diff --git a/src/accelerate/hooks.py b/src/accelerate/hooks.py
index 6b28642b30f..3a6e3890471 100644
--- a/src/accelerate/hooks.py
+++ b/src/accelerate/hooks.py
@@ -342,6 +342,19 @@ def init_hook(self, module):
 
         return module
 
+    def _maybe_get_fp16_statistics(self, name, value):
+        # Some quantized weights keep scale statistics as separate state-dict entries rather than
+        # parameters or buffers. When materializing an int8 weight from `weights_map`, pass those
+        # statistics along so the restored parameter does not keep stale meta-device attributes.
+        if value is None or value.dtype != torch.int8 or "weight" not in name:
+            return None
+
+        statistics_name = name.replace("weight", "SCB")
+        if statistics_name in self.weights_map:
+            return self.weights_map[statistics_name]
+
+        return None
+
     @_compiler_disable
     def pre_forward(self, module, *args, **kwargs):
         if self.io_same_device:
@@ -355,11 +368,8 @@ def pre_forward(self, module, *args, **kwargs):
                 recurse=self.place_submodules,
                 remove_non_persistent=True,
             ):
-                fp16_statistics = None
                 value = self.weights_map[name]
-                if "weight" in name and name.replace("weight", "SCB") in self.weights_map.keys():
-                    if value.dtype == torch.int8:
-                        fp16_statistics = self.weights_map[name.replace("weight", "SCB")]
+                fp16_statistics = self._maybe_get_fp16_statistics(name, value)
 
                 # In case we are using offloading with tied weights, we need to keep track of the offloaded weights
                 # that are loaded on device at this point, as we will need to remove them as well from the dictionary
@@ -424,7 +434,11 @@ def detach_hook(self, module):
         if self.offload:
             for name, device in self.original_devices.items():
                 if device != torch.device("meta"):
-                    set_module_tensor_to_device(module, name, device, value=self.weights_map.get(name, None))
+                    value = self.weights_map.get(name, None)
+                    fp16_statistics = self._maybe_get_fp16_statistics(name, value)
+                    set_module_tensor_to_device(
+                        module, name, device, value=value, fp16_statistics=fp16_statistics
+                    )
         return module