diff --git a/scripts/run_ci.sh b/scripts/run_ci.sh
index 6b7f40071..0d2c76108 100644
--- a/scripts/run_ci.sh
+++ b/scripts/run_ci.sh
@@ -9,21 +9,54 @@ python -m pip install jsonschema aistudio_sdk==0.2.6
 bash build.sh || exit 1
 
 failed_files=()
-run_path="$DIR/../test/ci_use"
-pushd "$run_path" || exit 1  # 目录不存在时退出
+run_path="$DIR/../test/ci_use/"
 
-for file in test_*; do
-    if [ -f "$file" ]; then
-        abs_path=$(realpath "$file")
-        echo "Running pytest on $abs_path"
-        if ! python -m pytest -sv "$abs_path"; then
-            echo "Test failed: $file"
-            failed_files+=("$file")
-        fi
+# load all test files
+for subdir in "$run_path"*/; do
+    if [ -d "$subdir" ]; then
+        pushd "$subdir" > /dev/null || continue  # into test dir or continue
+
+        # search for test_*.py files
+        for file in test_*.py; do
+            if [ -f "$file" ]; then
+                echo "============================================================"
+                echo "Running pytest on $(realpath "$file")"
+                echo "------------------------------------------------------------"
+
+                set +e
+                timeout 360 python -m pytest --disable-warnings -sv "$file"
+                exit_code=$?
+                set -e
+
+                if [ $exit_code -ne 0 ]; then
+                    if [ -f "${subdir%/}/log/workerlog.0" ]; then
+                        echo "---------------- log/workerlog.0 -------------------"
+                        cat "${subdir%/}/log/workerlog.0"
+                        echo "----------------------------------------------------"
+                    fi
+
+                    if [ -f "${subdir%/}/server.log" ]; then
+                        echo "---------------- server.log ----------------"
+                        cat "${subdir%/}/server.log"
+                        echo "--------------------------------------------"
+                    fi
+
+                    if [ "$exit_code" -eq 1 ] || [ "$exit_code" -eq 124 ]; then
+                        echo "[ERROR] $file 起服务或执行异常，exit_code=$exit_code"
+                        if [ "$exit_code" -eq 124 ]; then
+                            echo "[TIMEOUT] $file 脚本执行超过 6 分钟, 任务超时退出！"
+                        fi
+                    fi
+
+                    failed_files+=("$subdir$file")
+                    exit 1
+                fi
+                echo "------------------------------------------------------------"
+            fi
+        done
+        popd > /dev/null  # back to test dir
     fi
 done
-popd
-
 
