diff --git a/selfdrive/modeld/compile_modeld.py b/selfdrive/modeld/compile_modeld.py
index 031f13be3..633f1f03b 100755
--- a/selfdrive/modeld/compile_modeld.py
+++ b/selfdrive/modeld/compile_modeld.py
@@ -103,10 +103,10 @@ def make_input_queues(vision_input_shapes, policy_input_shapes, frame_skip, devi
     'big_tfm': np.zeros((3, 3), dtype=np.float32),
   }
   input_queues = {
-    'img_q': Tensor.zeros(img_buf_shape, dtype='uint8', device=device).contiguous().realize(),
-    'big_img_q': Tensor.zeros(img_buf_shape, dtype='uint8', device=device).contiguous().realize(),
-    'feat_q': Tensor.zeros(frame_skip * (fb[1] - 1) + 1, fb[0], fb[2], device=device).contiguous().realize(),
-    'desire_q': Tensor.zeros(frame_skip * dp[1], dp[0], dp[2], device=device).contiguous().realize(),
+    'img_q': Tensor(np.zeros(img_buf_shape, dtype=np.uint8), device=device).contiguous().realize(),
+    'big_img_q': Tensor(np.zeros(img_buf_shape, dtype=np.uint8), device=device).contiguous().realize(),
+    'feat_q': Tensor(np.zeros((frame_skip * (fb[1] - 1) + 1, fb[0], fb[2]), dtype=np.float32), device=device).contiguous().realize(),
+    'desire_q': Tensor(np.zeros((frame_skip * dp[1], dp[0], dp[2]), dtype=np.float32), device=device).contiguous().realize(),
     **{k: Tensor(v, device='NPY').realize() for k, v in npy.items()},
   }
   return input_queues, npy
diff --git a/selfdrive/modeld/modeld.py b/selfdrive/modeld/modeld.py
index 6aa94be49..93de1ee82 100755
--- a/selfdrive/modeld/modeld.py
+++ b/selfdrive/modeld/modeld.py
@@ -95,8 +95,8 @@ class ModelState:
     self.warp_enqueue = jits[(cam_w,cam_h)]['warp_enqueue']
     self.warp_enqueue(
       **self.input_queues,
-      frame=Tensor.zeros(self.frame_buf_params['img'][3], dtype='uint8', device=self.DEV).contiguous().realize(),
-      big_frame=Tensor.zeros(self.frame_buf_params['big_img'][3], dtype='uint8', device=self.DEV).contiguous().realize())
+      frame=Tensor(np.zeros(self.frame_buf_params['img'][3], dtype=np.uint8), device=self.DEV).contiguous().realize(),
+      big_frame=Tensor(np.zeros(self.frame_buf_params['big_img'][3], dtype=np.uint8), device=self.DEV).contiguous().realize())
 
   def slice_outputs(self, model_outputs: np.ndarray, output_slices: dict[str, slice]) -> dict[str, np.ndarray]:
     parsed_model_outputs = {k: model_outputs[np.newaxis, v] for k,v in output_slices.items()}