diff --git a/tico/quantization/algorithm/fpi_gptq/fpi_gptq.py b/tico/quantization/algorithm/fpi_gptq/fpi_gptq.py
index cdd99ef7..3ca13443 100644
--- a/tico/quantization/algorithm/fpi_gptq/fpi_gptq.py
+++ b/tico/quantization/algorithm/fpi_gptq/fpi_gptq.py
@@ -32,29 +32,7 @@
 )
 
 from tico.quantization.algorithm.gptq.quant import quantize, Quantizer
-
-
-def iterate_GPTQ(scale, zero, maxq, W, Hinv, max_num_of_iters=50):
-
-    cur_weights = W.clone()
-    mults = torch.pow(torch.diag(Hinv), -1)
-    Hinv_U = torch.triu(Hinv, diagonal=1)
-
-    init_weights = W.clone()
-    for _ in range(max_num_of_iters):
-        cur_Q = quantize(cur_weights, scale, zero, maxq)
-
-        d_W = torch.mul((cur_weights - cur_Q), mults)
-        cur_weights = init_weights - torch.matmul(d_W, Hinv_U)
-        del d_W, cur_Q
-        d_W = cur_Q = None
-
-    del init_weights
-    init_weights = None
-
-    cur_Q = quantize(cur_weights, scale, zero, maxq)
-
-    return cur_Q, cur_weights
+from tico.quantization.algorithm.fpi_gptq.util import iterate_GPTQ, quantize
 
 
 class FPI_GPTQ:
diff --git a/tico/quantization/algorithm/fpi_gptq/util.py b/tico/quantization/algorithm/fpi_gptq/util.py
new file mode 100644
index 00000000..e2c9ad7d
--- /dev/null
+++ b/tico/quantization/algorithm/fpi_gptq/util.py
@@ -0,0 +1,56 @@
+# Copyright IST-DASLab. 2025. (commit: 2d65066). GitHub repository.
+# Retrieved from https://github.com/IST-DASLab/gptq. Licensed under the
+# Apache License 2.0.
+
+# Copyright (c) 2026 Samsung Electronics Co., Ltd. All Rights Reserved
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# https://github.com/IST-DASLab/gptq/blob/2d65066/quant.py
+
+import torch
+
+
+def quantize(x, scale, zero, maxq):
+    if maxq < 0:
+        return (x > scale / 2).float() * scale + (x < zero / 2).float() * zero
+    q = torch.clamp(torch.round(x / scale) + zero, 0, maxq)
+    return scale * (q - zero)
+
+
+def iterate_GPTQ(scale, zero, maxq, W, Hinv, max_num_of_iters=50):
+
+    cur_weights = W.clone()
+    mults = torch.pow(torch.diag(Hinv), -1)
+    Hinv_U = torch.triu(Hinv, diagonal=1)
+
+    init_weights = W.clone()
+    for _ in range(max_num_of_iters):
+        cur_Q = quantize(cur_weights, scale, zero, maxq)
+
+        d_W = torch.mul((cur_weights - cur_Q), mults)
+        cur_weights = init_weights - torch.matmul(d_W, Hinv_U)
+        del d_W, cur_Q
+        d_W = cur_Q = None
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+
+    del init_weights
+    init_weights = None
+
+    if torch.cuda.is_available():
+        torch.cuda.empty_cache()
+
+    cur_Q = quantize(cur_weights, scale, zero, maxq)
+
+    return cur_Q, cur_weights
diff --git a/tico/quantization/algorithm/gptq/README.md b/tico/quantization/algorithm/gptq/README.md
index ae443a13..db99dcf7 100644
--- a/tico/quantization/algorithm/gptq/README.md
+++ b/tico/quantization/algorithm/gptq/README.md
@@ -52,6 +52,9 @@ applied after _convert()_, the effectiveness of GPTQ may be diminished.
 There are two options :
 1. `mse`- vanilla `mse`. Produce quantization parameters for GPTQ quantizer (`min`\`max`) which minimize mean squared error of quantization. $MSE_{MIN, MAX}(W) = argmin_{min, max}||W-Q_{min, max}(W)||^2$.