 if [ ${#failed_files[@]} -gt 0 ]; then
     echo "The following tests failed:"
diff --git a/test/ci_use/EB_Lite/test_EB_Lite_serving.py b/test/ci_use/EB_Lite/test_EB_Lite_serving.py
new file mode 100644
index 000000000..82c9e634e
--- /dev/null
+++ b/test/ci_use/EB_Lite/test_EB_Lite_serving.py
@@ -0,0 +1,323 @@
+# 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 pytest
+import requests
+import time
+import subprocess
+import socket
+import os
+import signal
+import sys
+import openai
+
+# Read ports from environment variables; use default values if not set
+FD_API_PORT = int(os.getenv("FD_API_PORT", 8188))
+FD_ENGINE_QUEUE_PORT = int(os.getenv("FD_ENGINE_QUEUE_PORT", 8133))
+FD_METRICS_PORT = int(os.getenv("FD_METRICS_PORT", 8233))
+
+# List of ports to clean before and after tests
+PORTS_TO_CLEAN = [FD_API_PORT, FD_ENGINE_QUEUE_PORT, FD_METRICS_PORT]
+
+def is_port_open(host: str, port: int, timeout=1.0):
+    """
+    Check if a TCP port is open on the given host.
+    Returns True if connection succeeds, False otherwise.
+    """
+    try:
+        with socket.create_connection((host, port), timeout):
+            return True
+    except Exception:
+        return False
+
+def kill_process_on_port(port: int):
+    """
+    Kill processes that are listening on the given port.
+    Uses `lsof` to find process ids and sends SIGKILL.
+    """
+    try:
+        output = subprocess.check_output("lsof -i:{} -t".format(port), shell=True).decode().strip()
+        for pid in output.splitlines():
+            os.kill(int(pid), signal.SIGKILL)
+            print("Killed process on port {}, pid={}".format(port, pid))
+    except subprocess.CalledProcessError:
+        pass
+
+def clean_ports():
+    """
+    Kill all processes occupying the ports listed in PORTS_TO_CLEAN.
+    """
+    for port in PORTS_TO_CLEAN:
+        kill_process_on_port(port)
+
+@pytest.fixture(scope="session", autouse=True)
+def setup_and_run_server():
+    """
+    Pytest fixture that runs once per test session:
+    - Cleans ports before tests
+    - Starts the API server as a subprocess
+    - Waits for server port to open (up to 30 seconds)
+    - Tears down server after all tests finish
+    """
+    print("Pre-test port cleanup...")
+    clean_ports()
+
+    base_path = os.getenv("MODEL_PATH")
+    if base_path:
+        model_path = os.path.join(base_path, "ernie-4_5-21b-a3b-bf16-paddle")
+    else:
+        model_path = "./ernie-4_5-21b-a3b-bf16-paddle"
+
+    log_path = "server.log"
+    cmd = [
+        sys.executable, "-m", "fastdeploy.entrypoints.openai.api_server",
+        "--model", model_path,
+        "--port", str(FD_API_PORT),
+        "--tensor-parallel-size", "1",
+        "--engine-worker-queue-port", str(FD_ENGINE_QUEUE_PORT),
+        "--metrics-port", str(FD_METRICS_PORT),
+        "--max-model-len", "32768",
+        "--max-num-seqs", "128",
+        "--quantization", "wint4",
+    ]
+
+    # Set environment variables
+    env = os.environ.copy()
+    env["ENABLE_FASTDEPLOY_LOAD_MODEL_CONCURRENCY"] = "0"
+    env["FLAGS_use_append_attn"] = "1"
+    env["ELLM_DYNAMIC_MODE"] = "1"
+    env["NCCL_ALGO"] = "Ring"
+    env["USE_WORKER_V1"] = "1"
+
+    # Start subprocess in new process group
+    with open(log_path, "w") as logfile:
+        process = subprocess.Popen(
+            cmd,
+            env=env,
+            stdout=logfile,
+            stderr=subprocess.STDOUT,
+            start_new_session=True  # Enables killing full group via os.killpg
+        )
+
+    # Wait up to 300 seconds for API server to be ready
+    for _ in range(300):
+        if is_port_open("127.0.0.1", FD_API_PORT):
+            print("API server is up on port {}".format(FD_API_PORT))
+            break
+        time.sleep(1)
+    else:
+        print("[TIMEOUT] API server failed to start in 5 minutes. Cleaning up...")
+        try:
+            os.killpg(process.pid, signal.SIGTERM)
+        except Exception as e:
+            print("Failed to kill process group: {}".format(e))
+        raise RuntimeError("API server did not start on port {}".format(FD_API_PORT))
+
+    yield  # Run tests
+
+    print("\n===== Post-test server cleanup... =====")
+    try:
+        os.killpg(process.pid, signal.SIGTERM)
+        print("API server (pid={}) terminated".format(process.pid))
+    except Exception as e:
+        print("Failed to terminate API server: {}".format(e))
+
+
+@pytest.fixture(scope="session")
+def api_url(request):
+    """
+    Returns the API endpoint URL for chat completions.
+    """
+    return "http://0.0.0.0:{}/v1/chat/completions".format(FD_API_PORT)
+
+
+@pytest.fixture(scope="session")
+def metrics_url(request):
+    """
+    Returns the metrics endpoint URL.
+    """
+    return "http://0.0.0.0:{}/metrics".format(FD_METRICS_PORT)
+
+
+@pytest.fixture
+def headers():
+    """
+    Returns common HTTP request headers.
+    """
+    return {"Content-Type": "application/json"}
+
+
+@pytest.fixture
+def consistent_payload():
+    """
+    Returns a fixed payload for consistency testing,
+    including a fixed random seed and temperature.
+    """
+    return {
+        "messages": [{"role": "user", "content": "用一句话介绍 PaddlePaddle"}],
+        "temperature": 0.9,
+        "top_p": 0,  # fix top_p to reduce randomness
+        "seed": 13  # fixed random seed
+    }
+
+# ==========================
+# Helper function to calculate difference rate between two texts
+# ==========================
+def calculate_diff_rate(text1, text2):
+    """
+    Calculate the difference rate between two strings
+    based on the normalized Levenshtein edit distance.
+    Returns a float in [0,1], where 0 means identical.
+    """
+    if text1 == text2:
+        return 0.0
+
+    len1, len2 = len(text1), len(text2)
+    dp = [[0] * (len2 + 1) for _ in range(len1 + 1)]
+
+    for i in range(len1 + 1):
+        for j in range(len2 + 1):
+            if i == 0 or j == 0:
+                dp[i][j] = i + j
+            elif text1[i - 1] == text2[j - 1]:
+                dp[i][j] = dp[i - 1][j - 1]
+            else:
+                dp[i][j] = 1 + min(dp[i - 1][j], dp[i][j - 1], dp[i - 1][j - 1])
+
+    edit_distance = dp[len1][len2]
+    max_len = max(len1, len2)
+    return edit_distance / max_len if max_len > 0 else 0.0
+
+# ==========================
+# Consistency test for repeated runs with fixed payload
+# ==========================
+def test_consistency_between_runs(api_url, headers, consistent_payload):
+    """
+    Test that two runs with the same fixed input produce similar outputs.
+    """
+    # First request
+    resp1 = requests.post(api_url, headers=headers, json=consistent_payload)
+    assert resp1.status_code == 200
+    result1 = resp1.json()
+    content1 = result1["choices"][0]["message"]["content"]
+
+    # Second request
+    resp2 = requests.post(api_url, headers=headers, json=consistent_payload)
+    assert resp2.status_code == 200
+    result2 = resp2.json()
+    content2 = result2["choices"][0]["message"]["content"]
+
+    # Calculate difference rate
+    diff_rate = calculate_diff_rate(content1, content2)
+
+    # Verify that the difference rate is below the threshold
