diff --git a/torchao/prototype/moe_training/examples/simple_moe_layer.py b/torchao/prototype/moe_training/examples/simple_moe_layer.py
index 244d786c80..063a12dfcf 100644
--- a/torchao/prototype/moe_training/examples/simple_moe_layer.py
+++ b/torchao/prototype/moe_training/examples/simple_moe_layer.py
@@ -1,3 +1,9 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD 3-Clause license found in the
+# LICENSE file in the root directory of this source tree.
+
 import torch
 from torch import nn
 from torch.nn import functional as F
@@ -5,27 +11,41 @@
 # this feature requires CUDA and SM89+
 assert torch.cuda.is_available() and torch.cuda.get_device_capability() >= (8, 9)
 
-from torchao.prototype.moe_training.conversion_utils import MoETrainingConfig
+from torchao.prototype.moe_training.conversion_utils import (
+    MoEScalingType,
+    MoETrainingConfig,
+)
 from torchao.quantization.quant_api import quantize_
 
 # this example uses torchtitan llama4 MoE, see
 try:
-    from torchtitan.experiments.llama4.model.args import TransformerModelArgs
-    from torchtitan.experiments.llama4.model.moe import MoE
+    from torchtitan.models.moe import MoE, MoEArgs
+    from torchtitan.models.moe.utils import set_token_group_alignment_size_m
 except ImportError as e:
     raise ImportError(
         "torchtitan not installed, see installation instructions at https://github.com/pytorch/torchtitan"
     ) from e
 
 
+from argparse import ArgumentParser
+
+parser = ArgumentParser()
+parser.add_argument(
+    "--scaling_type",
+    type=str,
+    default="fp8_rowwise",
+    choices=["fp8_rowwise", "mxfp8"],
+)
+args = parser.parse_args()
+
+
 # initialize model
 device = torch.device("cuda")
-model_args = TransformerModelArgs(
-    moe_enabled=True,
-    num_experts=8,
-    dim=256,
-)
-model = MoE(model_args).to(torch.bfloat16).to(device)
+torch.manual_seed(42)
+model_args = MoEArgs(num_experts=8, top_k=2, use_grouped_mm=True)
+dim = 1024
+hidden_dim = dim * 4
+model = MoE(model_args, dim, hidden_dim).to(torch.bfloat16).to(device)
 init_std = 0.02
 model.init_weights(init_std, device)
 
@@ -40,14 +60,21 @@ def moe_module_filter_fn(mod: nn.Module, cur_fqn: str) -> bool:
     return False
 
 
-# quantize the model
-config = MoETrainingConfig()
+if args.scaling_type == "fp8_rowwise":
+    config = MoETrainingConfig()
+    alignment_size = 16
+
+elif args.scaling_type == "mxfp8":
+    config = MoETrainingConfig(scaling_type=MoEScalingType.MXFP8)
+    alignment_size = 32
+
 quantize_(model, config=config, filter_fn=moe_module_filter_fn)
+set_token_group_alignment_size_m(alignment_size)
 
 # training loop
 optimizer = torch.optim.AdamW(model.parameters(), lr=1e-3)
 for step in range(10):
-    batch, seq, dim = 8, 2048, 256
+    batch, seq = 8, 2048
     x = torch.randn(
         batch, seq, dim, dtype=torch.bfloat16, requires_grad=True, device=device
     )