 2. `smse` - sensitivity-based `mse`. Use sensitivity of some global feature (e.g. float model logits) to parameters change to minimize global effect of quantization. $SMSE_{MIN, MAX}(W) = argmin_{min, max}|(W-Q_{min, max}(W))^2*Sensitivity(W)|$. So we try to keep `important` parameters unchanged, while quantizing `unimportant` parameters more aggressively.
+3. `smse_for_gptq` - `smse` adjusted for GPTQ. GPTQ modifies the matrix during the quantization process, so the most accurate method would consist in finding a quantizer that yields the smallest quantization error after the GPTQ method has been applied $SMSE\_FOR\_GPTQ_{MIN, MAX}(W) = argmin_{min, max}|(W-Q_{min, max}(W_{GPTQ}))^2*Sensitivity(W)|$. Since this would be quite computationally expensive, we can use an accelerated approximate GPTQ method — FPI_GPTQ $SMSE\_FOR\_GPTQ_{MIN, MAX}(W) = argmin_{min, max}|(W-Q_{min, max}(W_{FPI\_GPTQ}))^2*Sensitivity(W)|$. This is slower than `mse`/`smse` but can provide better accuracy.
+
+
 
 You can turn this feature `on`/`off` by using `mse` parameter of `GPTQConfig`:
 ```
diff --git a/tico/quantization/algorithm/gptq/gptq.py b/tico/quantization/algorithm/gptq/gptq.py
index ab01721f..17caa85c 100644
--- a/tico/quantization/algorithm/gptq/gptq.py
+++ b/tico/quantization/algorithm/gptq/gptq.py
@@ -361,6 +361,8 @@ def fasterquant(
         H = torch.linalg.cholesky(H, upper=True).float()
         Hinv = H
 
+        self.quantizer.update(W, Hinv, perm)
+
         assert isinstance(Hinv, torch.Tensor)
         for i1 in range(0, self.columns, blocksize):
             i2 = min(i1 + blocksize, self.columns)
diff --git a/tico/quantization/algorithm/gptq/quant.py b/tico/quantization/algorithm/gptq/quant.py
index 98e7731d..fb92817b 100644
--- a/tico/quantization/algorithm/gptq/quant.py
+++ b/tico/quantization/algorithm/gptq/quant.py
@@ -21,6 +21,8 @@
 import torch
 import torch.nn as nn
 
+from tico.quantization.algorithm.fpi_gptq.util import iterate_GPTQ
+
 
 def quantize(x, scale, zero, maxq):
     if maxq < 0:
@@ -59,10 +61,16 @@ def configure(
         if trits:
             self.maxq = torch.tensor(-1)
 
-    def find_params(self, x, weight=False):
-        dev = x.device
-        self.maxq = self.maxq.to(dev)
+    def _prepare_tensor(self, x, weight=False):
+        """Prepare tensor for quantization by flattening according to per-channel setting.
+
+        Args:
+            x: Input tensor to prepare
+            weight: Whether the tensor is a weight (affects flattening for activations)
 
+        Returns:
+            Tuple of (prepared tensor, original shape)
+        """
         shape = x.shape
         if self.perchannel:
             if weight:
@@ -77,7 +85,18 @@ def find_params(self, x, weight=False):
                     x = x.t()
         else:
             x = x.flatten().unsqueeze(0)
+        return x, shape
 
+    def _compute_scale_zero_bounds(self, x):
+        """Compute scale and zero bounds from tensor values.
+
+        Args:
+            x: Prepared tensor (flattened according to per-channel setting)
+
+        Returns:
+            Tuple of (scale, zero, xmin, xmax) computed from tensor bounds
+        """
+        dev = x.device
         tmp = torch.zeros(x.shape[0], device=dev)
         xmin = torch.minimum(x.min(1)[0], tmp)
         xmax = torch.maximum(x.max(1)[0], tmp)
@@ -92,65 +111,204 @@ def find_params(self, x, weight=False):
         xmax[tmp] = +1
 
         if self.maxq < 0:
-            self.scale = xmax
-            self.zero = xmin
+            scale = xmax
+            zero = xmin
         else:
-            self.scale = (xmax - xmin) / self.maxq
+            scale = (xmax - xmin) / self.maxq
             if self.sym:
-                self.zero = torch.full_like(self.scale, (self.maxq + 1) / 2)  # type: ignore[arg-type]
+                zero = torch.full_like(scale, (self.maxq + 1) / 2)  # type: ignore[arg-type]
             else:
-                self.zero = torch.round(-xmin / self.scale)
-
-        if self.mse is not None:
-            best = torch.full([x.shape[0]], float("inf"), device=dev)
-            for i in range(int(self.maxshrink * self.grid)):
-                p = 1 - i / self.grid
-                xmin1 = p * xmin
-                xmax1 = p * xmax
-                scale1 = (xmax1 - xmin1) / self.maxq
-                zero1 = torch.round(-xmin1 / scale1) if not self.sym else self.zero
-                q = quantize(x, scale1.unsqueeze(1), zero1.unsqueeze(1), self.maxq)
-                q -= x
-                q.abs_()
-                if self.mse == "smse":  # senstitivity weighted mse
-                    # in case senstitivity is a second order derivatives of some global loss
-                    # (q**2) * self.sensitivity is just a global loss change due to quantization.
-                    q = (q**2) * self.sensitivity.to(
-                        q.device
-                    )  # estimate global target change
-                else:
-                    assert self.mse == "mse"
-                    q.pow_(self.norm)
-                err = torch.sum(q, 1)
-                tmp = err < best
-                if torch.any(tmp):
-                    best[tmp] = err[tmp]
-                    self.scale[tmp] = scale1[tmp]
-                    self.zero[tmp] = zero1[tmp]
-        if not self.perchannel:
-            if weight:
-                tmp = shape[0]
-            else:
-                tmp = shape[1] if len(shape) != 3 else shape[2]
-            assert isinstance(tmp, int)
-            self.scale = self.scale.repeat(tmp)
-            self.zero = self.zero.repeat(tmp)
+                zero = torch.round(-xmin / scale)
+
+        return scale, zero, xmin, xmax
 
+    def _reshape_scale_zero(self, shape, weight=False):
+        """Reshape scale and zero tensors according to the original tensor shape.
+
+        Args:
+            shape: Original tensor shape before preparation
+            weight: Whether the tensor is a weight (affects reshape for activations)
+        """
         if weight:
             shape = [-1] + [1] * (len(shape) - 1)
-            self.scale = self.scale.reshape(shape)
-            self.zero = self.zero.reshape(shape)
+            self.scale = self.scale.reshape(shape)  # type: ignore[has-type]
+            self.zero = self.zero.reshape(shape)  # type: ignore[has-type]
             return
+
         if len(shape) == 4:
             self.scale = self.scale.reshape((1, -1, 1, 1))
             self.zero = self.zero.reshape((1, -1, 1, 1))
-        if len(shape) == 3:
+        elif len(shape) == 3:
             self.scale = self.scale.reshape((1, 1, -1))
             self.zero = self.zero.reshape((1, 1, -1))
-        if len(shape) == 2:
+        elif len(shape) == 2:
             self.scale = self.scale.unsqueeze(0)
             self.zero = self.zero.unsqueeze(0)
 
+    def _expand_for_per_tensor(self, shape, weight=False):
+        """Expand scale and zero for per-tensor quantization.
+
+        Args:
+            shape: Original tensor shape before preparation
+            weight: Whether the tensor is a weight
+        """
+        if not self.perchannel:
+            if weight:
+                tmp = shape[0]
+            else:
+                tmp = shape[1] if len(shape) != 3 else shape[2]
+            assert isinstance(tmp, int)
+            self.scale = self.scale.repeat(tmp)
+            self.zero = self.zero.repeat(tmp)
+
+    def find_params(self, x, weight=False):
+        dev = x.device
+        self.maxq = self.maxq.to(dev)
+
+        shape = x.shape
+        x, shape = self._prepare_tensor(x, weight)
+
+        self.scale, self.zero, xmin, xmax = self._compute_scale_zero_bounds(x)
+
+        if (
+            self.mse is not None
+            and self.mse != "smse_for_gptq"
+            and self.mse != "mse_for_gptq"
+        ):
+            self._optimize_mse(x, xmin, xmax)
+
+        self._expand_for_per_tensor(shape, weight)
+        self._reshape_scale_zero(shape, weight)
+
+    def _compute_shrink_params(self, p, xmin, xmax):
+        """Compute scale and zero for a shrink factor p.
+
+        Args:
+            p: Shrink factor (1 - i / grid)
+            xmin: Minimum values per channel
+            xmax: Maximum values per channel
+
+        Returns:
+            Tuple of (scale1, zero1) for the given shrink factor
+        """
+        xmin1 = p * xmin
+        xmax1 = p * xmax
+        scale1 = (xmax1 - xmin1) / self.maxq
+        zero1 = torch.round(-xmin1 / scale1) if not self.sym else self.zero
+        return scale1, zero1
+
+    def _update_best_params(self, best, err, scale1, zero1):
+        """Update best parameters if current error is lower.
+
+        Args:
+            best: Current best error values
+            err: Current iteration error values
+            scale1: Current iteration scale values
+            zero1: Current iteration zero values
+
+        Returns:
+            Updated best error values
+        """
+        tmp = err < best
+        if torch.any(tmp):
+            best[tmp] = err[tmp]
+            self.scale[tmp] = scale1[tmp]
+            self.zero[tmp] = zero1[tmp]
+        return best
+
+    def _grid_search(self, x, xmin, xmax, compute_error_fn):
+        """Common grid search loop for MSE optimization.
+
+        Args:
+            x: Prepared tensor
+            xmin: Minimum values per channel
+            xmax: Maximum values per channel
+            compute_error_fn: Function that takes (x, scale1, zero1) and returns error tensor
+        """
+        dev = x.device
+        best = torch.full([x.shape[0]], float("inf"), device=dev)
+        for i in range(int(self.maxshrink * self.grid)):
+            p = 1 - i / self.grid
+            scale1, zero1 = self._compute_shrink_params(p, xmin, xmax)
+            err = compute_error_fn(x, scale1, zero1)
+            best = self._update_best_params(best, err, scale1, zero1)
+
+    def _optimize_mse(self, x, xmin, xmax):
+        """Optimize scale and zero using MSE-based grid search.
+
+        Args:
+            x: Prepared tensor
+            xmin: Minimum values per channel
+            xmax: Maximum values per channel
+        """
+
+        def compute_error(x, scale1, zero1):
+            q = quantize(x, scale1.unsqueeze(1), zero1.unsqueeze(1), self.maxq)
+            q -= x
+            q.abs_()
+            if self.mse == "smse":  # sensitivity weighted mse
+                # in case sensitivity is a second order derivatives of some global loss
+                # (q**2) * self.sensitivity is just a global loss change due to quantization.
+                q = (q**2) * self.sensitivity.to(
+                    q.device
+                )  # estimate global target change
+            else:
+                assert self.mse == "mse"
+                q.pow_(self.norm)
+            return torch.sum(q, 1)
+
+        self._grid_search(x, xmin, xmax, compute_error)
+
+    def update(self, x, Hinv, perm):
+        if self.mse is None or self.mse != "smse_for_gptq":
+            return
+
+        shape = x.shape
+        x, shape = self._prepare_tensor(x, weight=True)
+
+        self.scale, self.zero, xmin, xmax = self._compute_scale_zero_bounds(x)
+
+        sensitivity = None
+        if self.sensitivity is not None:
+            sensitivity = self.sensitivity.to(Hinv.dtype).to(x.device)
+            if perm is not None:
+                sensitivity = sensitivity[:, perm.to(x.device)]
+
+        self._optimize_mse_for_gptq(x, Hinv, sensitivity, xmin, xmax)
+
+        self._reshape_scale_zero(shape, weight=True)
+
+        del sensitivity
+        sensitivity = None
+
+    def _optimize_mse_for_gptq(self, x, Hinv, sensitivity, xmin, xmax):
+        """Optimize scale and zero using GPTQ-aware MSE grid search.