+    assert diff_rate < 0.05, "Output difference too large ({:.4%})".format(diff_rate)
+
+# ==========================
+# OpenAI Client chat.completions Test
+# ==========================
+
+@pytest.fixture
+def openai_client():
+    ip = "0.0.0.0"
+    service_http_port = str(FD_API_PORT)
+    client = openai.Client(
+        base_url="http://{}:{}/v1".format(ip, service_http_port),
+        api_key="EMPTY_API_KEY"
+    )
+    return client
+
+# Non-streaming test
+def test_non_streaming_chat(openai_client):
+    """
+    Test non-streaming chat functionality with the local service
+    """
+    response = openai_client.chat.completions.create(
+        model="default",
+        messages=[
+            {"role": "system", "content": "You are a helpful AI assistant."},
+            {"role": "user", "content": "List 3 countries and their capitals."},
+        ],
+        temperature=1,
+        max_tokens=1024,
+        stream=False,
+    )
+
+    assert hasattr(response, 'choices')
+    assert len(response.choices) > 0
+    assert hasattr(response.choices[0], 'message')
+    assert hasattr(response.choices[0].message, 'content')
+
+# Streaming test
+def test_streaming_chat(openai_client, capsys):
+    """
+    Test streaming chat functionality with the local service
+    """
+    response = openai_client.chat.completions.create(
+        model="default",
+        messages=[
+            {"role": "system", "content": "You are a helpful AI assistant."},
+            {"role": "user", "content": "List 3 countries and their capitals."},
+            {"role": "assistant", "content": "China(Beijing), France(Paris), Australia(Canberra)."},
+            {"role": "user", "content": "OK, tell more."},
+        ],
+        temperature=1,
+        max_tokens=1024,
+        stream=True,
+    )
+
+    output = []
+    for chunk in response:
+        if hasattr(chunk.choices[0], 'delta') and hasattr(chunk.choices[0].delta, 'content'):
+            output.append(chunk.choices[0].delta.content)
+    assert len(output) > 2
+
+# ==========================
+# OpenAI Client completions Test
+# ==========================
+
+def test_non_streaming(openai_client):
+    """
+    Test non-streaming chat functionality with the local service
+    """
+    response = openai_client.completions.create(
+        model="default",
+        prompt="Hello, how are you?",
+        temperature=1,
+        max_tokens=1024,
+        stream=False,
+    )
+
+    # Assertions to check the response structure
+    assert hasattr(response, 'choices')
+    assert len(response.choices) > 0
+
+
+def test_streaming(openai_client, capsys):
+    """
+    Test streaming functionality with the local service
+    """
+    response = openai_client.completions.create(
+        model="default",
+        prompt="Hello, how are you?",
+        temperature=1,
+        max_tokens=1024,
+        stream=True,
+    )
+
+    # Collect streaming output
+    output = []
+    for chunk in response:
+        output.append(chunk.choices[0].text)
+    assert len(output) > 0
\ No newline at end of file
diff --git a/test/ci_use/EB_VL_Lite/test_EB_VL_Lite_serving.py b/test/ci_use/EB_VL_Lite/test_EB_VL_Lite_serving.py
new file mode 100644
index 000000000..a68313d4d
--- /dev/null
+++ b/test/ci_use/EB_VL_Lite/test_EB_VL_Lite_serving.py
@@ -0,0 +1,331 @@
+# 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 pytest
+import requests
+import time
+import json
+import subprocess
+import socket
+import os
+import signal
+import sys
+import openai
+
+
+# Read ports from environment variables; use default values if not set
+FD_API_PORT = int(os.getenv("FD_API_PORT", 8188))
+FD_ENGINE_QUEUE_PORT = int(os.getenv("FD_ENGINE_QUEUE_PORT", 8133))
+FD_METRICS_PORT = int(os.getenv("FD_METRICS_PORT", 8233))
+
+# List of ports to clean before and after tests
+PORTS_TO_CLEAN = [FD_API_PORT, FD_ENGINE_QUEUE_PORT, FD_METRICS_PORT]
+
+def is_port_open(host: str, port: int, timeout=1.0):
+    """
+    Check if a TCP port is open on the given host.
+    Returns True if connection succeeds, False otherwise.
+    """
+    try:
+        with socket.create_connection((host, port), timeout):
+            return True
+    except Exception:
+        return False
+
+def kill_process_on_port(port: int):
+    """
+    Kill processes that are listening on the given port.
+    Uses `lsof` to find process ids and sends SIGKILL.
+    """
+    try:
+        output = subprocess.check_output("lsof -i:{} -t".format(port), shell=True).decode().strip()
+        for pid in output.splitlines():
+            os.kill(int(pid), signal.SIGKILL)
+            print("Killed process on port {}, pid={}".format(port, pid))
+    except subprocess.CalledProcessError:
+        pass
+
+def clean_ports():
+    """
+    Kill all processes occupying the ports listed in PORTS_TO_CLEAN.
+    """
+    for port in PORTS_TO_CLEAN:
+        kill_process_on_port(port)
+
+@pytest.fixture(scope="session", autouse=True)
+def setup_and_run_server():
+    """
+    Pytest fixture that runs once per test session:
+    - Cleans ports before tests
+    - Starts the API server as a subprocess
+    - Waits for server port to open (up to 30 seconds)
+    - Tears down server after all tests finish
+    """
+    print("Pre-test port cleanup...")
+    clean_ports()
+
+    base_path = os.getenv("MODEL_PATH")
+    if base_path:
+        model_path=os.path.join(base_path, "ernie-4_5-vl-28b-a3b-bf16-paddle")
+    else:
+        model_path="./ernie-4_5-vl-28b-a3b-bf16-paddle"
+
+    log_path = "server.log"
+    limit_mm_str = json.dumps({"image": 100, "video": 100})
+
+    cmd = [
+        sys.executable, "-m", "fastdeploy.entrypoints.openai.api_server",
+        "--model", model_path,
+        "--port", str(FD_API_PORT),
+        "--tensor-parallel-size", "1",
+        "--engine-worker-queue-port", str(FD_ENGINE_QUEUE_PORT),
+        "--metrics-port", str(FD_METRICS_PORT),
+        "--enable-mm",
+        "--max-model-len", "32768",
+        "--max-num-batched-tokens", "384",
+        "--max-num-seqs", "128",
+        "--limit-mm-per-prompt", limit_mm_str,
+        "--enable-chunked-prefill",
+        "--kv-cache-ratio", "0.71",
+        "--quantization", "wint4"
+    ]
+
+    # Set environment variables
+    env = os.environ.copy()
+    env["ENABLE_FASTDEPLOY_LOAD_MODEL_CONCURRENCY"] = "0"
+    env["NCCL_ALGO"] = "Ring"
+
+    # Start subprocess in new process group
+    with open(log_path, "w") as logfile:
+        process = subprocess.Popen(
+            cmd,
+            env=env,
+            stdout=logfile,
+            stderr=subprocess.STDOUT,
+            start_new_session=True  # Enables killing full group via os.killpg
+        )
+
+    # Wait up to 300 seconds for API server to be ready
+    for _ in range(300):
+        if is_port_open("127.0.0.1", FD_API_PORT):
+            print("API server is up on port {}".format(FD_API_PORT))
+            break
+        time.sleep(1)
+    else:
+        print("[TIMEOUT] API server failed to start in 5 minutes. Cleaning up...")
+        try:
+            os.killpg(process.pid, signal.SIGTERM)
+        except Exception as e:
+            print("Failed to kill process group: {}".format(e))
