diff --git a/src/sparseml/modifiers/quantization/gptq/base.py b/src/sparseml/modifiers/quantization/gptq/base.py
index 43bc596d849..833fa284531 100644
--- a/src/sparseml/modifiers/quantization/gptq/base.py
+++ b/src/sparseml/modifiers/quantization/gptq/base.py
@@ -50,6 +50,7 @@ class GPTQModifier(Modifier):
             - LayerCompressor.revert_layer_wrappers()
 
 
+    :param actorder: Whether to use activation reordering or not
     :param sequential_update: Whether or not to update weights sequentially by layer,
         True saves on GPU memory
     :param targets: list of layer names to compress during GPTQ, or '__ALL__'
diff --git a/src/sparseml/modifiers/quantization/gptq/utils/gptq_wrapper.py b/src/sparseml/modifiers/quantization/gptq/utils/gptq_wrapper.py
index ded28b4123b..9f660b987fb 100644
--- a/src/sparseml/modifiers/quantization/gptq/utils/gptq_wrapper.py
+++ b/src/sparseml/modifiers/quantization/gptq/utils/gptq_wrapper.py
@@ -88,6 +88,7 @@ def fasterprune(
         Run pruning and quantization(if applicable) on the layer up to the target
         sparsity value.
 
+        :param actorder: Flag to apply activation reordering
         :param blocksize: Number of columns to compress in one pass
         :param percdamp: Amount of dampening to apply to H, as a fraction of the
             diagonal norm
@@ -127,6 +128,9 @@ def fasterprune(
         self.H = torch.linalg.cholesky(self.H, upper=True)
         Hinv = self.H
 
+        actorder = False
+        invperm = None
+
         # See section 3.4 of https://arxiv.org/abs/2203.07259
         for i1 in range(0, self.columns, blocksize):
             i2 = min(i1 + blocksize, self.columns)
@@ -144,6 +148,7 @@ def fasterprune(
             for i in range(count):
                 w = W1[:, i]
                 d = Hinv1[i, i]
+
                 q = w.clone()
 
                 if hasattr(self.layer, "weight_fake_quant"):
@@ -156,18 +161,42 @@ def fasterprune(
                     else:
                         q = torch.quantize_per_channel(q, scale, zero_point, 0, dtype)
                     q = torch.dequantize(q)
+
                 elif hasattr(self.layer, "quantization_scheme"):
                     quant_scheme = self.layer.quantization_scheme
+                    actorder = quant_scheme.weights.actorder
                     if quant_scheme.weights is not None:
+
+                        if actorder:
+                            perm = torch.argsort(torch.diag(self.H), descending=True)
+                            W = W[:, perm]
+                            self.H = self.H[perm][:, perm]
+                            invperm = torch.argsort(perm)
+
                         scale = self.layer.weight_scale
                         zero_point = self.layer.weight_zero_point
+
+                        group_size = quant_scheme.weights.group_size
+                        if group_size is None or group_size == -1:
+                            group_size = self.layer.weight.shape[1]
+
+                        if actorder:
+                            indices = torch.arange(self.columns, device=invperm.device)
+                            g_idx = (perm[indices] // group_size).to(dtype=torch.int32)
+                            g_idx = g_idx[invperm]
+                            self.layer.weight_g_idx.data = g_idx
+                        else:
+                            indices = torch.arange(
+                                self.columns, device=W.device, dtype=torch.int32
+                            )
+                            g_idx = indices // group_size
+
                         from compressed_tensors.quantization import QuantizationStrategy
                         from compressed_tensors.quantization.lifecycle.forward import (
                             fake_quantize,
                         )
 
                         strategy = quant_scheme.weights.strategy
-
                         if strategy == QuantizationStrategy.TENSOR:
                             q = fake_quantize(
                                 q,
@@ -189,11 +218,21 @@ def fasterprune(
                             input_dim_group = (
                                 column_idx // quant_scheme.weights.group_size
                             )
-
                             # Since we're only applying quantization to a slice, this
                             # ends up being a channelwise application
                             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_idze
+                                indices_to_extract = torch.arange(
+                                    0, g_idx.shape[0], group_size
+                                )
+                                scale = scale[:, g_idx[indices_to_extract]]
+                                zero_point = zero_point[:, g_idx[indices_to_extract]]
+
                             q = fake_quantize(
                                 q,
                                 scale[:, input_dim_group],
@@ -224,6 +263,9 @@ def fasterprune(
         _LOGGER.info("time %.2f" % (time.time() - tick))
         _LOGGER.info("error %.2f" % torch.sum(Losses).item())
 
+        if actorder:
+            W = W[:, invperm]
+
         if isinstance(self.layer, transformers.Conv1D):
             W = W.t()
         W = W.reshape(final_shape).to(final_dtype)
diff --git a/src/sparseml/modifiers/utils/layer_compressor.py b/src/sparseml/modifiers/utils/layer_compressor.py
index e5a36f77278..eb0b51cf269 100644
--- a/src/sparseml/modifiers/utils/layer_compressor.py
+++ b/src/sparseml/modifiers/utils/layer_compressor.py
@@ -134,6 +134,8 @@ def revert_layer_wrappers(self):
     def compress(self):
         """
         Apply compression to each wrapped submodule in the layer
+
+        :param: actorder: flag to apply activation reordering
         """
 
         @torch.no_grad()