diff --git a/neural_compressor/common/base_config.py b/neural_compressor/common/base_config.py
index 35b0f532738..3f65a2ea9c0 100644
--- a/neural_compressor/common/base_config.py
+++ b/neural_compressor/common/base_config.py
@@ -377,6 +377,9 @@ def expand(self) -> List[BaseConfig]:
         if len(tuning_param_list) == 0:
             config_list = [config]
         else:
+            # The `TuningParam` instance with no options will cause the product to be empty.
+            # Filter out the `TuningParam` instances with no options
+            tuning_param_list = list(filter(lambda x: len(x.options) > 0, tuning_param_list))
             tuning_param_name_lst = [tuning_param.name for tuning_param in tuning_param_list]
             for params_values in product(*[tuning_param.options for tuning_param in tuning_param_list]):
                 tuning_param_pair = dict(zip(tuning_param_name_lst, params_values))
diff --git a/test/3x/common/test_common.py b/test/3x/common/test_common.py
index 4af0e1a276d..90a5db3c315 100644
--- a/test/3x/common/test_common.py
+++ b/test/3x/common/test_common.py
@@ -277,6 +277,11 @@ def test_config_expand_complex_tunable_type(self):
         for i in range(len(configs_list)):
             self.assertEqual(configs_list[i].target_op_type_list, target_op_type_list_options[i])
 
+    def test_config_expand_with_empty_options(self):
+        configs = FakeAlgoConfig(weight_dtype=["int", "float32"], weight_bits=[])
+        configs_list = configs.expand()
+        self.assertEqual(len(configs_list), 2)
+
     def test_mixed_two_algos(self):
         model = FakeModel()
         OP1_NAME = "OP1_NAME"