diff --git a/autosklearn/automl.py b/autosklearn/automl.py
index 414a0ee8d8..7bdbea6434 100644
--- a/autosklearn/automl.py
+++ b/autosklearn/automl.py
@@ -814,7 +814,7 @@ def fit(
 
     @staticmethod
     def subsample_if_too_large(X, y, logger, seed, memory_limit, task):
-        if isinstance(X, np.ndarray):
+        if memory_limit and isinstance(X, np.ndarray):
             if X.dtype == np.float32:
                 multiplier = 4
             elif X.dtype in (np.float64, np.float):
diff --git a/test/test_automl/test_automl.py b/test/test_automl/test_automl.py
index c358ac9553..d8861480d0 100644
--- a/test/test_automl/test_automl.py
+++ b/test/test_automl/test_automl.py
@@ -664,16 +664,16 @@ def test_fail_if_feat_type_on_pandas_input(backend, dask_client):
     [
         (memory_limit, task)
         for task in itertools.chain(CLASSIFICATION_TASKS, REGRESSION_TASKS)
-        for memory_limit in (1, 10)
+        for memory_limit in (1, 10, None)
     ]
 )
 def test_subsample_if_too_large(memory_limit, task):
     fixture = {
-        BINARY_CLASSIFICATION: {1: 436, 10: 569},
-        MULTICLASS_CLASSIFICATION: {1: 204, 10: 1797},
-        MULTILABEL_CLASSIFICATION: {1: 204, 10: 1797},
-        REGRESSION: {1: 1310, 10: 1326},
-        MULTIOUTPUT_REGRESSION: {1: 1310, 10: 1326}
+        BINARY_CLASSIFICATION: {1: 436, 10: 569, None: 569},
+        MULTICLASS_CLASSIFICATION: {1: 204, 10: 1797, None: 1797},
+        MULTILABEL_CLASSIFICATION: {1: 204, 10: 1797, None: 1797},
+        REGRESSION: {1: 1310, 10: 1326, None: 1326},
+        MULTIOUTPUT_REGRESSION: {1: 1310, 10: 1326, None: 1326}
     }
     mock = unittest.mock.Mock()
     if task == BINARY_CLASSIFICATION: