diff --git a/models/demos/ttnn_resnet/tt/ttnn_functional_resnet50_new_conv_api.py b/models/demos/ttnn_resnet/tt/ttnn_functional_resnet50_new_conv_api.py
index a8944b654c37..7a2f400d4377 100644
--- a/models/demos/ttnn_resnet/tt/ttnn_functional_resnet50_new_conv_api.py
+++ b/models/demos/ttnn_resnet/tt/ttnn_functional_resnet50_new_conv_api.py
@@ -231,6 +231,48 @@ def __call__(
         # conv1 is 1x1 conv
         logger.debug(f"Running conv1")
         module_input_height = input_height
+        conv_config = ttnn.Conv2dConfig(
+            dtype=self.model_config["ACTIVATIONS_DTYPE"],
+            weights_dtype=self.model_config["WEIGHTS_DTYPE"],
+            activation="relu",
+            shard_layout=ttnn.TensorMemoryLayout.HEIGHT_SHARDED
+            if height_sharding
+            else ttnn.TensorMemoryLayout.BLOCK_SHARDED,
+            reshard_if_not_optimal=reshard_if_not_optimal,
+            transpose_shards=transpose_shards,
+        )
+        conv_kwargs = {
+            "input_layout": x.get_layout(),
+            "in_channels": self.conv1_input_channels,
+            "out_channels": self.conv1_output_channels,
+            "batch_size": batch_size,
+            "input_height": input_height,
+            "input_width": input_width,
+            "kernel_size": (1, 1),
+            "stride": (1, 1),
+            "padding": (0, 0),
+            "dilation": (1, 1),
+            "groups": 1,
+            "device": device,
+            "conv_config": conv_config,
+        }
+
+        if not ttnn.is_tensor_storage_on_device(self.conv1_weight_tensor):
+            print("preparing conv1 weights")
+            self.conv1_weight_tensor = ttnn.prepare_conv_weights(
+                weight_tensor=self.conv1_weight_tensor,
+                weights_format="OIHW",
+                input_memory_config=x.memory_config(),
+                **conv_kwargs,
+            )
+            self.conv1_bias_tensor = ttnn.prepare_conv_bias(
+                bias_tensor=self.conv1_bias_tensor,
+                input_memory_config=x.memory_config(),
+                **conv_kwargs if self.conv1_bias_tensor is not None else None,
+            )
+            self.conv1_weight_tensor = ttnn.to_device(self.conv1_weight_tensor, device)
+            self.conv1_bias_tensor = ttnn.to_device(self.conv1_bias_tensor, device) if self.conv1_bias_tensor else None
+
         out, [input_height, input_width], [self.conv1_weight_tensor, self.conv1_bias_tensor] = ttnn.conv2d(
             input_tensor=x,
             weight_tensor=self.conv1_weight_tensor,
@@ -244,16 +286,7 @@ def __call__(
             batch_size=batch_size,
             input_height=input_height,
             input_width=input_width,
-            conv_config=ttnn.Conv2dConfig(
-                dtype=self.model_config["ACTIVATIONS_DTYPE"],
-                weights_dtype=self.model_config["WEIGHTS_DTYPE"],
-                activation="relu",
-                shard_layout=ttnn.TensorMemoryLayout.HEIGHT_SHARDED
-                if height_sharding
-                else ttnn.TensorMemoryLayout.BLOCK_SHARDED,
-                reshard_if_not_optimal=reshard_if_not_optimal,
-                transpose_shards=transpose_shards,
-            ),
+            conv_config=conv_config,
             compute_config=ttnn.init_device_compute_kernel_config(
                 device.arch(),
                 math_fidelity=self.model_config["MATH_FIDELITY"],
@@ -317,6 +350,55 @@ def __call__(
 
         reallocate_halo_output = batch_size == 20
         logger.debug(f"Running conv2")
+        conv_config = ttnn.Conv2dConfig(
+            dtype=self.model_config["ACTIVATIONS_DTYPE"],
+            weights_dtype=self.model_config["WEIGHTS_DTYPE"],
+            activation="relu",
+            deallocate_activation=True,
+            reallocate_halo_output=reallocate_halo_output,
+            act_block_h_override=act_block_h_override,
+            shard_layout=ttnn.TensorMemoryLayout.HEIGHT_SHARDED
+            if height_sharding
+            else ttnn.TensorMemoryLayout.BLOCK_SHARDED,
