diff --git a/optimum/intel/openvino/quantization.py b/optimum/intel/openvino/quantization.py
index aae66c148b..07765920bc 100644
--- a/optimum/intel/openvino/quantization.py
+++ b/optimum/intel/openvino/quantization.py
@@ -21,6 +21,8 @@
 from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, Union
 
 import datasets
+from transformers.utils.quantization_config import QuantizationMethod
+
 import nncf
 import openvino
 import torch
@@ -677,10 +679,10 @@ def _weight_only_quantization(
         group_size=config.group_size,
         all_layers=config.all_layers,
         sensitivity_metric=sensitivity_metric,
-        # awq=config.quant_method == QuantizationMethod.AWQ,    # TODO : enable from nncf v2.9.0
+        awq=config.quant_method == QuantizationMethod.AWQ,
         ignored_scope=config.get_ignored_scope_instance(),
         dataset=dataset,
-        # subset_size=config.num_samples if config.num_samples else 128,    # TODO : enable from nncf v2.9.0
+        subset_size=config.num_samples if config.num_samples else 128,
     )
 
 
diff --git a/setup.py b/setup.py
index ea87e6ad59..bc91ba2472 100644
--- a/setup.py
+++ b/setup.py
@@ -60,8 +60,8 @@
 
 EXTRAS_REQUIRE = {
     "neural-compressor": ["neural-compressor>=2.2.0", "onnxruntime<1.15.0", "accelerate"],
-    "openvino": ["openvino>=2023.3", "nncf>=2.8.1", "openvino-tokenizers[transformers]"],
-    "nncf": ["nncf>=2.8.1"],
+    "openvino": ["openvino>=2023.3", "nncf>=2.10.0", "openvino-tokenizers[transformers]"],
+    "nncf": ["nncf>=2.10.0"],
     "ipex": ["intel-extension-for-pytorch", "transformers>=4.36.0,<4.39.0"],
     "diffusers": ["diffusers"],
     "quality": QUALITY_REQUIRE,
diff --git a/tests/openvino/test_quantization.py b/tests/openvino/test_quantization.py
index e269578c35..ca4d43d295 100644
--- a/tests/openvino/test_quantization.py
+++ b/tests/openvino/test_quantization.py
@@ -224,17 +224,17 @@ class OVWeightCompressionTest(unittest.TestCase):
         ),
         (
             OVModelForCausalLM,
-            "hf-internal-testing/tiny-random-OPTForCausalLM",
+            "HuggingFaceH4/tiny-random-LlamaForCausalLM",
             dict(
                 bits=4,
                 sym=True,
-                group_size=-1,
+                group_size=16,
                 ratio=0.8,
                 sensitivity_metric="mean_activation_magnitude",
                 dataset="ptb",
                 quant_method=QuantizationMethod.AWQ,
             ),
-            14,
+            16,
         ),
     )
 
@@ -455,7 +455,20 @@ def test_ovmodel_4bit_auto_compression_with_config(
     ):
         with tempfile.TemporaryDirectory() as tmp_dir:
             quantization_config = OVWeightQuantizationConfig.from_dict(quantization_config)
-            model = model_cls.from_pretrained(model_id, export=True, quantization_config=quantization_config)
+
+            from nncf.common.logging.track_progress import track
+
+            with unittest.mock.patch("nncf.common.logging.track_progress.track", wraps=track) as track_patch:
+                model = model_cls.from_pretrained(model_id, export=True, quantization_config=quantization_config)
+                if quantization_config.quant_method == QuantizationMethod.AWQ:
+                    # Called at least once with description="Applying AWQ"
+                    self.assertTrue(
+                        any(
+                            args.kwargs.get("description", None) == "Applying AWQ"
+                            for args in track_patch.call_args_list
+                        )
+                    )
+
             tokenizer = AutoTokenizer.from_pretrained(model_id)
             if tokenizer.pad_token is None:
                 tokenizer.pad_token = tokenizer.eos_token