diff --git a/test/prototype/mx_formats/test_mx_tensor.py b/test/prototype/mx_formats/test_mx_tensor.py
index 2c89fae96d..305ec64500 100644
--- a/test/prototype/mx_formats/test_mx_tensor.py
+++ b/test/prototype/mx_formats/test_mx_tensor.py
@@ -26,6 +26,7 @@
 from torchao.quantization.utils import compute_error
 from torchao.utils import (
     is_sm_at_least_89,
+    is_sm_at_least_90,
     is_sm_at_least_100,
     torch_version_at_least,
 )
@@ -556,6 +557,26 @@ def test_to_mx_inductor_single_kernel():
     FileCheck().check("def call(").check_count(".run(", 1, exactly=True).run(code[0])
 
 
+@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
+@pytest.mark.skipIf(not is_sm_at_least_90(), "Need sm90+")
+def test_index_select():
+    """
+    test that `x_0 = x[0]` works when `x` is a 3D `MXTensor`. This is
+    useful when stitching checkpoints of `num_experts` 2D parameters into
+    a single 3D parameter when converting between model definitions that
+    use 2D and 3D parameters for their expert weights.
+    """
+
+    E, K, N = 128, 256, 512
+    x = torch.randn(E, N, K, device="cuda", dtype=torch.bfloat16)
+    x_mx = MXTensor.to_mx(x, torch.float8_e4m3fn, 32)
+
+    x_mx_1 = x_mx[1]
+    torch.testing.assert_close(
+        x_mx.to_dtype(x.dtype)[1], x_mx_1.to_dtype(x.dtype), atol=0, rtol=0
+    )
+
+
 @pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
 @pytest.mark.skipif(
     not is_sm_at_least_89(),
diff --git a/torchao/prototype/mx_formats/mx_ops.py b/torchao/prototype/mx_formats/mx_ops.py
index d870698601..8fcda5e69c 100644
--- a/torchao/prototype/mx_formats/mx_ops.py
+++ b/torchao/prototype/mx_formats/mx_ops.py
@@ -322,3 +322,23 @@ def mx_clone(func, types, args, kwargs):
         clone_fn = lambda x: x.clone()
 
     return self._apply_fn_to_data(clone_fn)
+
+
+@implements([aten.select.int])
+def mx_select(func, types, args, kwargs):
+    old_mx_tensor, dim, index = args
+    assert dim == 0, f"MXTensor aten.select.int with {dim=} is not yet supported"
+    assert len(old_mx_tensor.qdata.shape) == len(old_mx_tensor._scale_e8m0.shape), (
+        "unsupported"
+    )
+    new_mx_tensor = old_mx_tensor.__class__(
+        old_mx_tensor.qdata[index],
+        old_mx_tensor._scale_e8m0[index],
+        old_mx_tensor._elem_dtype,
+        old_mx_tensor._block_size,
+        old_mx_tensor._orig_dtype,
+        old_mx_tensor._gemm_kernel_choice,
+        old_mx_tensor._pack_fp6,
+        old_mx_tensor.act_quant_kwargs,
+    )
+    return return_and_correct_aliasing(func, args, kwargs, new_mx_tensor)