diff --git a/transformer_engine/pytorch/csrc/quantizer.cpp b/transformer_engine/pytorch/csrc/quantizer.cpp
index 4704261790..33d9e4e5d6 100644
--- a/transformer_engine/pytorch/csrc/quantizer.cpp
+++ b/transformer_engine/pytorch/csrc/quantizer.cpp
@@ -1531,8 +1531,13 @@ std::pair<TensorWrapper, py::object> MXFP8Quantizer::create_tensor(
   if (columnwise_usage) {
     const std::vector<int64_t> scale_inv_shape_int64(columnwise_scale_inv_shape.begin(),
                                                      columnwise_scale_inv_shape.end());
-    columnwise_data_tensor = at::empty(shape_int64, uint8_tensor_opts);
     columnwise_scale_inv_tensor = at::empty(scale_inv_shape_int64, uint8_tensor_opts);
+    if (with_2d_quantization && rowwise_usage) {
+      // 2D quantization: rowwise and columnwise data are identical, share the buffer
+      columnwise_data_tensor = rowwise_data_tensor;
+    } else {
+      columnwise_data_tensor = at::empty(shape_int64, uint8_tensor_opts);
+    }
   }
 
   // Convert tensors to Python
diff --git a/transformer_engine/pytorch/tensor/mxfp8_tensor.py b/transformer_engine/pytorch/tensor/mxfp8_tensor.py
index 54cb281bd6..7567d6be96 100644
--- a/transformer_engine/pytorch/tensor/mxfp8_tensor.py
+++ b/transformer_engine/pytorch/tensor/mxfp8_tensor.py
@@ -87,7 +87,9 @@ def inner_tensor_specs(
                 torch.uint8,
             )
         if self.columnwise_usage:
-            specs["_columnwise_data"] = (shape, torch.uint8)
+            # 2D quantization: data is identical, reuse rowwise_data instead of allocating a copy
+            if not (self.with_2d_quantization and self.rowwise_usage):
+                specs["_columnwise_data"] = (shape, torch.uint8)
             specs["_columnwise_scale_inv"] = (
                 tuple(self.get_scale_shape(shape, columnwise=True)),
                 torch.uint8,
@@ -263,6 +265,15 @@ def __new__(
         with_gemm_swizzled_scales: bool,
         **kwargs,
     ):
+        # 2D quantization: columnwise data is identical to rowwise, alias it
+        if (
+            columnwise_data is None
+            and rowwise_data is not None
+            and quantizer is not None
+            and getattr(quantizer, "with_2d_quantization", False)
+            and getattr(quantizer, "columnwise_usage", False)
+        ):
+            columnwise_data = rowwise_data
         return super().__new__(
             cls,
             rowwise_data,