diff --git a/src/brevitas_examples/common/generative/quantize.py b/src/brevitas_examples/common/generative/quantize.py
index 21cb46a19..31ab57361 100644
--- a/src/brevitas_examples/common/generative/quantize.py
+++ b/src/brevitas_examples/common/generative/quantize.py
@@ -141,7 +141,8 @@ def quantize_model(
         input_quant_granularity=None,
         input_group_size=None,
         quantize_input_zero_point=False,
-        quantize_embedding=False):
+        quantize_embedding=False,
+        device=None):
     """
     Replace float layers with quant layers in the target model
     """
@@ -208,7 +209,8 @@ def quantize_model(
             **{
                 'bit_width': input_bit_width,
                 'quantize_zero_point': quantize_input_zero_point,
-                'dtype': dtype,},
+                'dtype': dtype,
+                'device': device},
             **input_float_format)
         if input_scale_type == 'dynamic':
             if input_quant_granularity == 'per_row':
@@ -224,7 +226,8 @@ def quantize_model(
             **{
                 'bit_width': input_bit_width,
                 'quantize_zero_point': quantize_input_zero_point,
-                'dtype': dtype},
+                'dtype': dtype,
+                'device': device},
             **input_float_format)
         if input_scale_type == 'dynamic':
             if input_quant_granularity == 'per_tensor':
@@ -263,7 +266,8 @@ def quantize_model(
             **{
                 'bit_width': input_bit_width,
                 'quantize_zero_point': quantize_input_zero_point,
-                'dtype': dtype},
+                'dtype': dtype,
+                'device': device},
             **input_float_format)
         if input_scale_type == 'dynamic':
             if input_quant_granularity == 'per_row':
@@ -279,8 +283,12 @@ def quantize_model(
                         'group_dim': -1, 'group_size': input_group_size})
 
     quant_linear_kwargs = {
-        'input_quant': linear_input_quant, 'weight_quant': weight_quant, 'dtype': dtype}
-    quant_conv_kwargs = {'input_quant': input_quant, 'weight_quant': weight_quant, 'dtype': dtype}
+        'input_quant': linear_input_quant,
+        'weight_quant': weight_quant,
+        'dtype': dtype,
+        'device': device}
+    quant_conv_kwargs = {
+        'input_quant': input_quant, 'weight_quant': weight_quant, 'dtype': dtype, 'device': device}
 
     quant_mha_kwargs = {
         'in_proj_input_quant': input_quant,
@@ -300,7 +308,8 @@ def quantize_model(
         # activation equalization requires packed_in_proj
         # since it supports only self-attention
         'packed_in_proj': True,
-        'dtype': dtype}
+        'dtype': dtype,
+        'device': device}
 
     layer_map = {
         nn.Linear: (qnn.QuantLinear, quant_linear_kwargs),
@@ -311,7 +320,7 @@ def quantize_model(
         nn.MultiheadAttention: (qnn.QuantMultiheadAttention, quant_mha_kwargs)}
 
     if quantize_embedding:
-        quant_embedding_kwargs = {'weight_quant': weight_quant, 'dtype': dtype}
+        quant_embedding_kwargs = {'weight_quant': weight_quant, 'dtype': dtype, 'device': device}
         layer_map[nn.Embedding] = (qnn.QuantEmbedding, quant_embedding_kwargs)
 
     model = layerwise_quantize(