+        raise RuntimeError("API server did not start on port {}".format(FD_API_PORT))
+
+    yield  # Run tests
+
+    print("\n===== Post-test server cleanup... =====")
+    try:
+        os.killpg(process.pid, signal.SIGTERM)
+        print("API server (pid={}) terminated".format(process.pid))
+    except Exception as e:
+        print("Failed to terminate API server: {}".format(e))
+
+
+@pytest.fixture(scope="session")
+def api_url(request):
+    """
+    Returns the API endpoint URL for chat completions.
+    """
+    return "http://0.0.0.0:{}/v1/chat/completions".format(FD_API_PORT)
+
+
+@pytest.fixture(scope="session")
+def metrics_url(request):
+    """
+    Returns the metrics endpoint URL.
+    """
+    return "http://0.0.0.0:{}/metrics".format(FD_METRICS_PORT)
+
+
+@pytest.fixture
+def headers():
+    """
+    Returns common HTTP request headers.
+    """
+    return {"Content-Type": "application/json"}
+
+
+@pytest.fixture
+def consistent_payload():
+    """
+    Returns a fixed payload for consistency testing,
+    including a fixed random seed and temperature.
+    """
+    return {
+        "messages": [
+            {"role": "user", "content": [
+            {"type": "image_url", "image_url": {"url": "https://ku.baidu-int.com/vk-assets-ltd/space/2024/09/13/933d1e0a0760498e94ec0f2ccee865e0", "detail": "high"}},
+            {"type": "text", "text": "请描述图片内容"}
+            ]}
+        ],
+        "temperature": 0.8,
+        "top_p": 0,  # fix top_p to reduce randomness
+        "seed": 13  # fixed random seed
+    }
+
+# ==========================
+# Helper function to calculate difference rate between two texts
+# ==========================
+def calculate_diff_rate(text1, text2):
+    """
+    Calculate the difference rate between two strings
+    based on the normalized Levenshtein edit distance.
+    Returns a float in [0,1], where 0 means identical.
+    """
+    if text1 == text2:
+        return 0.0
+
+    len1, len2 = len(text1), len(text2)
+    dp = [[0] * (len2 + 1) for _ in range(len1 + 1)]
+
+    for i in range(len1 + 1):
+        for j in range(len2 + 1):
+            if i == 0 or j == 0:
+                dp[i][j] = i + j
+            elif text1[i - 1] == text2[j - 1]:
+                dp[i][j] = dp[i - 1][j - 1]
+            else:
+                dp[i][j] = 1 + min(dp[i - 1][j], dp[i][j - 1], dp[i - 1][j - 1])
+
+    edit_distance = dp[len1][len2]
+    max_len = max(len1, len2)
+    return edit_distance / max_len if max_len > 0 else 0.0
+
+# ==========================
+# Consistency test for repeated runs with fixed payload
+# ==========================
+def test_consistency_between_runs(api_url, headers, consistent_payload):
+    """
+    Test that two runs with the same fixed input produce similar outputs.
+    """
+    # First request
+    resp1 = requests.post(api_url, headers=headers, json=consistent_payload)
+    assert resp1.status_code == 200
+    result1 = resp1.json()
+    content1 = result1["choices"][0]["message"]["content"]
+
+    # Second request
+    resp2 = requests.post(api_url, headers=headers, json=consistent_payload)
+    assert resp2.status_code == 200
+    result2 = resp2.json()
+    content2 = result2["choices"][0]["message"]["content"]
+
+    # Calculate difference rate
+    diff_rate = calculate_diff_rate(content1, content2)
+
+    # Verify that the difference rate is below the threshold
+    assert diff_rate < 0.05, "Output difference too large ({:.4%})".format(diff_rate)
+
+# ==========================
+# OpenAI Client Chat Completion Test
+# ==========================
+
+@pytest.fixture
+def openai_client():
+    ip = "0.0.0.0"
+    service_http_port = str(FD_API_PORT)
+    client = openai.Client(
+        base_url = "http://{}:{}/v1".format(ip, service_http_port),
+        api_key="EMPTY_API_KEY"
+    )
+    return client
+
+# Non-streaming test
+def test_non_streaming_chat(openai_client):
+    """Test non-streaming chat functionality with the local service"""
+    response = openai_client.chat.completions.create(
+        model="default",
+        messages=[
+            {
+                "role": "system",
+                "content": "You are a helpful AI assistant."
+            },  # system不是必需，可选
+            {
+                "role":
+                "user",
+                "content": [{
+                    "type": "image_url",
+                    "image_url": {
+                        "url":
+                        "https://ku.baidu-int.com/vk-assets-ltd/space/2024/09/13/933d1e0a0760498e94ec0f2ccee865e0",
+                        "detail": "high"
+                    }
+                }, {
+                    "type": "text",
+                    "text": "请描述图片内容"
+                }]
+            }
+        ],
+        temperature=1,
+        max_tokens=53,
+        stream=False,
+    )
+
+    assert hasattr(response, 'choices')
+    assert len(response.choices) > 0
+    assert hasattr(response.choices[0], 'message')
+    assert hasattr(response.choices[0].message, 'content')
+
+# Streaming test
+def test_streaming_chat(openai_client, capsys):
+    """Test streaming chat functionality with the local service"""
+    response = openai_client.chat.completions.create(
+        model="default",
+        messages=[
+            {
+                "role": "system",
+                "content": "You are a helpful AI assistant."
+            },  # system不是必需，可选
+            {
+                "role": "user",
+                "content": "List 3 countries and their capitals."
+            },
+            {
+                "role": "assistant",
+                "content": "China(Beijing), France(Paris), Australia(Canberra)."
+            },
+            {
+                "role":
+                "user",
+                "content": [{
+                    "type": "image_url",
+                    "image_url": {
+                        "url":
+                        "https://ku.baidu-int.com/vk-assets-ltd/space/2024/09/13/933d1e0a0760498e94ec0f2ccee865e0",
+                        "detail": "high"
+                    }
+                }, {
+                    "type": "text",
+                    "text": "请描述图片内容"
+                }]
+            },
+        ],
+        temperature=1,
+        max_tokens=512,
+        stream=True,
+    )
+
+    output = []
+    for chunk in response:
+        if hasattr(chunk.choices[0], 'delta') and hasattr(chunk.choices[0].delta, 'content'):
+            output.append(chunk.choices[0].delta.content)
+    assert len(output) > 2
\ No newline at end of file
diff --git a/test/ci_use/test_qwen2_offline.py b/test/ci_use/Qwen2-7B-Instruct_offline/test_Qwen2-7B-Instruct_offline.py
similarity index 80%
rename from test/ci_use/test_qwen2_offline.py
rename to test/ci_use/Qwen2-7B-Instruct_offline/test_Qwen2-7B-Instruct_offline.py
index f2443934e..dc7f97070 100644
--- a/test/ci_use/test_qwen2_offline.py
+++ b/test/ci_use/Qwen2-7B-Instruct_offline/test_Qwen2-7B-Instruct_offline.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved.
+# 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.
@@ -18,8 +18,24 @@ from fastdeploy import LLM, SamplingParams
 import os
 import subprocess
 import signal
+import time
+import socket
+
 
