diff --git a/keras_core/layers/rnn/dropout_rnn_cell_test.py b/keras_core/layers/rnn/dropout_rnn_cell_test.py
index a73512f75..4062f0aec 100644
--- a/keras_core/layers/rnn/dropout_rnn_cell_test.py
+++ b/keras_core/layers/rnn/dropout_rnn_cell_test.py
@@ -67,19 +67,27 @@ def test_basics(self):
             run_mixed_precision_check=False,
         )
 
-        # custom mixed_float16 check
-        self.run_layer_test(
-            layers.RNN,
-            init_kwargs={
-                "cell": RNNCellWithDropout(5, seed=1337, dtype="mixed_float16"),
-                "dtype": "mixed_float16",
-            },
-            input_shape=(3, 2, 4),
-            call_kwargs={"training": True},
-            expected_output_shape=(3, 5),
-            expected_num_trainable_weights=2,
-            expected_num_non_trainable_weights=0,
-            expected_num_non_trainable_variables=1,
-            supports_masking=True,
-            run_mixed_precision_check=False,
-        )
+        # Custom mixed_float16 check
+        # Never test mixed precision on torch CPU. Torch lacks support.
+        if backend.backend() == "torch":
+            import torch
+
+            run_mixed_precision_check = torch.cuda.is_available()
+        if run_mixed_precision_check:
+            self.run_layer_test(
+                layers.RNN,
+                init_kwargs={
+                    "cell": RNNCellWithDropout(
+                        5, seed=1337, dtype="mixed_float16"
+                    ),
+                    "dtype": "mixed_float16",
+                },
+                input_shape=(3, 2, 4),
+                call_kwargs={"training": True},
+                expected_output_shape=(3, 5),
+                expected_num_trainable_weights=2,
+                expected_num_non_trainable_weights=0,
+                expected_num_non_trainable_variables=1,
+                supports_masking=True,
+                run_mixed_precision_check=False,
+            )