FP32 OpenVINO model:", end=" ")
-fp32_fps = run_benchmark(FP32_OV_MODEL_PATH, args)
+fp32_fps = execute_benchmark_on_cpu(FP32_OV_MODEL_PATH, time=30, shape=[1, 3, args.imgsz, args.imgsz])
 print(f"{fp32_fps} FPS")
 
 print("[4/7] Benchmark INT8 OpenVINO model:", end=" ")
-int8_fps = run_benchmark(INT8_OV_MODEL_PATH, args)
+int8_fps = execute_benchmark_on_cpu(INT8_OV_MODEL_PATH, time=30, shape=[1, 3, args.imgsz, args.imgsz])
 print(f"{int8_fps} FPS")
 
 validator, data_loader = prepare_validation(YOLO(ROOT / f"{MODEL_NAME}.pt"), args)
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 ba48000fe3c..1db1a2cb5fd 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
@@ -10,8 +10,6 @@
 # limitations under the License.
 
 import json
-import re
-import subprocess
 import sys
 from functools import partial
 from pathlib import Path
@@ -26,6 +24,7 @@
 from anomalib.utils.metrics import create_metric_collection
 
 import nncf
+from examples import execute_benchmark_on_cpu
 
 ROOT = Path(__file__).parent.resolve()
 HOME_PATH = Path.home()
@@ -82,21 +81,6 @@ def validate(
     return metric_value, per_sample_metric_values
 
 
-def run_benchmark(model_path: Path, shape: list[int]) -> float:
-    command = [
-        "benchmark_app",
-        "-m", model_path.as_posix(),
-        "-d", "CPU",
-        "-api", "async",
-        "-t", "15",
-        "-shape", str(shape),
-    ]  # fmt: skip
-    cmd_output = subprocess.check_output(command, text=True)  # nosec
-    print(*cmd_output.splitlines()[-8:], sep="\n")
-    match = re.search(r"Throughput\: (.+?) FPS", cmd_output)
-    return float(match.group(1))
-
-
 def get_model_size(ir_path: Path, m_type: str = "Mb") -> float:
     xml_size = ir_path.stat().st_size
     bin_size = ir_path.with_suffix(".bin").stat().st_size
@@ -182,9 +166,9 @@ def transform_fn(data_item):
     int8_size = get_model_size(int8_ir_path)
 
     print("[3/7] Benchmark FP32 model:")
-    fp32_fps = run_benchmark(fp32_ir_path, shape=[1, 3, 256, 256])
+    fp32_fps = execute_benchmark_on_cpu(fp32_ir_path, time=15, shape=[1, 3, 256, 256])
     print("[4/7] Benchmark INT8 model:")
-    int8_fps = run_benchmark(int8_ir_path, shape=[1, 3, 256, 256])
+    int8_fps = execute_benchmark_on_cpu(int8_ir_path, time=15, shape=[1, 3, 256, 256])
 
     print("[5/7] Validate OpenVINO FP32 model:")
     compiled_model = ov.compile_model(ov_model, device_name="CPU")
diff --git a/examples/post_training_quantization/openvino/mobilenet_v2/main.py b/examples/post_training_quantization/openvino/mobilenet_v2/main.py
index a53a0a97fb5..aa09f37f258 100644
--- a/examples/post_training_quantization/openvino/mobilenet_v2/main.py
+++ b/examples/post_training_quantization/openvino/mobilenet_v2/main.py
@@ -9,8 +9,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import re
-import subprocess
 from pathlib import Path
 
 import numpy as np
@@ -23,6 +21,7 @@
 from torchvision import transforms
 
 import nncf
+from examples import execute_benchmark_on_cpu
 
 ROOT = Path(__file__).parent.resolve()
 DATASET_PATH = Path().home() / ".cache" / "nncf" / "datasets"
@@ -54,14 +53,6 @@ def validate(model: ov.Model, val_loader: torch.utils.data.DataLoader) -> float:
     return accuracy_score(predictions, references)
 
 
-def run_benchmark(model_path: Path, shape: list[int]) -> float:
-    cmd = ["benchmark_app", "-m", model_path.as_posix(), "-d", "CPU", "-api", "async", "-t", "15", "-shape", str(shape)]
-    cmd_output = subprocess.check_output(cmd, text=True)  # nosec
-    print(*cmd_output.splitlines()[-8:], sep="\n")
-    match = re.search(r"Throughput\: (.+?) FPS", cmd_output)
-    return float(match.group(1))
-
-
 def get_model_size(ir_path: Path, m_type: str = "Mb") -> float:
     xml_size = ir_path.stat().st_size
     bin_size = ir_path.with_suffix(".bin").stat().st_size
@@ -141,9 +132,10 @@ def transform_fn(data_item):
 int8_model_size = get_model_size(int8_ir_path)
 
 print("[3/7] Benchmark FP32 model:")
-fp32_fps = run_benchmark(fp32_ir_path, shape=[1, 3, 224, 224])
+fp32_fps = execute_benchmark_on_cpu(fp32_ir_path, time=15, shape=[1, 3, 224, 224])
 print("[4/7] Benchmark INT8 model:")
-int8_fps = run_benchmark(int8_ir_path, shape=[1, 3, 224, 224])
+int8_fps = execute_benchmark_on_cpu(int8_ir_path, time=15, shape=[1, 3, 224, 224])
+
 
 print("[5/7] Validate OpenVINO FP32 model:")
 fp32_top1 = validate(ov_model, val_data_loader)
diff --git a/examples/post_training_quantization/openvino/yolo26/main.py b/examples/post_training_quantization/openvino/yolo26/main.py
index 75825744dad..01e8147175f 100644
--- a/examples/post_training_quantization/openvino/yolo26/main.py
+++ b/examples/post_training_quantization/openvino/yolo26/main.py
@@ -8,8 +8,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-import re
-import subprocess
 from pathlib import Path
 from typing import Any
 
@@ -26,6 +24,7 @@
 from ultralytics.utils.metrics import ConfusionMatrix
 
 import nncf
+from examples import execute_benchmark_on_cpu
 
 MODEL_NAME = "yolo26n"
 
@@ -83,20 +82,6 @@ def prepare_validation(model: YOLO, args: Any) -> tuple[DetectionValidator, torc
     return validator, data_loader
 
 
-def benchmark_performance(model_path: Path, config) -> float:
-    command = [
-        "benchmark_app",
-        "-m", model_path.as_posix(),
-        "-d", "CPU",
-        "-api", "async",
-        "-t", "30",
-        "-shape", str([1, 3, config.imgsz, config.imgsz]),
-    ]  # fmt: skip
-    cmd_output = subprocess.check_output(command, text=True)  # nosec
-    match = re.search(r"Throughput\: (.+?) FPS", cmd_output)
-    return float(match.group(1))
-
-
 def prepare_openvino_model(model: YOLO, model_name: str) -> tuple[ov.Model, Path]:
     ir_model_path = ROOT / f"{model_name}_openvino_model" / f"{model_name}.xml"
     if not ir_model_path.exists():
@@ -162,11 +147,11 @@ def main():
     print_statistics(q_stats, total_images, total_objects)
 
     # Benchmark performance of FP32 model
-    fp_model_perf = benchmark_performance(ov_model_path, args)
+    fp_model_perf = execute_benchmark_on_cpu(ov_model_path, time=30, shape=[1, 3, args.imgsz, args.imgsz])
     print(f"Floating-point model performance: {fp_model_perf} FPS")
 
     # Benchmark performance of quantized model
-    quantized_model_perf = benchmark_performance(quantized_model_path, args)
+    quantized_model_perf = execute_benchmark_on_cpu(quantized_model_path, time=30, shape=[1, 3, args.imgsz, args.imgsz])
     print(f"Quantized model performance: {quantized_model_perf} FPS")
 
     return fp_stats["metrics/mAP50-95(B)"], q_stats["metrics/mAP50-95(B)"], fp_model_perf, quantized_model_perf
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 88dba967af1..3c2dfb06091 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
@@ -8,8 +8,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-import re
-import subprocess
 from functools import partial
 from pathlib import Path
 from typing import Any
@@ -28,6 +26,7 @@
 from ultralytics.utils.metrics import ConfusionMatrix
 
 import nncf
+from examples import execute_benchmark_on_cpu
 
 MODEL_NAME = "yolov8n-seg"
 
@@ -109,20 +108,6 @@ def prepare_validation(model: YOLO, args: Any) -> tuple[SegmentationValidator, t
     return validator, data_loader
 
