diff --git a/torchao/csrc/cuda/mx_kernels/mxfp8_extension.cpp b/torchao/csrc/cuda/mx_kernels/mxfp8_extension.cpp
index 3b49b4c1c0..23b60d9fd7 100644
--- a/torchao/csrc/cuda/mx_kernels/mxfp8_extension.cpp
+++ b/torchao/csrc/cuda/mx_kernels/mxfp8_extension.cpp
@@ -114,13 +114,11 @@ mxfp8_quantize(const at::Tensor& input, bool rowwise, bool colwise,
   if (colwise) {
     const int64_t num_row_blocks = (rows + scale_dim_y - 1) / scale_dim_y;
     output_colwise = at::empty_strided({rows, cols}, {1, rows}, options_fp8);
-    // Need scales_colwise to be this shape so the 'col' dim stride is 1, 
-    // for colwise scaling, we can avoid uncoalesced writes to global memory.
-    // This is because each of the 32 threads in a warp will be computing
-    // a scale for a different column of 32 input data values, then each writing
-    // that scale to global memory - so the stride along this `col` dim should be 1
-    // so writes can be coalesced into a single transaction.
-    scales_colwise = at::empty_strided({cols, num_row_blocks}, {1, cols}, options_scale);
+
+    // Accept uncoalesced global stores for scale tensor, since row major is much for favorable for the subsequent 
+    // per-group blocked format kernel.
+    // Microbenchmarks show the memory bandwidth utilization is virtually identical to coalesced global stores.
+    scales_colwise = at::empty({cols, num_row_blocks}, options_scale);
   } else {
     output_colwise = at::empty({0}, options_fp8);
     scales_colwise = at::empty({0}, options_scale);
diff --git a/torchao/prototype/moe_training/kernels/mxfp8/__init__.py b/torchao/prototype/moe_training/kernels/mxfp8/__init__.py
index 69bff9e1a0..d7d41aba1e 100644
--- a/torchao/prototype/moe_training/kernels/mxfp8/__init__.py
+++ b/torchao/prototype/moe_training/kernels/mxfp8/__init__.py
@@ -1,4 +1,5 @@
 from torchao.prototype.moe_training.kernels.mxfp8.quant import (
+    mx_block_rearrange_2d_K_groups_cuda,  # noqa: F401
     mxfp8_quantize_cuda_3d,  # noqa: F401
     torch_to_blocked_2d_K_groups,  # noqa: F401
     torch_to_blocked_2d_M_groups,  # noqa: F401
diff --git a/torchao/prototype/moe_training/scaled_grouped_mm.py b/torchao/prototype/moe_training/scaled_grouped_mm.py
index accc8853cb..98a5fd39e5 100644
--- a/torchao/prototype/moe_training/scaled_grouped_mm.py
+++ b/torchao/prototype/moe_training/scaled_grouped_mm.py
@@ -17,8 +17,8 @@
     triton_fp8_rowwise_3d_transpose_rhs,
 )
 from torchao.prototype.moe_training.kernels.mxfp8 import (
+    mx_block_rearrange_2d_K_groups_cuda,
     mxfp8_quantize_cuda_3d,
-    triton_mx_block_rearrange_2d_K_groups,
     triton_mx_block_rearrange_2d_M_groups,
     triton_mx_block_rearrange_per_group_3d,
 )
@@ -449,11 +449,11 @@ def backward(ctx, grad_out: torch.Tensor):
 
             # Convert scales to blocked format for 2d-2d grouped mm
             scale_group_offsets = offs // block_size
-            grad_out_t_scales_blocked = triton_mx_block_rearrange_2d_K_groups(
+            grad_out_t_scales_blocked = mx_block_rearrange_2d_K_groups_cuda(
                 grad_out_t_scales,
                 scale_group_offsets,
             )
-            A_t_scales_blocked = triton_mx_block_rearrange_2d_K_groups(
+            A_t_scales_blocked = mx_block_rearrange_2d_K_groups_cuda(
                 A_t_scales,
                 scale_group_offsets,
             )
diff --git a/torchao/prototype/mx_formats/kernels.py b/torchao/prototype/mx_formats/kernels.py
index 236aa3db53..061502060e 100644
--- a/torchao/prototype/mx_formats/kernels.py
+++ b/torchao/prototype/mx_formats/kernels.py
@@ -1219,10 +1219,9 @@ def _fake_mxfp8_quantize(
                 (rows, cols), (1, rows), dtype=torch.float8_e4m3fn, device=x.device
             )
 
-            # colwise scales are written in column-major format to avoid uncoalesced global memory accesses
-            scales_colwise = torch.empty_strided(
+            # and microb
+            scales_colwise = torch.empty(
                 (cols, num_row_blocks),
-                (1, cols),
                 dtype=torch.float8_e8m0fnu,
                 device=x.device,
             )