diff --git a/src/llmcompressor/modifiers/quantization/gptq/utils/gptq_wrapper.py b/src/llmcompressor/modifiers/quantization/gptq/utils/gptq_wrapper.py
index c47969893..ebd4f2c97 100644
--- a/src/llmcompressor/modifiers/quantization/gptq/utils/gptq_wrapper.py
+++ b/src/llmcompressor/modifiers/quantization/gptq/utils/gptq_wrapper.py
@@ -100,6 +100,24 @@ def compress(
         W[:, dead] = 0
 
         g_idx = None
+        # if hasattr(self.layer, "quantization_scheme"):
+        #     quant_scheme = self.layer.quantization_scheme
+        #     actorder = quant_scheme.weights.actorder
+
+        #     if actorder:
+        #         group_size = quant_scheme.weights.group_size
+        #         perm = torch.argsort(torch.diag(self.H), descending=True)
+        #         # W = W[:, perm]
+        #         # self.H = self.H[perm][:, perm]
+        #         invperm = torch.argsort(perm)
+
+        #         # g_idx = torch.Tensor(
+        #         #     [perm[i] // group_size for i in range(self.columns)]
+        #         # ).to(device=invperm.device)
+        #         g_idx = torch.Tensor(
+        #             [i // group_size for i in range(self.columns)]
+        #         ).to(device=invperm.device)
+        #         self.layer.weight_g_idx.data = g_idx
         if hasattr(self.layer, "quantization_scheme"):
             quant_scheme = self.layer.quantization_scheme
             actorder = quant_scheme.weights.actorder
@@ -114,8 +132,11 @@ def compress(
                 g_idx = torch.Tensor(
                     [perm[i] // group_size for i in range(self.columns)]
                 ).to(device=invperm.device)
+                # g_idx = torch.Tensor(
+                #     [i // group_size for i in range(self.columns)]
+                # ).to(device=invperm.device)
                 self.layer.weight_g_idx.data = g_idx
-
+        
         Losses = torch.zeros(self.rows, device=self.dev)
 
         damp = percdamp * torch.mean(torch.diag(self.H))
@@ -200,24 +221,22 @@ def compress(
                             altered_qargs = copy(quant_scheme.weights)
                             altered_qargs.strategy = QuantizationStrategy.CHANNEL
 
-                            # apply g_idx
                             if g_idx is not None:
-                                # scale and zp already transformed by group_size
-                                # extract first index of group_size
-                                indices_to_extract = torch.arange(
-                                    0, g_idx.shape[0], group_size
+                                q = fake_quantize(
+                                    q,
+                                    scale[:, int(g_idx[column_idx])],
+                                    zero_point[:, int(g_idx[column_idx])],
+                                    altered_qargs,
+                                )
+                                
+                            else:
+
+                                q = fake_quantize(
+                                    q,
+                                    scale[:, input_dim_group],
+                                    zero_point[:, input_dim_group],
+                                    altered_qargs,
                                 )
-                                grouped_indicies = g_idx[indices_to_extract].int()
-
-                                scale = scale[:, grouped_indicies]
-                                zero_point = zero_point[:, grouped_indicies]
-
-                            q = fake_quantize(
-                                q,
-                                scale[:, input_dim_group],
-                                zero_point[:, input_dim_group],
-                                altered_qargs,
-                            )
 
                 Q1[:, i] = q
                 Losses1[:, i] = (w - q) ** 2 / d**2
@@ -244,6 +263,7 @@ def compress(
 
         if actorder:
             W = W[:, invperm]
+            self.H = self.H[perm][:, perm]
 
         if isinstance(self.layer, transformers.Conv1D):
             W = W.t()