diff --git a/transformer_engine/debug/features/utils/stats_computation.py b/transformer_engine/debug/features/utils/stats_computation.py
index b0002ffee6..6668400017 100644
--- a/transformer_engine/debug/features/utils/stats_computation.py
+++ b/transformer_engine/debug/features/utils/stats_computation.py
@@ -348,6 +348,21 @@ def get_scale_inv(quantized_tensor, columnwise):
             return getattr(quantized_tensor, "_columnwise_scale_inv")
         return getattr(quantized_tensor, "_rowwise_scale_inv")
 
+    def nonzero_min(scale_inv):
+        # MXFP8/NVFP4 quantizers round the scale_inv shape up to multiples of
+        # 128 along one axis and 4 along the other and fill the extra slots
+        # with zeros (via torch.nn.functional.pad with the default value=0),
+        # so a plain .min() always returns 0 for shapes that needed padding.
+        # A real scale_inv entry is never 0: compute_scale_from_amax returns
+        # scale=1.0 for all-zero blocks and clamps the inf case to a finite
+        # fallback, so zeros uniquely identify padding and masking them out
+        # gives the true minimum. The empty-after-mask branch is a safety
+        # net for the (in practice unreachable) all-zero tensor.
+        nz = scale_inv[scale_inv != 0]
+        if nz.numel() == 0:
+            return scale_inv.new_zeros(())
+        return nz.min()
+
     columnwise_suffix = "_columnwise" if columnwise else ""
     # Prepare stat names.
     stat_name_min = (
@@ -363,7 +378,9 @@ def get_scale_inv(quantized_tensor, columnwise):
 
     # Capture the attribute name inside lambdas via default args to avoid late binding.
     STATS[stat_name_min] = (
-        lambda x, aux_dict, _col=columnwise: get_scale_inv(aux_dict[recipe_name], _col).min(),
+        lambda x, aux_dict, _col=columnwise: nonzero_min(
+            get_scale_inv(aux_dict[recipe_name], _col)
+        ),
         lambda buffers, _sn=stat_name_min: min(_get(buffers, _sn)),
     )
     STATS[stat_name_max] = (