 
-def benchmark_performance(model_path, config) -> float:
-    command = [
-        "benchmark_app",
-        "-m", model_path.as_posix(),
-        "-d", "CPU",
-        "-api", "async",
-        "-t", "30",
-        "-shape", str([1, 3, config.imgsz, config.imgsz]),
-    ]  # fmt: skip
-    cmd_output = subprocess.check_output(command, text=True)  # nosec
-    match = re.search(r"Throughput\: (.+?) FPS", cmd_output)
-    return float(match.group(1))
-
-
 def prepare_openvino_model(model: YOLO, model_name: str) -> tuple[ov.Model, Path]:
     ir_model_path = ROOT / f"{model_name}_openvino_model" / f"{model_name}.xml"
     if not ir_model_path.exists():
@@ -235,11 +220,11 @@ def main():
     print_statistics(q_stats, total_images, total_objects)
 
     # Benchmark performance of FP32 model
-    fp_model_perf = benchmark_performance(ov_model_path, args)
+    fp_model_perf = execute_benchmark_on_cpu(ov_model_path, time=30, shape=[1, 3, args.imgsz, args.imgsz])
     print(f"Floating-point model performance: {fp_model_perf} FPS")
 
     # Benchmark performance of quantized model
-    quantized_model_perf = benchmark_performance(quantized_model_path, args)
+    quantized_model_perf = execute_benchmark_on_cpu(quantized_model_path, time=30, shape=[1, 3, args.imgsz, args.imgsz])
     print(f"Quantized model performance: {quantized_model_perf} FPS")
 
     return fp_stats["metrics/mAP50-95(B)"], q_stats["metrics/mAP50-95(B)"], fp_model_perf, quantized_model_perf
diff --git a/examples/post_training_quantization/torch/mobilenet_v2/main.py b/examples/post_training_quantization/torch/mobilenet_v2/main.py
index 42f59ab401d..360e079f19e 100644
--- a/examples/post_training_quantization/torch/mobilenet_v2/main.py
+++ b/examples/post_training_quantization/torch/mobilenet_v2/main.py
@@ -9,8 +9,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import re
-import subprocess
 from functools import partial
 from pathlib import Path
 
@@ -25,6 +23,7 @@
 from torchvision import transforms
 
 import nncf
+from examples import execute_benchmark_on_cpu
 
 ROOT = Path(__file__).parent.resolve()
 CHECKPOINT_URL = "https://huggingface.co/alexsu52/mobilenet_v2_imagenette/resolve/main/pytorch_model.bin"
@@ -61,21 +60,6 @@ def validate(model: ov.Model, val_loader: torch.utils.data.DataLoader) -> float:
     return accuracy_score(predictions, references)
 
 
-def run_benchmark(model_path: Path, shape: list[int]) -> float:
-    command = [
-        "benchmark_app",
-        "-m", model_path.as_posix(),
-        "-d", "CPU",
-        "-api", "async",
-        "-t", "15",
-        "-shape", str(shape),
-    ]  # fmt: skip
-    cmd_output = subprocess.check_output(command, text=True)  # nosec
-    print(*cmd_output.splitlines()[-8:], sep="\n")
-    match = re.search(r"Throughput\: (.+?) FPS", cmd_output)
-    return float(match.group(1))
-
-
 def get_model_size(ir_path: Path, m_type: str = "Mb") -> float:
     xml_size = ir_path.stat().st_size
     bin_size = ir_path.with_suffix(".bin").stat().st_size
@@ -165,9 +149,10 @@ def transform_fn(data_item: tuple[torch.Tensor, int], device: torch.device) -> t
 int8_model_size = get_model_size(int8_ir_path)
 
 print("[3/7] Benchmark FP32 model:")
-fp32_fps = run_benchmark(fp32_ir_path, shape=[1, 3, 224, 224])
+fp32_fps = execute_benchmark_on_cpu(fp32_ir_path, time=15, shape=[1, 3, 224, 224])
+
 print("[4/7] Benchmark INT8 model:")
-int8_fps = run_benchmark(int8_ir_path, shape=[1, 3, 224, 224])
+int8_fps = execute_benchmark_on_cpu(int8_ir_path, time=15, shape=[1, 3, 224, 224])
 
 print("[5/7] Validate OpenVINO FP32 model:")
 fp32_top1 = validate(ov_model, val_data_loader)
diff --git a/examples/post_training_quantization/torch/ssd300_vgg16/main.py b/examples/post_training_quantization/torch/ssd300_vgg16/main.py
index 2d35fe56043..8d95c484d26 100644
--- a/examples/post_training_quantization/torch/ssd300_vgg16/main.py
+++ b/examples/post_training_quantization/torch/ssd300_vgg16/main.py
@@ -9,8 +9,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # isort: off
-import re
-import subprocess
 from pathlib import Path
 from typing import Callable
 
