diff --git a/scripts/bench_moe.py b/scripts/bench_moe.py
index 777a88024..db4044748 100644
--- a/scripts/bench_moe.py
+++ b/scripts/bench_moe.py
@@ -210,7 +210,12 @@ def main():
                 f"torch_grouped\t{t_ms:.2f} ms\t{tokens / (t_ms / 1000):.1f} tok/s\t{tflops:.2f} TFLOP/s\t{speedup:.2f}×"
             )
         else:
-            print("torch_grouped\tN/A (op not callable)")
+            try:
+                from axolotl.kernels.moe.torch_grouped import LAST_ERROR as _TG_ERR
+            except Exception:
+                _TG_ERR = None
+            reason = f" (reason: {_TG_ERR})" if _TG_ERR else ""
+            print(f"torch_grouped\tN/A (op not callable){reason}")
     else:
         print("torch_grouped\tN/A (unavailable)")
 
diff --git a/src/axolotl/kernels/moe/torch_grouped.py b/src/axolotl/kernels/moe/torch_grouped.py
index d8689fba0..1ed2ce20b 100644
--- a/src/axolotl/kernels/moe/torch_grouped.py
+++ b/src/axolotl/kernels/moe/torch_grouped.py
@@ -25,6 +25,9 @@ def available() -> bool:
         return False
 
 
+LAST_ERROR: Optional[str] = None
+
+
 def _call_grouped_mm(
     As: List[torch.Tensor], Bs: List[torch.Tensor]
 ) -> Optional[List[torch.Tensor]]:
@@ -32,13 +35,24 @@ def _call_grouped_mm(
     Try calling the appropriate grouped mm op available in this torch build.
     Returns list of outputs or None on failure.
     """
+    global LAST_ERROR
     try:
+        # Ensure 2D contiguous inputs
+        As = [a.contiguous().view(a.shape[0], a.shape[1]) for a in As]
+        Bs = [b.contiguous().view(b.shape[0], b.shape[1]) for b in Bs]
+
         if hasattr(torch.ops.aten, "_grouped_mm"):
-            return torch.ops.aten._grouped_mm(As, Bs)  # type: ignore[attr-defined]
+            try:
+                return torch.ops.aten._grouped_mm(As, Bs)  # type: ignore[attr-defined]
+            except Exception as e:
+                LAST_ERROR = f"_grouped_mm failed: {e}"
         if hasattr(torch.ops.aten, "_scaled_grouped_mm"):
-            # signature likely (As, Bs, alpha, beta)
-            return torch.ops.aten._scaled_grouped_mm(As, Bs, 1.0, 0.0)  # type: ignore[attr-defined]
-    except Exception:
+            try:
+                return torch.ops.aten._scaled_grouped_mm(As, Bs, 1.0, 0.0)  # type: ignore[attr-defined]
+            except Exception as e:
+                LAST_ERROR = f"_scaled_grouped_mm failed: {e}"
+    except Exception as e:
+        LAST_ERROR = f"call error: {e}"
         return None
     return None