 FD_ENGINE_QUEUE_PORT = int(os.getenv("FD_ENGINE_QUEUE_PORT", 8313))
+MAX_WAIT_SECONDS = 60
+
+def is_port_open(host: str, port: int, timeout=1.0):
+    """
+    Check if a TCP port is open on the given host.
+    Returns True if connection succeeds, False otherwise.
+    """
+    try:
+        with socket.create_connection((host, port), timeout):
+            return True
+    except Exception:
+        return False
+
 
 def format_chat_prompt(messages):
     """
@@ -49,27 +65,39 @@ def model_path():
     else:
         return "./Qwen2-7B-Instruct"
 
+
 @pytest.fixture(scope="module")
 def llm(model_path):
     """
     Fixture to initialize the LLM model with a given model path
     """
     try:
-        output = subprocess.check_output(f"lsof -i:{FD_ENGINE_QUEUE_PORT} -t", shell=True).decode().strip()
+        output = subprocess.check_output("lsof -i:{} -t".format(FD_ENGINE_QUEUE_PORT), shell=True).decode().strip()
         for pid in output.splitlines():
             os.kill(int(pid), signal.SIGKILL)
-            print(f"Killed process on port {FD_ENGINE_QUEUE_PORT}, pid={pid}")
+            print("Killed process on port {}, pid={}".format(FD_ENGINE_QUEUE_PORT, pid))
     except subprocess.CalledProcessError:
         pass
 
     try:
+        start = time.time()
         llm = LLM(
             model=model_path,
             tensor_parallel_size=1,
             engine_worker_queue_port=FD_ENGINE_QUEUE_PORT,
-            max_model_len=4096
+            max_model_len=32768,
+            quantization="wint8"
         )
-        print("Model loaded successfully from {}.".format(model_path))
+
+        # Wait for the port to be open
+        wait_start = time.time()
+        while not is_port_open("127.0.0.1", FD_ENGINE_QUEUE_PORT):
+            if time.time() - wait_start > MAX_WAIT_SECONDS:
+                pytest.fail("Model engine did not start within {} seconds on port {}".format(
+                    MAX_WAIT_SECONDS, FD_ENGINE_QUEUE_PORT))
+            time.sleep(1)
+
+        print("Model loaded successfully from {} in {:.2f}s.".format(model_path, time.time() - start))
         yield llm
     except Exception:
         print("Failed to load model from {}.".format(model_path))
@@ -84,8 +112,8 @@ def test_generate_prompts(llm):
     # Only one prompt enabled for testing currently
     prompts = [
         "请介绍一下中国的四大发明。",
-        # "太阳和地球之间的距离是多少？",
-        # "写一首关于春天的古风诗。",
+        "太阳和地球之间的距离是多少？",
+        "写一首关于春天的古风诗。",
     ]
 
