diff --git a/src/compressed_tensors/utils/helpers.py b/src/compressed_tensors/utils/helpers.py
index 39066e8b..3e970484 100644
--- a/src/compressed_tensors/utils/helpers.py
+++ b/src/compressed_tensors/utils/helpers.py
@@ -255,35 +255,35 @@ def shard_tensor(
 
 def combine_shards(shards, dim=0):
     """
-    Combine decompressed shards along a given dimension without using torch.cat
-    for unsupported dtypes like float8_e4m3fn.
+    Combine decompressed shards along a given dimension using `narrow`.
 
     :param shards: List of decompressed shard tensors.
     :param dim: Dimension to combine along (default: 0).
     :return: Combined decompressed tensor.
     """
-    try:
-        # Attempt regular concatenation
-        return torch.cat(shards, dim=dim)
-    except RuntimeError as e:
-        # Handle unsupported concatenation
-        if all(shard.dtype == torch.float8_e4m3fn for shard in shards):
-            total_shape = list(shards[0].shape)
-            total_shape[dim] = sum(shard.shape[dim] for shard in shards)
-            combined = torch.zeros(
-                total_shape, dtype=shards[0].dtype, device=shards[0].device
-            )
+    if not shards:
+        raise ValueError("The list of shards is empty.")
 
-            shard_offset = 0
-            for shard in shards:
-                shard_size = shard.shape[dim]
-                combined.narrow(dim, shard_offset, shard_size).copy_(shard)
-                shard_offset += shard_size
+    # Assert that all shards have the same dtype
+    shard_dtypes = {shard.dtype for shard in shards}
+    if len(shard_dtypes) > 1:
+        raise ValueError("All shards must have the same dtype.")
 
-            return combined
-        else:
-            # Re-raise unexpected errors
-            raise e
+    # Determine the total shape of the combined tensor
+    total_shape = list(shards[0].shape)
+    total_shape[dim] = sum(shard.shape[dim] for shard in shards)
+
+    # Create the combined tensor
+    combined = torch.zeros(total_shape, dtype=shards[0].dtype, device=shards[0].device)
+
+    # Fill the combined tensor using narrow
+    shard_offset = 0
+    for shard in shards:
+        shard_size = shard.shape[dim]
+        combined.narrow(dim, shard_offset, shard_size).copy_(shard)
+        shard_offset += shard_size
+
+    return combined
 
 
 def pack_into_bitmasks(bytemasks: torch.Tensor) -> torch.Tensor: