diff --git a/et_replay/execution_trace.py b/et_replay/execution_trace.py
index c71d0185..d8eec397 100644
--- a/et_replay/execution_trace.py
+++ b/et_replay/execution_trace.py
@@ -128,6 +128,7 @@ def __init__(
         comm_args: _CommArgs | None = None,
         input_strides: list[Any] | None = None,
         output_strides: list[Any] | None = None,
+        tensor_range: str | None = None,
     ):
         self.name: str = name
         self.parent_id: int = parent_id
@@ -156,6 +157,11 @@ def __init__(
         self.output_shapes: list[Any] = output_shapes
         self.output_strides: list[Any] | None = output_strides
         self.commArgs: _CommArgs | None = comm_args
+        self.tensor_range = json.loads(tensor_range) if tensor_range else None
+        if self.tensor_range is not None:
+            self.tensor_range = {
+                int(index): min_max for index, min_max in self.tensor_range.items()
+            }
 
     def get_inputs(self) -> Iterable:
         return zip(self.input_types, self.inputs, self.input_shapes)
@@ -293,6 +299,12 @@ def get_input_tensor_strides(self) -> list[tuple] | None:
         else:
             return self.get_tensor_strides(self.get_inputs(), self.input_strides)
 
+    def get_input_tensor_range(self, tensor_index) -> tuple | None:
+        if self.tensor_range is None or tensor_index not in self.tensor_range:
+            return None
+        else:
+            return self.tensor_range[tensor_index]
+
     def get_output_tensor_strides(self) -> list[tuple] | None:
         if self.output_strides is None:
             return None
@@ -404,6 +416,7 @@ def schema_chakra(self) -> tuple[int, int, int]:
         "tid": int,
         "kernel_backend": str,
         "kernel_file": str,
+        "tensor_range": str,
     }
 
     @classmethod
@@ -477,6 +490,7 @@ def _create_node_v1_0_2_chakra_0_0_4(pid, x: dict[str, Any]) -> Node:
             tid,
             kernel_backend,
             kernel_file,
+            _,
         ) = ExecutionTrace._read_attrs(x)
 
         comm_attrs = (
@@ -520,6 +534,7 @@ def _create_node_v1_1_1_chakra_0_0_4(pid, x: dict[str, Any]) -> Node:
             tid,
             kernel_backend,
             kernel_file,
+            tensor_range,
         ) = ExecutionTrace._read_attrs(x)
 
         comm_attrs = (
@@ -551,6 +566,7 @@ def _create_node_v1_1_1_chakra_0_0_4(pid, x: dict[str, Any]) -> Node:
             comm_attrs,
             x["inputs"]["strides"],
             x["outputs"]["strides"],
+            tensor_range,
         )
 
     def get_nodes(self, clean: bool = False):
diff --git a/et_replay/tools/et_replay.py b/et_replay/tools/et_replay.py
index ca7b4a5b..f680ffe1 100644
--- a/et_replay/tools/et_replay.py
+++ b/et_replay/tools/et_replay.py
@@ -662,6 +662,7 @@ def add_comm_tensor_registry(tensor_strides, tensors):
                             shape,
                             dtype,
                             strides,
+                            node.get_input_tensor_range(idx),
                         )
                         self.tensor_registry_permanent[replay_t_id] = tensor
                     except KeyError:
@@ -724,6 +725,7 @@ def allocate_comp_tensors(self, node):  # noqa: C901
                             shape,
                             dtype,
                             strides,
+                            node.get_input_tensor_range(idx),
                         )
                         self.tensor_registry_permanent[replay_t_id] = tensor
 
@@ -1178,11 +1180,40 @@ def get_tensor_from_storage(
         shape,
         data_type,
         strides,
+        tensor_range,
     ):
         assert storage_id in self.tensor_storage_map
         tensor_data = self.tensor_storage_map[storage_id]
         if device not in tensor_data[1]:
-            if data_type in [torch.half, torch.float32, torch.float64, torch.bfloat16]:
+            if (
+                data_type
+                in [
+                    torch.int8,
+                    torch.int16,
+                    torch.int32,
+                    torch.int64,
+                    torch.uint8,
+                    torch.uint16,
+                    torch.uint32,
+                    torch.uint64,
+                    torch.int,
+                    torch.long,
+                ]
+                and tensor_range is not None
+            ):
+                storage_tensor = torch.randint(
+                    tensor_range[0],
+                    tensor_range[1] + 1,
+                    (tensor_data[0] // elem_bytes,),
+                    dtype=data_type,
+                    device=device,
+                )
+            elif data_type in [
+                torch.half,
+                torch.float32,
+                torch.float64,
+                torch.bfloat16,
+            ]:
                 storage_tensor = torch.rand(
                     (tensor_data[0] // elem_bytes), dtype=data_type, device=device
                 )
@@ -1369,7 +1400,6 @@ def run_op(self, node, iter, cnt):  # noqa: C901
             inputs, msg = self.get_data(node, True)
             if msg != "":
                 return False, msg
-
             # TODO: why need this hack?
             # Workaround to eliminate the "strides() called on undefined Tensor" error.
             if node.name == "aten::convolution_backward":