diff --git a/torchao/float8/float8_ops.py b/torchao/float8/float8_ops.py
index f7ccad4a86..a15368c9ba 100644
--- a/torchao/float8/float8_ops.py
+++ b/torchao/float8/float8_ops.py
@@ -40,20 +40,6 @@ def addmm_float8_unwrapped(
     a_inverse_scale = a_scale.reciprocal()
     b_inverse_scale = b_scale.reciprocal()
 
-    post_inverse_scale = None
-    is_rowwise_scaling = a_scale.shape == (a_data.shape[0], 1) and b_scale.shape == (
-        1,
-        b_data.shape[1],
-    )
-
-    if is_rowwise_scaling and not use_fast_accum:
-        # The rowwise CUTLASS-based kernel is so slow without fast-accum that
-        # we'd rather use the tensorwise cuBLAS-based kernel and do the scaling
-        # manually afterwards (hoping Inductor will be able to fuse it).
-        post_inverse_scale = a_inverse_scale * b_inverse_scale
-        a_inverse_scale = a_inverse_scale.new_ones(())
-        b_inverse_scale = a_inverse_scale.new_ones(())
-
     post_bias = None
     if output_dtype == torch.float32:
         # Bias is not supported by _scaled_mm when output is fp32
@@ -71,8 +57,6 @@ def addmm_float8_unwrapped(
         use_fast_accum=use_fast_accum,
     )
 
-    if post_inverse_scale is not None:
-        output *= post_inverse_scale
     if post_bias is not None:
         output += post_bias