diff --git a/src/llmcompressor/modifiers/smoothquant/base.py b/src/llmcompressor/modifiers/smoothquant/base.py
index f4117e31d..9381348b1 100644
--- a/src/llmcompressor/modifiers/smoothquant/base.py
+++ b/src/llmcompressor/modifiers/smoothquant/base.py
@@ -2,6 +2,7 @@
 from typing import Callable, Dict, List, Optional, Tuple
 
 import torch
+from compressed_tensors.utils.offload import is_module_offloaded
 from loguru import logger
 from torch.nn import Module
 
@@ -282,6 +283,10 @@ def _apply_smoothing(self, model: Module):
 
             @torch.no_grad()
             def smooth(module):
+                offloaded = is_module_offloaded(module)
+                if offloaded:
+                    module._hf_hook.pre_forward(module)
+
                 if module in balance_layers:
                     module.weight.mul_(scales.view(1, -1))
                 elif module == smooth_layer:
@@ -292,6 +297,9 @@ def smooth(module):
                     if hasattr(module, "bias") and module.bias is not None:
                         module.bias.div_(scales)
 
+                if offloaded:
+                    module._hf_hook.post_forward(module, None)
+
             parent = get_fsdp_parent(mapping.smooth_name, model)
             if parent is not None:
                 parent.apply(smooth)
@@ -318,8 +326,16 @@ def _calculate_smoothing_scales(
         # get the channel-wise dynamic range for each layer to be balanced
         weight_scales = []
         for layer in balance_layers:
+            offloaded = is_module_offloaded(layer)
+            if offloaded:
+                layer._hf_hook.pre_forward(layer)
+
             scale = layer.weight.abs().max(dim=0, keepdim=True)[0]
             weight_scales.append(scale)
+
+            if offloaded:
+                layer._hf_hook.post_forward(layer, None)
+
         weight_scales = 2.0 * torch.cat(weight_scales, dim=0).max(dim=0)[0]
 
         # calculate the amount of smoothing to apply
@@ -329,4 +345,5 @@ def _calculate_smoothing_scales(
             1 - self.smoothing_strength
         )
         scales = torch.where(weight_scales > 0.0, scales, activation_scales)
+
         return scales