diff --git a/torchao/prototype/moe_training/scaled_grouped_mm.py b/torchao/prototype/moe_training/scaled_grouped_mm.py
index 2bb3166d16..95cb5f4ce1 100644
--- a/torchao/prototype/moe_training/scaled_grouped_mm.py
+++ b/torchao/prototype/moe_training/scaled_grouped_mm.py
@@ -296,6 +296,7 @@ def forward(
         out_dtype: Optional[torch.dtype] = torch.bfloat16,
         emulated: bool = False,
         use_triton_for_dim0_cast: bool = False,
+        scale_calculation_mode: ScaleCalculationMode = ScaleCalculationMode.RCEIL,
     ) -> torch.Tensor:
         # torchao _quantize_then_scaled_grouped_mm only supports A=2D and B=3D.
         assert A.ndim == 2, "A must be 2D"
@@ -321,11 +322,13 @@ def forward(
                 A,
                 elem_dtype=torch.float8_e4m3fn,
                 block_size=block_size,
+                scaling_mode=scale_calculation_mode,
             )
             B_scales, B_data = to_mx(
                 B_t.transpose(-2, -1),
                 elem_dtype=torch.float8_e4m3fn,
                 block_size=block_size,
+                scaling_mode=scale_calculation_mode,
             )
 
         # Convert scales to blocked format for 2d-3d grouped mm
@@ -355,6 +358,7 @@ def forward(
         ctx.out_dtype = out_dtype
         ctx.emulated = emulated
         ctx.use_triton_for_dim0_cast = use_triton_for_dim0_cast
+        ctx.scale_calculation_mode = scale_calculation_mode
         return out
 
     @staticmethod
@@ -363,6 +367,7 @@ def backward(ctx, grad_out: torch.Tensor):
         block_size = ctx.block_size
         out_dtype = ctx.out_dtype
         use_triton_for_dim0_cast = ctx.use_triton_for_dim0_cast
+        scale_calculation_mode = ctx.scale_calculation_mode
 
         # grad_out_data shape: (M, N)
         # grad_out_scale shape: (M, N//block_size)
@@ -375,13 +380,16 @@ def backward(ctx, grad_out: torch.Tensor):
                 grad_out,
                 elem_dtype=torch.float8_e4m3fn,
                 block_size=block_size,
+                scaling_mode=scale_calculation_mode,
             )
 
         # Quantize 3d expert weights along N (contraction dimension for next grouped gemm)
         # (E, K, N) -> (E, N, K)
         B = B_t.transpose(-2, -1)
         B_data, B_scales = mxfp8_quantize_cuda_3d(
-            B._data if hasattr(B, "_data") else B, block_size=block_size
+            B._data if hasattr(B, "_data") else B,
+            block_size=block_size,
+            scaling_mode=scale_calculation_mode.value.lower(),
         )
         # (E, N//block_size, K) -> (E, K, N//block_size)
         B_scales = B_scales.transpose(-2, -1)
@@ -413,7 +421,7 @@ def backward(ctx, grad_out: torch.Tensor):
             hp_dtype=grad_out.dtype,
             gemm_kernel_choice=MXGemmKernelChoice.CUTLASS,  # Not used
             cast_kernel_choice=MXFP8Dim1CastKernelChoice.CUDA,
-            scale_calculation_mode=ScaleCalculationMode.FLOOR,
+            scale_calculation_mode=scale_calculation_mode,
         )
         grad_out_t_data = grad_out_t_mx.qdata
         grad_out_t_scales = grad_out_t_mx.scale
@@ -429,7 +437,7 @@ def backward(ctx, grad_out: torch.Tensor):
             hp_dtype=A.dtype,
             gemm_kernel_choice=MXGemmKernelChoice.CUTLASS,  # Not used
             cast_kernel_choice=MXFP8Dim1CastKernelChoice.CUDA,
-            scale_calculation_mode=ScaleCalculationMode.FLOOR,
+            scale_calculation_mode=scale_calculation_mode,
         )
         A_t_data = A_t_mx.qdata
         A_t_scales = A_t_mx.scale