diff --git a/vllm/nvfp4_cutedsl.py b/vllm/nvfp4_cutedsl.py
index aea180a5..34f5ec74 100644
--- a/vllm/nvfp4_cutedsl.py
+++ b/vllm/nvfp4_cutedsl.py
@@ -45,7 +45,11 @@ class CuTeDSLMoERunner:
         self._l2_gsb = None
 
     def _ensure_stacked(self):
-        """Lazily stack weight tensors into the format the kernel expects."""
+        """Lazily stack weight tensors into the format the kernel expects.
+        
+        After stacking, the per-expert lists are freed to avoid holding
+        two copies of ~175GB of weight data in GPU memory.
+        """
         if self._l1_mat_b is not None:
             return
         self._l1_mat_b = make_b_k_major(torch.stack(self.l1_fp4))
@@ -54,6 +58,13 @@ class CuTeDSLMoERunner:
         self._l2_scale_b = assemble_scales_3d_side(self.l2_sf)
         self._l1_gsb = torch.tensor(self.l1_gs, dtype=torch.float32, device=self.device)
         self._l2_gsb = torch.tensor(self.l2_gs, dtype=torch.float32, device=self.device)
+        # Free per-expert lists — stacked tensors are the only copy now
+        self.l1_fp4 = None
+        self.l1_sf = None
+        self.l1_gs = None
+        self.l2_fp4 = None
+        self.l2_sf = None
+        self.l2_gs = None
 
     def prepare_weights_direct(self, l1_fp4, l1_sf, l1_gs, l2_fp4, l2_sf, l2_gs):
         """Set weights directly from checkpoint (no dequant→requant)."""