diff --git a/mlx/primitives.cpp b/mlx/primitives.cpp
index 590af60f6..3d36f0881 100644
--- a/mlx/primitives.cpp
+++ b/mlx/primitives.cpp
@@ -1275,6 +1275,61 @@ std::vector<array> Convolution::vjp(
   return grads;
 }
 
+std::pair<std::vector<array>, std::vector<int>> Convolution::vmap(
+    const std::vector<array>& inputs,
+    const std::vector<int>& axes) {
+  auto do_conv = [&](const array& in, const array& w, int groups) {
+    return conv_general(
+        in,
+        w,
+        kernel_strides_,
+        padding_,
+        kernel_dilation_,
+        input_dilation_,
+        groups,
+        flip_,
+        stream());
+  };
+  bool in_vmap = axes[0] >= 0;
+  bool w_vmap = axes[1] >= 0;
+  auto in = inputs[0];
+  auto w = inputs[1];
+  if (in_vmap && !w_vmap) {
+    // flatten / unflatten the batch dimension
+    // of the input / output
+    if (axes[0] > 0) {
+      in = moveaxis(in, axes[0], 0, stream());
+    }
+    auto out = do_conv(flatten(in, 0, 1, stream()), w, groups_);
+    out = unflatten(out, 0, {in.shape(0), in.shape(1)}, stream());
+    return {{out}, {0}};
+  } else if (!in_vmap && w_vmap) {
+    // flatten into the output channels of w
+    // unflatten the channels of the output
+    if (axes[1] > 0) {
+      w = moveaxis(w, axes[1], 0, stream());
+    }
+    auto out = do_conv(in, flatten(w, 0, 1, stream()), groups_);
+    out = unflatten(out, -1, {w.shape(0), w.shape(1)}, stream());
+    return {{out}, {static_cast<int>(out.ndim() - 2)}};
+  } else if (in_vmap && w_vmap) {
+    // use a group convolution when both inputs are vmapped
+    auto b = in.shape(axes[0]);
+    in = moveaxis(in, axes[0], -2, stream());
+    in = flatten(in, -2, -1, stream());
+    if (axes[1] > 0) {
+      w = moveaxis(w, axes[1], 0, stream());
+    }
+    auto c_out = w.shape(1);
+    w = flatten(w, 0, 1, stream());
+    auto out = do_conv(in, w, groups_ * b);
+    out = unflatten(out, -1, {b, c_out}, stream());
+    return {{out}, {static_cast<int>(out.ndim() - 2)}};
+  } else {
+    return {{do_conv(in, w, groups_)}, {-1}};
+  }
+}
+
 bool Convolution::is_equivalent(const Primitive& other) const {
   const Convolution& c_other = static_cast<const Convolution&>(other);
   return padding_ == c_other.padding_ &&
diff --git a/mlx/primitives.h b/mlx/primitives.h
index 997931f30..3753e43c5 100644
--- a/mlx/primitives.h
+++ b/mlx/primitives.h
@@ -711,6 +711,7 @@ class Convolution : public UnaryPrimitive {
       const std::vector<int>& argnums,
       const std::vector<array>& outputs) override;
 
+  DEFINE_VMAP()
   DEFINE_PRINT(Convolution)
   bool is_equivalent(const Primitive& other) const override;
   auto state() const {
diff --git a/python/tests/test_vmap.py b/python/tests/test_vmap.py
index 1a1ba23b3..e571678d3 100644
--- a/python/tests/test_vmap.py
+++ b/python/tests/test_vmap.py
@@ -669,6 +669,57 @@ class TestVmap(mlx_tests.MLXTestCase):
         self.assertEqual(mx.vmap(fun, in_axes=(1,))(x).shape, (3, 8))
         self.assertEqual(mx.vmap(fun, in_axes=(2,))(x).shape, (4, 6))
 
+    def test_vmap_conv(self):
+        # vmap input only
+        x = mx.random.uniform(shape=(2, 2, 5, 4))
+        w = mx.random.uniform(shape=(8, 3, 4))
+
+        expected = mx.stack([mx.conv1d(xi, w) for xi in x])
+        out = mx.vmap(mx.conv1d, in_axes=(0, None))(x, w)
+        self.assertTrue(mx.allclose(expected, out))
+
+        x = mx.moveaxis(x, 0, 2)
+        out = mx.vmap(mx.conv1d, in_axes=(2, None))(x, w)
+        self.assertTrue(mx.allclose(expected, out))
+
+        # vmap weights only
+        x = mx.random.uniform(shape=(2, 5, 4))
+        w = mx.random.uniform(shape=(3, 8, 3, 4))
+
+        expected = mx.stack([mx.conv1d(x, wi) for wi in w])
+        out = mx.vmap(mx.conv1d, in_axes=(None, 0))(x, w)
+        self.assertTrue(mx.allclose(expected, out))
+
+        w = mx.moveaxis(w, 0, 1)
+        out = mx.vmap(mx.conv1d, in_axes=(None, 1))(x, w)
+        self.assertTrue(mx.allclose(expected, out))
+
+        # vmap weights and input
+        x = mx.random.uniform(shape=(3, 2, 5, 4))
+        w = mx.random.uniform(shape=(3, 8, 3, 4))
+
+        expected = mx.stack([mx.conv1d(xi, wi) for xi, wi in zip(x, w)])
+        out = mx.vmap(mx.conv1d, in_axes=(0, 0))(x, w)
+        self.assertTrue(mx.allclose(expected, out))
+
+        x = mx.random.uniform(shape=(2, 3, 5, 4))
+        w = mx.random.uniform(shape=(8, 3, 4, 3))
+
+        expected = mx.stack([mx.conv1d(x[:, i], w[..., i]) for i in range(3)])
+        out = mx.vmap(mx.conv1d, in_axes=(1, 3))(x, w)
+        self.assertTrue(mx.allclose(expected, out))
+
+        # Test with groups
+        x = mx.random.uniform(shape=(3, 2, 5, 8))
+        w = mx.random.uniform(shape=(3, 2, 3, 4))
+
+        def gconv(x, w):
+            return mx.conv1d(x, w, groups=2)
+
+        expected = mx.stack([gconv(xi, wi) for xi, wi in zip(x, w)])
+        out = mx.vmap(gconv, in_axes=(0, 0))(x, w)
+        self.assertTrue(mx.allclose(expected, out))
+
 
 if __name__ == "__main__":
     unittest.main()