diff --git a/transformer_nuggets/mx/to_blocked.py b/transformer_nuggets/mx/to_blocked.py
index c6f55cd..4432f15 100644
--- a/transformer_nuggets/mx/to_blocked.py
+++ b/transformer_nuggets/mx/to_blocked.py
@@ -1,5 +1,4 @@
 import torch
-import torch.nn.functional as F
 
 Tensor = torch.Tensor
 
@@ -8,7 +7,7 @@ def ceil_div(a, b):
     return (a + b - 1) // b
 
 
-def to_blocked(input_matrix) -> Tensor:
+def to_blocked(input_matrix: Tensor) -> Tensor:
     """
     Rearrange a large matrix by breaking it into blocks and applying the rearrangement pattern.
 
@@ -25,14 +24,21 @@ def to_blocked(input_matrix) -> Tensor:
     n_row_blocks = ceil_div(rows, 128)
     n_col_blocks = ceil_div(cols, 4)
 
-    # Pad out and view as tiles of (128, 4)
-    padded = F.pad(input_matrix, (0, -cols % 4, 0, -rows % 128))
-    blocks = padded.view(n_row_blocks, 128, n_col_blocks, 4).permute(0, 2, 1, 3)
+    # Calculate the padded shape
+    padded_rows = n_row_blocks * 128
+    padded_cols = n_col_blocks * 4
+
+    padded = input_matrix
+    if (rows, cols) != (padded_rows, padded_cols):
+        padded = torch.zeros(
+            (padded_rows, padded_cols), device=input_matrix.device, dtype=input_matrix.dtype
+        )
+        padded[:rows, :cols] = input_matrix
 
-    # rearrange all tiles
+    # Rearrange the blocks
+    blocks = padded.view(n_row_blocks, 128, n_col_blocks, 4).permute(0, 2, 1, 3)
     rearranged = blocks.reshape(-1, 4, 32, 4).transpose(1, 2).reshape(-1, 32, 16)
 
-    # Layout rearranged tiles according to second pic
     return rearranged.flatten()