diff --git a/tests/transformers/tests/generation/test_utils.py b/tests/transformers/tests/generation/test_utils.py
index b9dbd5baac..2c6ad23d22 100644
--- a/tests/transformers/tests/generation/test_utils.py
+++ b/tests/transformers/tests/generation/test_utils.py
@@ -84,6 +84,11 @@ class GenerationTesterMixin:
     all_generative_model_classes = ()
     input_name = "input_ids"
 
+    def _update_default_model_kwargs(self, model_kwargs):
+        model_kwargs["limit_hpu_graphs"] = False
+        model_kwargs["reuse_cache"] = False
+        model_kwargs["bucket_size"] = -1
+
     def _get_input_ids_and_config(self, batch_size=2):
         config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
         input_ids = inputs_dict[self.input_name]
@@ -268,6 +273,7 @@ def _greedy_generate(
 
         kwargs = {}
         model_kwargs = {"attention_mask": attention_mask} if attention_mask is not None else {}
+
         output_generate = model.generate(
             input_ids,
             do_sample=False,
@@ -294,6 +300,7 @@ def _greedy_generate(
 
         with torch.no_grad():
             model_kwargs = {"attention_mask": attention_mask} if attention_mask is not None else {}
+            self._update_default_model_kwargs(model_kwargs)
             output_greedy = model.greedy_search(
                 input_ids,
                 max_length=max_length,
@@ -423,6 +430,7 @@ def _beam_search_generate(
 
         with torch.no_grad():
             model_kwargs = {"attention_mask": attention_mask} if attention_mask is not None else {}
+            self._update_default_model_kwargs(model_kwargs)
             output_beam_search = model.beam_search(
                 input_ids.repeat_interleave(beam_scorer.num_beams, dim=0),
                 beam_scorer,
@@ -552,6 +560,8 @@ def _group_beam_search_generate(
 
         with torch.no_grad():
             model_kwargs = {"attention_mask": attention_mask} if attention_mask is not None else {}
+            self._update_default_model_kwargs(model_kwargs)
+
             output_group_beam_search = model.group_beam_search(
                 input_ids.repeat_interleave(beam_scorer.num_beams, dim=0),
                 beam_scorer,
@@ -615,6 +625,7 @@ def _constrained_beam_search_generate(
 
         with torch.no_grad():
             model_kwargs = {"attention_mask": attention_mask} if attention_mask is not None else {}
+            self._update_default_model_kwargs(model_kwargs)
             output_group_beam_search = model.constrained_beam_search(
                 input_ids.repeat_interleave(constrained_beam_scorer.num_beams, dim=0),
                 constrained_beam_scorer,