+
+        Args:
+            x: Prepared tensor
+            Hinv: Inverse Hessian matrix
+            sensitivity: Sensitivity tensor for weighted MSE
+            xmin: Minimum values per channel
+            xmax: Maximum values per channel
+        """
+        num_of_iters = 15
+
+        def compute_error(x, scale1, zero1):
+            q, _ = iterate_GPTQ(
+                scale1.unsqueeze(1),
+                zero1.unsqueeze(1),
+                self.maxq,
+                x,
+                Hinv,
+                max_num_of_iters=num_of_iters,
+            )
+            assert sensitivity is not None
+            assert self.mse == "smse_for_gptq"
+            err = ((q - x) ** 2) * sensitivity.to(q.device)
+            return torch.sum(err, 1)
+
+        self._grid_search(x, xmin, xmax, compute_error)
+
     def quantize(self, x):
         if self.ready():
             return quantize(x, self.scale, self.zero, self.maxq)
diff --git a/tico/quantization/algorithm/gptq/quantizer.py b/tico/quantization/algorithm/gptq/quantizer.py
index ba3d05b5..5b7025fb 100644
--- a/tico/quantization/algorithm/gptq/quantizer.py
+++ b/tico/quantization/algorithm/gptq/quantizer.py
@@ -473,6 +473,12 @@ def _hook(_, inp, out):
         handles = [layer.register_forward_hook(add_batch())]
 
         # Run layer forward over all cached batches to build Hessian/statistics
+        old_device = device
+        model = model.to("cpu")
+        model.lm_head = model.lm_head.to(old_device)
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+
         device = next(layer.parameters()).device  # in case lm_head is located on cpu
         for batch_idx in tqdm(
             range(batch_num),
@@ -502,3 +508,4 @@ def _hook(_, inp, out):
         )
         quantizers[f"lm_head"] = gptq.quantizer
         gptq.free()
+        model = model.to(old_device)
diff --git a/tico/quantization/algorithm/gptq/utils.py b/tico/quantization/algorithm/gptq/utils.py
index 1f4d0321..b1af9980 100644
--- a/tico/quantization/algorithm/gptq/utils.py
+++ b/tico/quantization/algorithm/gptq/utils.py
@@ -163,7 +163,11 @@ def compute_sensitivity_info(self):
         if self.show_progress is True:
             print("Calibrating sensitivity")
         for inputs, targets in tqdm.tqdm(data_loader, disable=not self.show_progress):
-            model.zero_grad()
+            model.zero_grad(set_to_none=True)
+            if model.device.type != "cpu":
+                torch.cuda.empty_cache()
+                torch.cuda.synchronize()
+
             if isinstance(inputs, torch.Tensor):
                 inp_ids = inputs.squeeze(0)  # remove redundant batch dimension
                 logits = model(inp_ids.to(model.device)).logits
@@ -219,6 +223,11 @@ def compute_sensitivity_info(self):
         for name in modules_to_process:
             sensitivity[name] /= len(data_loader)
 
+        model.zero_grad(set_to_none=True)
+        if model.device.type != "cpu":
+            torch.cuda.synchronize()
+            torch.cuda.empty_cache()
+
         model = model.to(dtype)
 
         return sensitivity
diff --git a/tico/quantization/wrapq/examples/quantize_full_qmodel_with_gptq.py b/tico/quantization/wrapq/examples/quantize_full_qmodel_with_gptq.py
index 4a55c978..eaab7c35 100644
--- a/tico/quantization/wrapq/examples/quantize_full_qmodel_with_gptq.py
+++ b/tico/quantization/wrapq/examples/quantize_full_qmodel_with_gptq.py
@@ -218,8 +218,8 @@ def parse_args():
         "--gptq_mse",
         type=str,
         default=None,
-        choices=["mse", "smse"],
-        help="Whether and how to use mse in gptq (none/mse/smse/)",
+        choices=["mse", "smse", "smse_for_gptq"],
