diff --git a/src/retargeters/dex_hand_retargeter.py b/src/retargeters/dex_hand_retargeter.py
index b056de146..ad4d38db4 100644
--- a/src/retargeters/dex_hand_retargeter.py
+++ b/src/retargeters/dex_hand_retargeter.py
@@ -412,10 +412,16 @@ def _compute_hand(self, poses: Dict[str, np.ndarray]) -> np.ndarray:
             ref_value = target_pos[task_indices, :] - target_pos[origin_indices, :]
 
         # 4. Run optimizer
+        # ``dex_retargeting`` solves a QP-style optimization that does not
+        # require autograd. ``torch.no_grad()`` avoids the per-step
+        # grad-tracking overhead the previous ``enable_grad`` context paid for.
+        # ``torch.inference_mode(False)`` is preserved so callers running
+        # inside an outer ``torch.inference_mode()`` (where some in-place /
+        # view ops can error) still see the optimizer execute in normal mode.
         try:
             import torch  # type: ignore
 
-            with torch.enable_grad(), torch.inference_mode(False):
+            with torch.inference_mode(False), torch.no_grad():
                 return self._dex_hand.retarget(ref_value)  # type: ignore
         except Exception as e:
             logger.error(f"Error in retargeting: {e}")