diff --git a/mlx/primitives.cpp b/mlx/primitives.cpp
index fa9e55700e..2193226838 100644
--- a/mlx/primitives.cpp
+++ b/mlx/primitives.cpp
@@ -3231,7 +3231,32 @@ std::vector<array> Pad::jvp(
 std::pair<std::vector<array>, std::vector<int>> Pad::vmap(
     const std::vector<array>& inputs,
     const std::vector<int>& axes) {
-  throw std::runtime_error("Pad vmap is NYI.");
+  assert(inputs.size() == 2);
+  assert(axes.size() == 2);
+
+  if (axes[1] >= 0) {
+    throw std::invalid_argument(
+        "[Pad::vmap] Vmap over padding value is not supported.");
+  }
+
+  auto ax = axes[0];
+  auto pad_axes = axes_;
+  if (ax >= 0) {
+    for (auto& pad_ax : pad_axes) {
+      pad_ax = (pad_ax >= ax) ? pad_ax + 1 : pad_ax;
+    }
+  }
+
+  return {
+      {pad(
+          inputs[0],
+          pad_axes,
+          low_pad_size_,
+          high_pad_size_,
+          inputs[1],
+          "constant",
+          stream())},
+      {ax}};
 }
 
 bool Pad::is_equivalent(const Primitive& other) const {
diff --git a/python/tests/test_vmap.py b/python/tests/test_vmap.py
index 7847a9a60e..30c85de00f 100644
--- a/python/tests/test_vmap.py
+++ b/python/tests/test_vmap.py
@@ -723,6 +723,44 @@ def gconv(x, w):
         out = mx.vmap(gconv, in_axes=(0, 0))(x, w)
         self.assertTrue(mx.allclose(expected, out))
 
+    def test_vmap_pad(self):
+        def pad2d(x, value=0.0):
+            return mx.pad(x, ((1, 2), (0, 1)), constant_values=value)
+
+        x = mx.arange(24, dtype=mx.float32).reshape(2, 3, 4)
+
+        expected = mx.stack([pad2d(xi) for xi in x])
+        out = mx.vmap(pad2d, in_axes=0)(x)
+        self.assertTrue(mx.array_equal(out, expected))
+
+        expected = mx.stack([pad2d(x[:, i, :]) for i in range(x.shape[1])])
+        out = mx.vmap(pad2d, in_axes=1)(x)
+        self.assertTrue(mx.array_equal(out, expected))
+
+        expected = mx.stack([pad2d(x[:, :, i]) for i in range(x.shape[2])], axis=2)
+        out = mx.vmap(pad2d, in_axes=-1, out_axes=-1)(x)
+        self.assertTrue(mx.array_equal(out, expected))
+
+        nested = mx.vmap(mx.vmap(lambda y: mx.pad(y, (1, 1))))
+        out = nested(x)
+        expected = mx.pad(x, ((0, 0), (0, 0), (1, 1)))
+        self.assertTrue(mx.array_equal(out, expected))
+
+        out = mx.vmap(
+            lambda a, v: mx.pad(a, ((1, 1), (1, 1)), constant_values=v),
+            in_axes=(0, None),
+        )(x, mx.array(5.0))
+        expected = mx.stack(
+            [mx.pad(xi, ((1, 1), (1, 1)), constant_values=mx.array(5.0)) for xi in x]
+        )
+        self.assertTrue(mx.array_equal(out, expected))
+
+        pad_values = mx.array([3.0, 4.0])
+        with self.assertRaises(ValueError):
+            mx.vmap(lambda a, v: mx.pad(a, ((1, 1), (1, 1)), constant_values=v))(
+                x, pad_values
+            )
+
     def test_vmap_types(self):
 
         from typing import NamedTuple