diff --git a/mlir/lib/Dialect/Vector/IR/VectorOps.cpp b/mlir/lib/Dialect/Vector/IR/VectorOps.cpp
--- a/mlir/lib/Dialect/Vector/IR/VectorOps.cpp
+++ b/mlir/lib/Dialect/Vector/IR/VectorOps.cpp
@@ -1151,7 +1151,8 @@
     auto n =
         std::min<size_t>(adaptor.getPosition().size(), vectorType.getRank());
     inferredReturnTypes.push_back(VectorType::get(
-        vectorType.getShape().drop_front(n), vectorType.getElementType()));
+        vectorType.getShape().drop_front(n), vectorType.getElementType(),
+        vectorType.getScalableDims().drop_front(n)));
   }
   return success();
 }
diff --git a/mlir/test/Dialect/Vector/vector-transforms.mlir b/mlir/test/Dialect/Vector/vector-transforms.mlir
--- a/mlir/test/Dialect/Vector/vector-transforms.mlir
+++ b/mlir/test/Dialect/Vector/vector-transforms.mlir
@@ -34,6 +34,32 @@
   return %1 : vector<2x[4]x1xf32>
 }
 
+// CHECK-LABEL:   func.func @cast_away_leading_one_dim(
+// CHECK-SAME:      %[[VAL_0:.*]]: vector<1x4x1xf32>,
+// CHECK-SAME:      %[[VAL_1:.*]]: vector<1x4x1xf32>) -> vector<1x4x1xf32> {
+// CHECK:           %[[VAL_2:.*]] = vector.extract %[[VAL_0]][0] : vector<1x4x1xf32>
+// CHECK:           %[[VAL_3:.*]] = vector.extract %[[VAL_1]][0] : vector<1x4x1xf32>
+// CHECK:           %[[VAL_4:.*]] = arith.mulf %[[VAL_2]], %[[VAL_3]] : vector<4x1xf32>
+// CHECK:           %[[VAL_5:.*]] = vector.broadcast %[[VAL_4]] : vector<4x1xf32> to vector<1x4x1xf32>
+// CHECK:           return %[[VAL_5]] : vector<1x4x1xf32>
+func.func @cast_away_leading_one_dim(%arg0: vector<1x4x1xf32>, %arg1: vector<1x4x1xf32>) -> vector<1x4x1xf32> {
+  %1 = arith.mulf %arg0, %arg1 : vector<1x4x1xf32>
+  return %1: vector<1x4x1xf32>
+}
+
+// CHECK-LABEL:   func.func @scalable_cast_away_leading_one_dim(
+// CHECK-SAME:       %[[VAL_0:.*]]: vector<1x[4]x1xf32>,
+// CHECK-SAME:       %[[VAL_1:.*]]: vector<1x[4]x1xf32>) -> vector<1x[4]x1xf32> {
+// CHECK:           %[[VAL_2:.*]] = vector.extract %[[VAL_0]][0] : vector<1x[4]x1xf32>
+// CHECK:           %[[VAL_3:.*]] = vector.extract %[[VAL_1]][0] : vector<1x[4]x1xf32>
+// CHECK:           %[[VAL_4:.*]] = arith.mulf %[[VAL_2]], %[[VAL_3]] : vector<[4]x1xf32>
+// CHECK:           %[[VAL_5:.*]] = vector.broadcast %[[VAL_4]] : vector<[4]x1xf32> to vector<1x[4]x1xf32>
+// CHECK:           return %[[VAL_5]] : vector<1x[4]x1xf32>
+func.func @scalable_cast_away_leading_one_dim(%arg0: vector<1x[4]x1xf32>, %arg1: vector<1x[4]x1xf32>) -> vector<1x[4]x1xf32> {
+  %1 = arith.mulf %arg0, %arg1 : vector<1x[4]x1xf32>
+  return %1: vector<1x[4]x1xf32>
+}
+
 // CHECK-LABEL: func @add4x4
 //      CHECK: %[[S1:.*]] = vector.extract_strided_slice %{{.*}} {offsets = [0, 0], sizes = [2, 2], strides = [1, 1]} : vector<4x4xf32> to vector<2x2xf32>
 // CHECK-NEXT: %[[S2:.*]] = vector.extract_strided_slice %{{.*}} {offsets = [0, 0], sizes = [2, 2], strides = [1, 1]} : vector<4x4xf32> to vector<2x2xf32>