+            reshard_if_not_optimal=reshard_if_not_optimal,
+            transpose_shards=transpose_shards,
+            enable_act_double_buffer=enable_act_double_buffer,
+            enable_weights_double_buffer=True,
+            enable_split_reader=enable_split_reader,
+            enable_subblock_padding=enable_subblock_padding,
+        )
+        conv_kwargs = {
+            "input_layout": x.get_layout(),
+            "in_channels": self.conv2_input_channels,
+            "out_channels": self.conv2_output_channels,
+            "batch_size": batch_size,
+            "input_height": input_height,
+            "input_width": input_width,
+            "kernel_size": (3, 3),
+            "stride": (self.stride, self.stride),
+            "padding": (1, 1),
+            "dilation": (1, 1),
+            "groups": 1,
+            "device": device,
+            "conv_config": conv_config,
+        }
+
+        if not ttnn.is_tensor_storage_on_device(self.conv2_weight_tensor):
+            print("Preparing conv2 weights")
+            self.conv2_weight_tensor = ttnn.prepare_conv_weights(
+                weight_tensor=self.conv2_weight_tensor,
+                weights_format="OIHW",
+                input_memory_config=x.memory_config(),
+                **conv_kwargs,
+            )
+            self.conv2_bias_tensor = ttnn.prepare_conv_bias(
+                bias_tensor=self.conv2_bias_tensor,
+                input_memory_config=x.memory_config(),
+                **conv_kwargs if self.conv2_bias_tensor is not None else None,
+            )
+            self.conv2_weight_tensor = ttnn.to_device(self.conv2_weight_tensor, device)
+            self.conv2_bias_tensor = ttnn.to_device(self.conv2_bias_tensor, device) if self.conv2_bias_tensor else None
+
         out, [input_height, input_width], [self.conv2_weight_tensor, self.conv2_bias_tensor] = ttnn.conv2d(
             input_tensor=out,
             weight_tensor=self.conv2_weight_tensor,
@@ -330,23 +412,7 @@ def __call__(
             batch_size=batch_size,
             input_height=input_height,
             input_width=input_width,
-            conv_config=ttnn.Conv2dConfig(
-                dtype=self.model_config["ACTIVATIONS_DTYPE"],
-                weights_dtype=self.model_config["WEIGHTS_DTYPE"],
-                activation="relu",
-                deallocate_activation=True,
-                reallocate_halo_output=reallocate_halo_output,
-                act_block_h_override=act_block_h_override,
-                shard_layout=ttnn.TensorMemoryLayout.HEIGHT_SHARDED
-                if height_sharding
-                else ttnn.TensorMemoryLayout.BLOCK_SHARDED,
-                reshard_if_not_optimal=reshard_if_not_optimal,
-                transpose_shards=transpose_shards,
-                enable_act_double_buffer=enable_act_double_buffer,
-                enable_weights_double_buffer=True,
-                enable_split_reader=enable_split_reader,
-                enable_subblock_padding=enable_subblock_padding,
-            ),
+            conv_config=conv_config,
             compute_config=ttnn.init_device_compute_kernel_config(
                 device.arch(),
                 math_fidelity=self.model_config["MATH_FIDELITY"],
@@ -373,6 +439,47 @@ def __call__(
 
         # conv3 is 1x1 conv
         logger.debug(f"Running conv3")
+        conv_config = ttnn.Conv2dConfig(
+            dtype=self.model_config["ACTIVATIONS_DTYPE"],
+            weights_dtype=self.model_config["WEIGHTS_DTYPE"],
+            shard_layout=ttnn.TensorMemoryLayout.HEIGHT_SHARDED
+            if height_sharding
+            else ttnn.TensorMemoryLayout.BLOCK_SHARDED,
+            reshard_if_not_optimal=reshard_if_not_optimal,
+            transpose_shards=transpose_shards,
+        )
+        conv_kwargs = {
+            "input_layout": x.get_layout(),
+            "in_channels": self.conv3_input_channels,
+            "out_channels": self.conv3_output_channels,
+            "batch_size": batch_size,
