diff --git a/tests/test_inference_result_xpu.py b/tests/test_inference_result_xpu.py
index 3498ea45c..73dd7faa3 100644
--- a/tests/test_inference_result_xpu.py
+++ b/tests/test_inference_result_xpu.py
@@ -25,16 +25,15 @@ class TestInferenceResultXPU(ModelTest):
             (BACKEND.TORCH, DEVICE.XPU, False),
         ]
     )
-    def testTritonXPU(self, backend, device, template):
+    def testTorchXPU(self, backend, device, template):
         origin_model = GPTQModel.load(
             self.NATIVE_MODEL_ID,
-            quantize_config=QuantizeConfig(),
+            quantize_config=QuantizeConfig(device=device),
             backend=backend,
-            device=device,
         )
         tokenizer = self.load_tokenizer(self.NATIVE_MODEL_ID)
         calibration_dataset = self.load_dataset(tokenizer, rows=128)
-        origin_model.quantize(calibration_dataset, backend=BACKEND.TRITON)
+        origin_model.quantize(calibration_dataset, backend=BACKEND.TORCH)
 
         with tempfile.TemporaryDirectory() as tmpdir:
           origin_model.save(tmpdir)
diff --git a/tests/test_triton_xpu.py b/tests/test_torch_xpu.py
similarity index 92%
rename from tests/test_triton_xpu.py
rename to tests/test_torch_xpu.py
index bb761ab62..0547b79af 100644
--- a/tests/test_triton_xpu.py
+++ b/tests/test_torch_xpu.py
@@ -18,25 +18,25 @@
 from gptqmodel.models._const import DEVICE  # noqa: E402
 
 
-class TestTritonXPU(ModelTest):
+class TestTorchXPU(ModelTest):
     NATIVE_MODEL_ID = "/monster/data/model/Llama-3.2-1B-Instruct"
 
     def test(self):
         origin_model = GPTQModel.load(
             self.NATIVE_MODEL_ID,
             quantize_config=QuantizeConfig(),
-            backend=BACKEND.TRITON,
+            backend=BACKEND.TORCH,
             device=DEVICE.XPU,
         )
         tokenizer = self.load_tokenizer(self.NATIVE_MODEL_ID)
         calibration_dataset = self.load_dataset(tokenizer, self.DATASET_SIZE)
-        origin_model.quantize(calibration_dataset, backend=BACKEND.TRITON)
+        origin_model.quantize(calibration_dataset, backend=BACKEND.TORCH)
         with tempfile.TemporaryDirectory() as tmpdir:
           origin_model.save(tmpdir)
 
           model = GPTQModel.load(
               tmpdir,
-              backend=BACKEND.TRITON,
+              backend=BACKEND.TORCH,
               device=DEVICE.XPU,
           )
           generate_str = tokenizer.decode(model.generate(**tokenizer("The capital of France is is", return_tensors="pt").to(model.device), max_new_tokens=2)[0])