     sampling_params = SamplingParams(
diff --git a/test/ci_use/test_qwen2_serving.py b/test/ci_use/Qwen2-7B-Instruct_serving/test_Qwen2-7B-Instruct_serving.py
similarity index 55%
rename from test/ci_use/test_qwen2_serving.py
rename to test/ci_use/Qwen2-7B-Instruct_serving/test_Qwen2-7B-Instruct_serving.py
index 4f79ea834..76e9bbc38 100644
--- a/test/ci_use/test_qwen2_serving.py
+++ b/test/ci_use/Qwen2-7B-Instruct_serving/test_Qwen2-7B-Instruct_serving.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved.
+# 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.
@@ -18,18 +18,18 @@ import time
 import json
 from jsonschema import validate
 import concurrent.futures
-import numpy as np
 import subprocess
 import socket
 import os
 import signal
 import sys
+import openai
 
 
 # Read ports from environment variables; use default values if not set
-FD_API_PORT = int(os.getenv("FD_API_PORT", 8189))
-FD_ENGINE_QUEUE_PORT = int(os.getenv("FD_ENGINE_QUEUE_PORT", 8013))
-FD_METRICS_PORT = int(os.getenv("FD_METRICS_PORT", 8333))
+FD_API_PORT = int(os.getenv("FD_API_PORT", 8188))
+FD_ENGINE_QUEUE_PORT = int(os.getenv("FD_ENGINE_QUEUE_PORT", 8133))
+FD_METRICS_PORT = int(os.getenv("FD_METRICS_PORT", 8233))
 
 # List of ports to clean before and after tests
 PORTS_TO_CLEAN = [FD_API_PORT, FD_ENGINE_QUEUE_PORT, FD_METRICS_PORT]
@@ -51,10 +51,10 @@ def kill_process_on_port(port: int):
     Uses `lsof` to find process ids and sends SIGKILL.
     """
     try:
-        output = subprocess.check_output(f"lsof -i:{port} -t", shell=True).decode().strip()
+        output = subprocess.check_output("lsof -i:{} -t".format(port), shell=True).decode().strip()
         for pid in output.splitlines():
             os.kill(int(pid), signal.SIGKILL)
-            print(f"Killed process on port {port}, pid={pid}")
+            print("Killed process on port {}, pid={}".format(port, pid))
     except subprocess.CalledProcessError:
         pass
 
@@ -83,46 +83,58 @@ def setup_and_run_server():
     else:
         model_path="./Qwen2-7B-Instruct"
 
-    log_path = "api_server.log"
+    log_path = "server.log"
     cmd = [
         sys.executable, "-m", "fastdeploy.entrypoints.openai.api_server",
         "--model", model_path,
         "--port", str(FD_API_PORT),
         "--tensor-parallel-size", "1",
         "--engine-worker-queue-port", str(FD_ENGINE_QUEUE_PORT),
-        "--metrics-port", str(FD_METRICS_PORT)
+        "--metrics-port", str(FD_METRICS_PORT),
+        "--max-model-len", "32768",
+        "--max-num-seqs", "128",
+        "--quantization", "wint8"
     ]
 
+    # Start subprocess in new process group
     with open(log_path, "w") as logfile:
-        process = subprocess.Popen(cmd, stdout=logfile, stderr=subprocess.STDOUT)
+        process = subprocess.Popen(
+            cmd,
+            stdout=logfile,
+            stderr=subprocess.STDOUT,
+            start_new_session=True  # Enables killing full group via os.killpg
+        )
 
-    # Wait up to 120 seconds for API server port to become available
-    for _ in range(120):
+    # Wait up to 300 seconds for API server to be ready
+    for _ in range(300):
         if is_port_open("127.0.0.1", FD_API_PORT):
-            print(f"API server is up on port {FD_API_PORT}")
+            print("API server is up on port {}".format(FD_API_PORT))
             break
         time.sleep(1)
     else:
-        process.terminate()
-        raise RuntimeError(f"API server did not start on port {FD_API_PORT}")
+        print("[TIMEOUT] API server failed to start in 5 minutes. Cleaning up...")
+        try:
+            os.killpg(process.pid, signal.SIGTERM)
+        except Exception as e:
+            print("Failed to kill process group: {}".format(e))
+        raise RuntimeError("API server did not start on port {}".format(FD_API_PORT))
 
-    yield
+    yield  # Run tests
 
-    print("Post-test server cleanup...")
+    print("\n===== Post-test server cleanup... =====")
     try:
-        os.kill(process.pid, signal.SIGTERM)
-        print("API server terminated")
+        os.killpg(process.pid, signal.SIGTERM)
+        print("API server (pid={}) terminated".format(process.pid))
     except Exception as e:
-        print(f"Failed to kill server: {e}")
+        print("Failed to terminate API server: {}".format(e))
 
-    clean_ports()
 
 @pytest.fixture(scope="session")
 def api_url(request):
     """
     Returns the API endpoint URL for chat completions.
     """
-    return f"http://0.0.0.0:{FD_API_PORT}" + "/v1/chat/completions"
+    return "http://0.0.0.0:{}/v1/chat/completions".format(FD_API_PORT)
 