+            "input_height": input_height,
+            "input_width": input_width,
+            "kernel_size": (1, 1),
+            "stride": (1, 1),
+            "padding": (0, 0),
+            "dilation": (1, 1),
+            "groups": 1,
+            "device": device,
+            "conv_config": conv_config,
+        }
+
+        if not ttnn.is_tensor_storage_on_device(self.conv3_weight_tensor):
+            print("Preparing conv3 weights")
+            self.conv3_weight_tensor = ttnn.prepare_conv_weights(
+                weight_tensor=self.conv3_weight_tensor,
+                weights_format="OIHW",
+                input_memory_config=x.memory_config(),
+                **conv_kwargs,
+            )
+            self.conv3_bias_tensor = ttnn.prepare_conv_bias(
+                bias_tensor=self.conv3_bias_tensor,
+                input_memory_config=x.memory_config(),
+                **conv_kwargs if self.conv2_bias_tensor is not None else None,
+            )
+            self.conv3_weight_tensor = ttnn.to_device(self.conv3_weight_tensor, device)
+            self.conv3_bias_tensor = ttnn.to_device(self.conv3_bias_tensor, device) if self.conv3_bias_tensor else None
+
         out, [self.conv3_weight_tensor, self.conv3_bias_tensor] = ttnn.conv2d(
             input_tensor=out,
             weight_tensor=self.conv3_weight_tensor,
@@ -386,15 +493,7 @@ def __call__(
             batch_size=batch_size,
             input_height=input_height,
             input_width=input_width,
-            conv_config=ttnn.Conv2dConfig(
-                dtype=self.model_config["ACTIVATIONS_DTYPE"],
-                weights_dtype=self.model_config["WEIGHTS_DTYPE"],
-                shard_layout=ttnn.TensorMemoryLayout.HEIGHT_SHARDED
-                if height_sharding
-                else ttnn.TensorMemoryLayout.BLOCK_SHARDED,
-                reshard_if_not_optimal=reshard_if_not_optimal,
-                transpose_shards=transpose_shards,
-            ),
+            conv_config=conv_config,
             compute_config=ttnn.init_device_compute_kernel_config(
                 device.arch(),
                 math_fidelity=self.model_config["MATH_FIDELITY"],
@@ -742,6 +841,38 @@ def run(self, input_tensor, device, ops_parallel_config, conv_op_cache={}) -> tt
         logger.debug(f"==== first conv")
 
         # first conv
+        conv_kwargs = {
+            "input_layout": fold_output_tensor.get_layout(),
+            "in_channels": self.conv1_input_channels,
+            "out_channels": self.conv1_output_channels,
+            "batch_size": self.batch_size,
+            "input_height": self.conv1_input_height,
+            "input_width": self.conv1_input_width,
+            "kernel_size": self.conv1_kernel_size,
+            "stride": self.conv1_stride,
+            "padding": self.conv1_padding,
+            "dilation": (1, 1),
+            "groups": 1,
+            "device": device,
+            "conv_config": self.conv1_config,
+        }
+
+        if not ttnn.is_tensor_storage_on_device(self.conv1_weight_tensor):
+            self.conv1_weight_tensor = ttnn.prepare_conv_weights(
+                weight_tensor=self.conv1_weight_tensor,
+                weights_format="OIHW",
+                input_memory_config=fold_output_tensor.memory_config(),
+                **conv_kwargs,
+            )
+
+            self.conv1_bias_tensor = ttnn.prepare_conv_bias(
+                bias_tensor=self.conv1_bias_tensor,
+                input_memory_config=fold_output_tensor.memory_config(),
+                **conv_kwargs if self.conv1_bias_tensor is not None else None,
+            )
+            self.conv1_weight_tensor = ttnn.to_device(self.conv1_weight_tensor, device)
+            self.conv1_bias_tensor = ttnn.to_device(self.conv1_bias_tensor, device) if self.conv1_bias_tensor else None
+
         x, [x_height, x_width], [self.conv1_weight_tensor, self.conv1_bias_tensor] = ttnn.conv2d(
             input_tensor=fold_output_tensor,
             weight_tensor=self.conv1_weight_tensor,