diff --git a/tests/gpu/test_ocr.py b/tests/gpu/test_ocr.py
new file mode 100644
index 000000000..6ed3ba898
--- /dev/null
+++ b/tests/gpu/test_ocr.py
@@ -0,0 +1,171 @@
+# Copyright (c) 2025 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.
+
+import os
+import re
+import shutil
+import subprocess
+import tempfile
+import urllib.request
+
+
+import allure
+import yaml
+
+OUTPUT_DIR = "./output/"
+LOG_DIR = "./erniekit_dist_log/"
+MODEL_PATH = "./PaddleOCR-VL/"
+CONFIG_PATH = "./examples/configs/PaddleOCR-VL/"
+SFT_CONFIG_PATH = CONFIG_PATH + "sft/"
+PORT = 8188
+
+os.environ["NVIDIA_TF32_OVERRIDE"] = "0"
+os.environ["NCCL_ALGO"] = "Tree"
+os.environ["FLAGS_embedding_deterministic"] = "1"
+os.environ["FLAGS_cudnn_deterministic"] = "1"
+
+
+def clean_output_dir():
+    if os.path.exists(OUTPUT_DIR):
+        shutil.rmtree(OUTPUT_DIR)
+    if os.path.exists(LOG_DIR):
+        shutil.rmtree(LOG_DIR)
+
+
+def prepare_data():
+    data_dir = "examples/data"
+    os.makedirs(data_dir, exist_ok=True)
+
+    files = {
+        "ocr_vl_sft-train_Bengali.jsonl": "https://paddleformers.bj.bcebos.com/datasets/ocr_vl_sft-train_Bengali.jsonl",
+        "ocr_vl_sft-test_Bengali.jsonl": "https://paddleformers.bj.bcebos.com/datasets/ocr_vl_sft-test_Bengali.jsonl",
+    }
+
+    for filename, url in files.items():
+        file_path = os.path.join(data_dir, filename)
+
+        if not os.path.exists(file_path):
+            print(f"Downloading {filename} ...")
+            try:
+                urllib.request.urlretrieve(url, file_path)
+                print(f"Saved to {file_path}")
+            except Exception as e:
+                print(f"Failed to download {filename}: {e}")
+        else:
+            print(f"{filename} already exists, skip downloading.")
+
+
+def default_args(yaml_path):
+    with open(yaml_path, "r", encoding="utf-8") as f:
+        return yaml.safe_load(f)
+
+
+def run_update_config_training(config, steps="train"):
+    with tempfile.NamedTemporaryFile(
+        mode="w+", suffix=".yaml", delete=False
+    ) as temp_config:
+        yaml.dump(config, temp_config)
+        temp_config_path = temp_config.name
+    cmd = [
+        "erniekit",
+        steps,
+        temp_config_path,
+    ]
+    if steps == "export":
+        cmd.append("lora=True")
+
+    if steps == "server":
+        process = subprocess.Popen(
+            cmd,
+            stdout=subprocess.PIPE,
+            stderr=subprocess.STDOUT,
+            text=True,
+            preexec_fn=os.setsid,
+        )
+        return process
+    elif steps == "chat":
+        process = subprocess.Popen(
+            cmd,
+            stdout=subprocess.PIPE,
+            stderr=subprocess.STDOUT,
+            stdin=subprocess.PIPE,
+            text=True,
+            bufsize=1,
+        )
+        return process
+    else:
+        result = subprocess.run(
+            cmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, text=True
+        )
+        return result.returncode, result.stdout
+
+
+def assert_result(ret_code, log_output):
+    """assert result"""
+    if ret_code != 0:
+        print("\n".join(log_output.strip().splitlines()[-30:]))
+        raise AssertionError("Training Failed")
+
+
+def assert_loss(base_loss):
+    """
+    Calculate the average loss from the log file, and compare it with the expected value.
+    """
+    log_path = os.path.join(os.getcwd(), "erniekit_dist_log", "workerlog.0")
+    loss_pattern = re.compile(r"- loss:\s*([0-9]+\.[0-9]+)")
+    with open(log_path, encoding="utf-8") as f:
+        content = f.read()
+    losses = [float(m.group(1)) for m in loss_pattern.finditer(content)]
+
+    if losses:
+        sum_loss = sum(losses) / len(losses)
+        avg_loss = round(sum_loss, 6)
+    else:
+        avg_loss = 0
+
+    assert (
+        abs(avg_loss - base_loss) <= 0.0001
+    ), f"loss: {avg_loss}, base_loss: {base_loss}, exist diff!"
+
+
+def attach_log_file():
+    log_path = os.path.join(os.getcwd(), "erniekit_dist_log", "workerlog.0")
+    if os.path.exists(log_path):
+        allure.attach.file(
+            log_path, name="Trainning Log", attachment_type=allure.attachment_type.TEXT
+        )
+    else:
+        allure.attach(
+            f"Log file was not generated: {log_path}",
+            name="Log Missing",
+            attachment_type=allure.attachment_type.TEXT,
+        )
+
+
+def test_sft():
+    clean_output_dir()
+    prepare_data()
+    yaml_path = os.path.join(SFT_CONFIG_PATH, "run_ocr_vl_sft_16k.yaml")
+    config = default_args(yaml_path).copy()
+    config["max_steps"] = 3
+    config["save_steps"] = 2
+    config["model_name_or_path"] = MODEL_PATH
+    config["output_dir"] = OUTPUT_DIR
+
+    ret_code, err_log = run_update_config_training(config)
+    attach_log_file()
+    assert_result(ret_code, err_log)
+
+    base_loss = 5.402314
+    assert_loss(base_loss)