 
 @pytest.fixture(scope="session")
@@ -130,7 +142,7 @@ def metrics_url(request):
     """
     Returns the metrics endpoint URL.
     """
-    return f"http://0.0.0.0:{FD_METRICS_PORT}/metrics"
+    return "http://0.0.0.0:{}/metrics".format(FD_METRICS_PORT)
 
 
 @pytest.fixture
@@ -222,7 +234,6 @@ def calculate_diff_rate(text1, text2):
 valid_prompts = [
     [{"role": "user", "content": "你好"}],
     [{"role": "user", "content": "用一句话介绍 FastDeploy"}],
-    [{"role": "user", "content": "今天天气怎么样？"}],
 ]
 
 @pytest.mark.parametrize("messages", valid_prompts)
@@ -230,13 +241,10 @@ def test_valid_chat(messages, api_url, headers):
     """
     Test valid chat requests.
     """
-    start = time.time()
     resp = requests.post(api_url, headers=headers, json={"messages": messages})
-    duration = time.time() - start
 
     assert resp.status_code == 200
     validate(instance=resp.json(), schema=chat_response_schema)
-    assert duration < 5, "Response too slow：{:.2f}s".format(duration)
 
 # ==========================
 # Consistency test for repeated runs with fixed payload
@@ -261,7 +269,7 @@ def test_consistency_between_runs(api_url, headers, consistent_payload):
     diff_rate = calculate_diff_rate(content1, content2)
 
     # Verify that the difference rate is below the threshold
-    assert diff_rate < 0.05, f"Output difference too large ({diff_rate:.4%})"
+    assert diff_rate < 0.05, "Output difference too large ({:.4%})".format(diff_rate)
 
 # ==========================
 # Invalid prompt tests
@@ -303,7 +311,6 @@ def test_exceed_context_length(api_url, headers):
     # Check if the response indicates a token limit error or server error (500)
     try:
         response_json = resp.json()
-        print("Response JSON content:", json.dumps(response_json, ensure_ascii=False)[:1000])
     except Exception:
         response_json = {}
 
@@ -311,63 +318,6 @@ def test_exceed_context_length(api_url, headers):
     assert resp.status_code != 200 or "token" in json.dumps(response_json).lower(), \
         "Expected token limit error or similar, but got a normal response: {}".format(response_json)
 
-# ==========================
-# ChatTemplate Valid Structure Test
-# ==========================
-
-chat_template_cases = [
-    {"template": "chatml", "messages": [{"role": "user", "content": "你是谁？"}]},
-    {"template": "llama", "messages": [{"role": "user", "content": "请自我介绍"}]},
-    {"template": "alpaca", "messages": [{"role": "user", "content": "介绍一下 FastDeploy"}]},
-]
-
-@pytest.mark.parametrize("payload", chat_template_cases)
-def test_chattemplate_valid(payload, api_url, headers):
-    """
-    Test valid ChatTemplate structures.
-    """
-    resp = requests.post(api_url, headers=headers, json=payload)
-    assert resp.status_code == 200, "Request failed for template={}".format(payload['template'])
-    validate(instance=resp.json(), schema=chat_response_schema)
-
-# ==========================
-# ChatTemplate Invalid Structure Test
-# ==========================
-
-invalid_template_cases = [
-    {"template": "nonexist", "messages": [{"role": "user", "content": "你好"}]},
-    {"template": 123, "messages": [{"role": "user", "content": "你好"}]},
-    {"template": "", "messages": [{"role": "user", "content": "你好"}]},
-]
-
-
-@pytest.mark.parametrize("payload", invalid_template_cases)
-@pytest.mark.skip(reason="Validation not yet supported; assertion temporarily disabled")
-def test_chattemplate_invalid(payload, api_url, headers):
-    """
-    Test invalid ChatTemplate structures.
-    """
-    resp = requests.post(api_url, headers=headers, json=payload)
-    assert resp.status_code >= 400, "Invalid template should return an error status code"
-
-# ==========================
-# System Role Test
-# ==========================
-
-def test_system_role(api_url, headers):
-    """
-    Test whether the system role can correctly guide model behavior.
-    """
-    messages = [
-        {"role": "system", "content": "You are an English translation assistant."},
-        {"role": "user", "content": "Please translate: 你好"},
-    ]
-    resp = requests.post(api_url, headers=headers, json={"messages": messages})
-    assert resp.status_code == 200
-    validate(instance=resp.json(), schema=chat_response_schema)
-    result = resp.json()["choices"][0]["message"]["content"]
-    assert "hello" in result.lower()
-
 # ==========================
 # Multi-turn Conversation Test
 # ==========================
@@ -384,23 +334,9 @@ def test_multi_turn_conversation(api_url, headers):
     assert resp.status_code == 200
     validate(instance=resp.json(), schema=chat_response_schema)
 
-# ==========================
-# Simple Performance Test
-# ==========================
-
-def test_simple_perf(api_url, headers):
-    """
-    Send 10 requests to check response stability.
-    """
-    prompts = [{"role": "user", "content": "Introduce FastDeploy."}]
-    for _ in range(10):
-        resp = requests.post(api_url, headers=headers, json={"messages": prompts})
-        assert resp.status_code == 200
-
 # ==========================
 # Concurrent Performance Test
 # ==========================
-@pytest.mark.skip(reason="concurrent is unavailable")
 def test_concurrent_perf(api_url, headers):
     """
     Send concurrent requests to test stability and response time.
@@ -415,11 +351,11 @@ def test_concurrent_perf(api_url, headers):
         assert resp.status_code == 200
         return resp.elapsed.total_seconds()
 