@@ -21,6 +19,7 @@
 import openvino as ov
 import torch
 import torchvision
+from examples import execute_benchmark_on_cpu
 from fastdownload import FastDownload
 from PIL import Image
 from torchmetrics.detection.mean_ap import MeanAveragePrecision
@@ -54,14 +53,6 @@ def get_model_size(ir_path: Path, m_type: str = "Mb") -> float:
     return model_size
 
 
-def run_benchmark(model_path: Path) -> float:
-    command = ["benchmark_app", "-m", model_path.as_posix(), "-d", "CPU", "-api", "async", "-t", "15"]
-    cmd_output = subprocess.check_output(command, text=True)  # nosec
-    print(*cmd_output.splitlines()[-8:], sep="\n")
-    match = re.search(r"Throughput\: (.+?) FPS", cmd_output)
-    return float(match.group(1))
-
-
 class COCO128Dataset(torch.utils.data.Dataset):
     category_mapping = [
         1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 31, 32, 33,
@@ -169,9 +160,10 @@ def main():
     int8_model_size = get_model_size(int8_ir_path)
 
     print("[3/7] Benchmark FP32 model:")
-    fp32_fps = run_benchmark(fp32_ir_path)
+    fp32_fps = execute_benchmark_on_cpu(fp32_ir_path, time=15)
+
     print("[4/7] Benchmark INT8 model:")
-    int8_fps = run_benchmark(int8_ir_path)
+    int8_fps = execute_benchmark_on_cpu(int8_ir_path, time=15)
 
     print("[5/7] Validate FP32 model:")
     torch.backends.cudnn.deterministic = True
diff --git a/examples/quantization_aware_training/torch/anomalib/main.py b/examples/quantization_aware_training/torch/anomalib/main.py
index f8efb698108..f9d1df21a13 100644
--- a/examples/quantization_aware_training/torch/anomalib/main.py
+++ b/examples/quantization_aware_training/torch/anomalib/main.py
@@ -10,8 +10,6 @@
 # limitations under the License.
 
 import os
-import re
-import subprocess
 import tarfile
 import warnings
 from copy import deepcopy
@@ -27,6 +25,7 @@
 from anomalib.models import Stfpm
 
 import nncf
+from examples import execute_benchmark_on_cpu
 
 warnings.filterwarnings("ignore", category=torch.jit.TracerWarning)
 
@@ -93,21 +92,6 @@ def create_dataset(root: Path) -> MVTecAD:
     return data
 
 
-def run_benchmark(model_path: Path, shape: list[int]) -> float:
-    command = [
-        "benchmark_app",
-        "-m", model_path.as_posix(),
-        "-d", "CPU",
-        "-api", "async",
-        "-t", "15",
-        "-shape", str(shape),
-    ]  # fmt: skip
-    cmd_output = subprocess.check_output(command, text=True)  # nosec
-    print(*cmd_output.splitlines()[-8:], sep="\n")
-    match = re.search(r"Throughput\: (.+?) FPS", cmd_output)
-    return float(match.group(1))
-
-
 def get_model_size(ir_path: Path, m_type: str = "Mb") -> float:
     xml_size = ir_path.stat().st_size
     bin_size = ir_path.with_suffix(".bin").stat().st_size
@@ -209,10 +193,10 @@ def transform_fn(data_item):
     print(os.linesep + "[Step 5] Run benchmarks")
 
     print("Run benchmark for FP32 model (IR)...")
-    fp32_fps = run_benchmark(fp32_ir_path, shape=[1, 3, 256, 256])
+    fp32_fps = execute_benchmark_on_cpu(fp32_ir_path, time=15, shape=[1, 3, 256, 256])
 
     print("Run benchmark for INT8 model (IR)...")
-    int8_fps = run_benchmark(int8_ir_path, shape=[1, 3, 256, 256])
+    int8_fps = execute_benchmark_on_cpu(int8_ir_path, time=15, shape=[1, 3, 256, 256])
 
