diff --git a/src/transformers/generation/utils.py b/src/transformers/generation/utils.py
index e7736537e0c..385d68cfbef 100644
--- a/src/transformers/generation/utils.py
+++ b/src/transformers/generation/utils.py
@@ -1813,7 +1813,9 @@ class GenerationMixin:
                     )
                 model_kwargs[cache_name] = self._get_cache(
                     generation_config.cache_implementation,
-                    getattr(generation_config, "num_beams", 1) * batch_size,
+                    getattr(generation_config, "num_beams", 1)
+                    * getattr(generation_config, "num_return_sequences", 1)
+                    * batch_size,
                     generation_config.max_length,
                     model_kwargs,
                 )
diff --git a/tests/utils/test_cache_utils.py b/tests/utils/test_cache_utils.py
index 2729a2989ab..e8d120216a0 100644
--- a/tests/utils/test_cache_utils.py
+++ b/tests/utils/test_cache_utils.py
@@ -292,6 +292,30 @@ class CacheIntegrationTest(unittest.TestCase):
         ]
         self.assertListEqual(decoded, expected_text)
 
+    def test_hybrid_cache_n_sequences(self):
+        tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-9b")
+        model = AutoModelForCausalLM.from_pretrained(
+            "google/gemma-2-9b",
+            device_map="auto",
+            torch_dtype=torch.bfloat16,
+            attn_implementation="eager",
+        )
+
+        inputs = tokenizer(["Hello I am doing"], return_tensors="pt").to(model.device)
+
+        gen_out = model.generate(
+            **inputs,
+            do_sample=False,
+            max_new_tokens=20,
+            num_return_sequences=2,
+        )
+        decoded = tokenizer.batch_decode(gen_out, skip_special_tokens=True)
+        expected_text = [
+            "Hello I am doing a project on the 1918 flu pandemic and I am trying to find out how many",
+            "Hello I am doing a project on the 1918 flu pandemic and I am trying to find out how many",
+        ]
+        self.assertListEqual(decoded, expected_text)
+
     @require_auto_gptq
     def test_sink_cache_hard(self):
         tokenizer = AutoTokenizer.from_pretrained("TheBloke/LLaMa-7B-GPTQ")