diff --git a/tests/model_executor/test_fused_moe_marlin_backend.py b/tests/model_executor/test_fused_moe_marlin_backend.py
new file mode 100644
index 00000000000..97aef099b01
--- /dev/null
+++ b/tests/model_executor/test_fused_moe_marlin_backend.py
@@ -0,0 +1,87 @@
+"""
+# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+
+from types import SimpleNamespace
+
+import paddle
+
+from fastdeploy.model_executor.layers.moe import (
+    fused_moe_marlin_backend as marlin_backend,
+)
+
+paddle.set_device("gpu")
+
+
+class _DummyLayer(paddle.nn.Layer):
+    def __init__(self, hidden_size=64, moe_intermediate_size=32, topk_method="topk", num_local_experts=2):
+        super().__init__()
+        self.num_local_experts = num_local_experts
+        self.num_experts = num_local_experts
+        self.hidden_size = hidden_size
+        self.moe_intermediate_size = moe_intermediate_size
+        self.top_k = 1
+        self.n_group = 1
+        self.topk_group = 1
+        self.topk_method = topk_method
+        self.routed_scaling_factor = 1.0
+        self.gate_correction_bias = paddle.zeros([self.num_experts], dtype="float32")
+        self.renormalize = True
+        self.fd_config = SimpleNamespace()
+
+    def extract_moe_ffn_weights(self, state_dict):
+        return state_dict["up"], state_dict["down"], None, None
+
+
+def _make_weights(layer):
+    up = [
+        paddle.ones([layer.hidden_size, layer.moe_intermediate_size * 2], dtype="float16")
+        for _ in range(layer.num_local_experts)
+    ]
+    down = [
+        paddle.ones([layer.moe_intermediate_size, layer.hidden_size], dtype="float16")
+        for _ in range(layer.num_local_experts)
+    ]
+    return up, down
+
+
+def test_marlin_process_and_apply_paths(monkeypatch):
+    method = marlin_backend.MarlinWeightOnlyMoEMethod()
+    layer = _DummyLayer()
+
+    prev_dtype = paddle.get_default_dtype()
+    paddle.set_default_dtype("float16")
+    method.create_weights(layer)
+    paddle.set_default_dtype(prev_dtype)
+    up, down = _make_weights(layer)
+    method.process_loaded_weights(layer, {"up": up, "down": down})
+
+    scales = paddle.arange(128, dtype="float32").reshape([2, 64])
+    permuted = marlin_backend.marlin_permute_scales(scales, size_k=16, size_n=64, group_size=8)
+    assert permuted.shape == [2, 64]
+
+    gate = paddle.nn.Linear(layer.hidden_size, layer.num_experts, bias_attr=False)
+    x = paddle.ones([2, layer.hidden_size], dtype="float16")
+    monkeypatch.setattr(
+        marlin_backend,
+        "MoeWna16MarlinGemmApi",
+        lambda *_args, **kwargs: (paddle.zeros([kwargs["size_m"], kwargs["size_n"]], dtype=x.dtype),),
+    )
+    out = method.apply(layer, x, gate, topk_ids_hookfunc=lambda **_k: None)
+    assert out.shape == [2, layer.hidden_size]
+
+    layer.topk_method = "noaux_tc"
+    out_noaux = method.apply(layer, x, gate, topk_ids_hookfunc=lambda **_k: None)
+    assert out_noaux.shape == [2, layer.hidden_size]