     ###############################################################################
     # Step 6: Summary
diff --git a/examples/quantization_aware_training/torch/resnet18/main.py b/examples/quantization_aware_training/torch/resnet18/main.py
index e9a89461746..7b841e15f95 100644
--- a/examples/quantization_aware_training/torch/resnet18/main.py
+++ b/examples/quantization_aware_training/torch/resnet18/main.py
@@ -10,8 +10,6 @@
 # limitations under the License.
 
 import os
-import re
-import subprocess
 import warnings
 from copy import deepcopy
 from pathlib import Path
@@ -30,6 +28,7 @@
 from torch.jit import TracerWarning
 
 import nncf
+from examples import execute_benchmark_on_cpu
 from nncf.common.utils.helpers import create_table
 
 warnings.filterwarnings("ignore", category=TracerWarning)
@@ -205,20 +204,6 @@ def prepare_tiny_imagenet_200(dataset_dir: Path):
     val_images_dir.rmdir()
 
 
-def run_benchmark(model_path: Path, shape: tuple[int, ...]) -> float:
-    command = [
-        "benchmark_app",
-        "-m", model_path.as_posix(),
-        "-d", "CPU",
-        "-api", "async",
-        "-t", "15",
-        "-shape", str(list(shape)),
-    ]  # fmt: skip
-    cmd_output = subprocess.check_output(command, text=True)  # nosec
-    match = re.search(r"Throughput\: (.+?) FPS", cmd_output)
-    return float(match.group(1))
-
-
 def get_model_size(ir_path: Path, m_type: str = "Mb") -> float:
     xml_size = ir_path.stat().st_size
     bin_size = ir_path.with_suffix(".bin").stat().st_size
@@ -323,10 +308,10 @@ def transform_fn(data_item):
     # Step 5: Run benchmarks
     print(os.linesep + "[Step 5] Run benchmarks")
     print("Run benchmark for FP32 model (IR)...")
-    fp32_fps = run_benchmark(fp32_ir_path, shape=input_shape)
+    fp32_fps = execute_benchmark_on_cpu(fp32_ir_path, time=15, shape=input_shape)
 
     print("Run benchmark for INT8 model (IR)...")
-    int8_fps = run_benchmark(int8_ir_path, shape=input_shape)
+    int8_fps = execute_benchmark_on_cpu(int8_ir_path, time=15, shape=input_shape)
 
     fp32_model_size = get_model_size(fp32_ir_path)
     int8_model_size = get_model_size(int8_ir_path)
diff --git a/pyproject.toml b/pyproject.toml
index 599eca11a90..fd197c673d4 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -203,7 +203,6 @@ pythonpath = "."
 exclude_dirs = ["tools", "tests", "**/venv*", "build"]
 skips = [
     "B101", # assert_used
-    "B404", # import_subprocess
     "B614", # pytorch_load
     "B615", # huggingface_unsafe_download
 ]
diff --git a/src/custom_version.py b/src/custom_version.py
index 905efd7f22d..6e603364792 100644
--- a/src/custom_version.py
+++ b/src/custom_version.py
@@ -48,7 +48,7 @@
 import contextlib
 import os
 import re
-import subprocess
+import subprocess  # nosec B404; only git commands are executed (user data do not modified in any way executed commands)
 from pathlib import Path
 
 NNCF_VERSION_FILE = "src/nncf/version.py"
diff --git a/src/nncf/torch/quantization/extensions.py b/src/nncf/torch/quantization/extensions.py
index cb13acc39e7..79d670245e1 100644
--- a/src/nncf/torch/quantization/extensions.py
+++ b/src/nncf/torch/quantization/extensions.py
@@ -9,7 +9,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import subprocess
+from subprocess import (
+    CalledProcessError,  # nosec B404: imported only for CalledProcessError exception handling; no subprocess commands are executed in this module
+)
 
 import torch
 
@@ -92,7 +94,7 @@ def load(cls):
             )
         except ExtensionLoaderTimeoutException as e:
             raise e
-        except (subprocess.CalledProcessError, OSError, RuntimeError) as e:
+        except (CalledProcessError, OSError, RuntimeError) as e:
             assert torch.cuda.is_available()
             msg = (
                 "CUDA is available for PyTorch, but NNCF could not compile "