+        help="Whether and how to use mse in gptq (none/mse/smse/smse_for_gptq)",
     )
     parser.add_argument(
         "--max_seq_len",
@@ -390,6 +390,52 @@ def _print_sample(title, items):
         _print_sample("unused GPTQ entries", unused)
 
 
+def evaluate_ppl_of_model_on_dataset(model, dataset, device):
+    if hasattr(model, "device") and model.device.type != device.type:
+        if hasattr(model, "to"):
+            model.to(device)
+    nlls = []
+    with torch.no_grad():
+        for batch in tqdm.tqdm(dataset):
+            if isinstance(batch, torch.Tensor):
+                batch = batch.to(device)
+                output = model(
+                    batch.to(device),
+                )
+            else:
+                raise RuntimeError("Unknown input in ppl_eval_on_dataset")
+
+            if hasattr(output, "logits"):
+                lm_logits = output.logits
+            elif len(output) > 1:
+                lm_logits = torch.tensor(output[0])
+            else:
+                lm_logits = torch.tensor(output)
+
+            if torch.isfinite(lm_logits).all():
+                shift_logits = lm_logits[:, :-1, :].contiguous()
+                if isinstance(batch, torch.Tensor):
+                    shift_labels = batch[:, 1:].contiguous()
+                else:
+                    assert isinstance(batch, tuple)
+                    shift_labels = batch[0][:, 1:].contiguous()
+                loss_fct = torch.nn.CrossEntropyLoss(reduction="none")
+                loss = loss_fct(
+                    shift_logits.reshape(-1, shift_logits.size(-1)),
+                    shift_labels.view(-1),
+                )
+                nlls.append(loss)
+                del shift_logits, shift_labels
+                shift_logits = shift_labels = None  # type: ignore[assignment]
+
+            del batch, lm_logits, output
+            lm_logits = output = batch = None  # noqa: F841
+            torch.cuda.empty_cache()
+
+    ppl = np.exp(torch.cat(nlls, dim=-1).mean().item())
+    return ppl
+
+
 # -------------------------------------------------------------------------
 # Helper — clear gptq quantizers after injection
 # -------------------------------------------------------------------------
@@ -1285,9 +1331,9 @@ def build_calibration_inputs(
 
 def compute_or_load_sensitivity(model, calib_inputs, args):
     """
-    Load or compute sensitivity information for SMSE GPTQ.
+    Load or compute sensitivity information for sensitivity-based GPTQ.
     """
-    if args.gptq_mse != "smse":
+    if args.gptq_mse != "smse" and args.gptq_mse != "smse_for_gptq":
         return None
 
     if args.sensitivity_path is not None:
@@ -1414,6 +1460,12 @@ def main():
     evaluate_original_model(model, tokenizer, dataset_test, args, device)
 
     calib_inputs = build_calibration_inputs(model, tokenizer, args, device)
+    train_ppl_ioqdtype = evaluate_ppl_of_model_on_dataset(
+        model, calib_inputs, device=device
+    )
+    print("\n┌── Wikitext-2 train perplexity ─────────────")
+    print(f"│ FP32 : {train_ppl_ioqdtype:8.2f}")
+    print("└───────────────────────────────────────────")
 
     model = apply_spinquant(model, args)
     model = apply_cle(model, args)
@@ -1422,6 +1474,14 @@ def main():
     q_m = quantize_using_PTQ(model, calib_inputs, args)
 
     evaluate(q_m, tokenizer, dataset_test, args)
+
+    train_ppl_ioqdtype = evaluate_ppl_of_model_on_dataset(
+        q_m, calib_inputs, device=device
+    )
+    print("\n┌── Wikitext-2 train perplexity ─────────────")
+    print(f"│ int16 : {train_ppl_ioqdtype:8.2f}")
+    print("└───────────────────────────────────────────")
+
     save_requested_artifacts(q_m, tokenizer, calib_inputs, args)