diff --git a/examples/common_parser.py b/examples/common_parser.py
new file mode 100644
index 0000000000..2f0d329da3
--- /dev/null
+++ b/examples/common_parser.py
@@ -0,0 +1,21 @@
+from argparse import ArgumentParser
+
+
+def add_profiling_args(parser: ArgumentParser) -> None:
+    parser.add_argument(
+        "--profiling_warmup_steps",
+        default=0,
+        type=int,
+        help="Number of steps to ignore for profiling.",
+    )
+    parser.add_argument(
+        "--profiling_steps",
+        default=0,
+        type=int,
+        help="Number of steps to capture for profiling.",
+    )
+    parser.add_argument(
+        "--profiling_record_shapes",
+        action="store_true",
+        help="Record shapes when enabling profiling.",
+    )
diff --git a/examples/text-generation/run_generation.py b/examples/text-generation/run_generation.py
index ceda678419..e00c4768db 100755
--- a/examples/text-generation/run_generation.py
+++ b/examples/text-generation/run_generation.py
@@ -23,6 +23,7 @@
 import logging
 import math
 import os
+import sys
 import struct
 from itertools import cycle
 from pathlib import Path
@@ -40,6 +41,12 @@
 )
 
 
+project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), "../../"))
+if project_root not in sys.path:
+    sys.path.insert(0, project_root)
+from examples.common_parser import add_profiling_args  # noqa: E402
+
+
 logging.basicConfig(
     format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
     datefmt="%m/%d/%Y %H:%M:%S",
@@ -140,23 +147,7 @@ def setup_parser(parser):
         type=int,
         help="Seed to use for random generation. Useful to reproduce your runs with `--do_sample`.",
     )
-    parser.add_argument(
-        "--profiling_warmup_steps",
-        default=0,
-        type=int,
-        help="Number of steps to ignore for profiling.",
-    )
-    parser.add_argument(
-        "--profiling_steps",
-        default=0,
-        type=int,
-        help="Number of steps to capture for profiling.",
-    )
-    parser.add_argument(
-        "--profiling_record_shapes",
-        action="store_true",
-        help="Record shapes when enabling profiling.",
-    )
+    add_profiling_args(parser)
     parser.add_argument(
         "--profile_whole_sequences",
         action="store_true",
diff --git a/examples/video-comprehension/run_example.py b/examples/video-comprehension/run_example.py
index b53679fb0b..258cf444c8 100644
--- a/examples/video-comprehension/run_example.py
+++ b/examples/video-comprehension/run_example.py
@@ -17,6 +17,7 @@
 import json
 import logging
 import os
+import sys
 import time
 from pathlib import Path
 
@@ -32,6 +33,12 @@
 )
 
 
+project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), "../../"))
+if project_root not in sys.path:
+    sys.path.insert(0, project_root)
+from examples.common_parser import add_profiling_args  # noqa: E402
+
+
 logging.basicConfig(
     format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
     datefmt="%m/%d/%Y %H:%M:%S",
@@ -118,6 +125,7 @@ def main():
         help="Whether to enable Habana Flash Attention in recompute mode on first token generation. This gives an opportunity of splitting graph internally which helps reduce memory consumption.",
     )
 
+    add_profiling_args(parser)
     args = parser.parse_args()
 
     os.environ.setdefault("EXPERIMENTAL_WEIGHT_SHARING", "FALSE")
@@ -186,6 +194,9 @@ def main():
         )
     torch.hpu.synchronize()
 
+    from optimum.habana.utils import HabanaProfile
+
+    HabanaProfile.enable()
     start = time.perf_counter()
     for i in range(args.n_iterations):
         generate_ids = model.generate(
@@ -196,12 +207,16 @@ def main():
             ignore_eos=args.ignore_eos,
             use_flash_attention=args.use_flash_attention,
             flash_attention_recompute=args.flash_attention_recompute,
+            profiling_steps=args.profiling_steps,
+            profiling_warmup_steps=args.profiling_warmup_steps,
+            profiling_record_shapes=args.profiling_record_shapes,
         )
         generate_texts = processor.batch_decode(
             generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False
         )
     end = time.perf_counter()
     duration = end - start
+    HabanaProfile.disable()
 
     # Let's calculate the number of generated tokens
     n_input_tokens = inputs["input_ids"].shape[1]