-    with concurrent.futures.ThreadPoolExecutor(max_workers=33) as executor:
-        futures = [executor.submit(send_request) for _ in range(33)]
+    with concurrent.futures.ThreadPoolExecutor(max_workers=8) as executor:
+        futures = [executor.submit(send_request) for _ in range(8)]
         durations = [f.result() for f in futures]
 
-    print("Response time for each request:", durations)
+    print("\nResponse time for each request:", durations)
 
 # ==========================
 # Metrics Endpoint Test
@@ -436,14 +372,11 @@ def test_metrics_endpoint(metrics_url):
 
     # Parse Prometheus metrics data
     metrics_data = resp.text
-    # print(metrics_data)
     lines = metrics_data.split("\n")
 
     metric_lines = [line for line in lines if not line.startswith("#") and line.strip() != ""]
 
-    assert len(metric_lines) > 0, "No valid Prometheus metrics found"
-
-    # Assert specific metric values
+    # 断言 具体值
     num_requests_running_found = False
     num_requests_waiting_found = False
     time_to_first_token_seconds_sum_found = False
@@ -451,41 +384,185 @@ def test_metrics_endpoint(metrics_url):
     e2e_request_latency_seconds_sum_found = False
     request_inference_time_seconds_sum_found = False
     request_queue_time_seconds_sum_found = False
+    request_prefill_time_seconds_sum_found = False
+    request_decode_time_seconds_sum_found = False
+    prompt_tokens_total_found = False
+    generation_tokens_total_found = False
+    request_prompt_tokens_sum_found = False
+    request_generation_tokens_sum_found = False
+    gpu_cache_usage_perc_found = False
+    request_params_max_tokens_sum_found = False
+    request_success_total_found = False
 
     for line in metric_lines:
         if line.startswith("fastdeploy:num_requests_running"):
             _, value = line.rsplit(" ", 1)
-            assert float(value) >= 0, "Invalid value for num_requests_running"
+            assert float(value) >= 0, "num_requests_running 值错误"
             num_requests_running_found = True
         elif line.startswith("fastdeploy:num_requests_waiting"):
             _, value = line.rsplit(" ", 1)
-            assert float(value) >= 0, "Invalid value for num_requests_waiting"
             num_requests_waiting_found = True
+            assert float(value) >= 0, "num_requests_waiting 值错误"
         elif line.startswith("fastdeploy:time_to_first_token_seconds_sum"):
             _, value = line.rsplit(" ", 1)
-            assert float(value) >= 0, "Invalid value for time_to_first_token_seconds_sum"
+            assert float(value) >= 0, "time_to_first_token_seconds_sum 值错误"
             time_to_first_token_seconds_sum_found = True
         elif line.startswith("fastdeploy:time_per_output_token_seconds_sum"):
             _, value = line.rsplit(" ", 1)
-            assert float(value) >= 0, "Invalid value for time_per_output_token_seconds_sum"
+            assert float(value) >= 0, "time_per_output_token_seconds_sum 值错误"
             time_per_output_token_seconds_sum_found = True
         elif line.startswith("fastdeploy:e2e_request_latency_seconds_sum"):
             _, value = line.rsplit(" ", 1)
-            assert float(value) >= 0, "Invalid value for e2e_request_latency_seconds_sum"
+            assert float(value) >= 0, "e2e_request_latency_seconds_sum_found 值错误"
             e2e_request_latency_seconds_sum_found = True
         elif line.startswith("fastdeploy:request_inference_time_seconds_sum"):
             _, value = line.rsplit(" ", 1)
-            assert float(value) >= 0, "Invalid value for request_inference_time_seconds_sum"
+            assert float(value) >= 0, "request_inference_time_seconds_sum 值错误"
             request_inference_time_seconds_sum_found = True
         elif line.startswith("fastdeploy:request_queue_time_seconds_sum"):
             _, value = line.rsplit(" ", 1)
-            assert float(value) >= 0, "Invalid value for request_queue_time_seconds_sum"
+            assert float(value) >= 0, "request_queue_time_seconds_sum 值错误"
             request_queue_time_seconds_sum_found = True
+        elif line.startswith("fastdeploy:request_prefill_time_seconds_sum"):
+            _, value = line.rsplit(" ", 1)
+            assert float(value) >= 0, "request_prefill_time_seconds_sum 值错误"
+            request_prefill_time_seconds_sum_found = True
+        elif line.startswith("fastdeploy:request_decode_time_seconds_sum"):
+            _, value = line.rsplit(" ", 1)
+            assert float(value) >= 0, "request_decode_time_seconds_sum 值错误"
+            request_decode_time_seconds_sum_found = True
+        elif line.startswith("fastdeploy:prompt_tokens_total"):
+            _, value = line.rsplit(" ", 1)
+            assert float(value) >= 0, "prompt_tokens_total 值错误"
+            prompt_tokens_total_found = True
+        elif line.startswith("fastdeploy:generation_tokens_total"):
+            _, value = line.rsplit(" ", 1)
+            assert float(value) >= 0, "generation_tokens_total 值错误"
+            generation_tokens_total_found = True
+        elif line.startswith("fastdeploy:request_prompt_tokens_sum"):
+            _, value = line.rsplit(" ", 1)
+            assert float(value) >= 0, "request_prompt_tokens_sum 值错误"
+            request_prompt_tokens_sum_found = True
+        elif line.startswith("fastdeploy:request_generation_tokens_sum"):
+            _, value = line.rsplit(" ", 1)
+            assert float(value) >= 0, "request_generation_tokens_sum 值错误"
+            request_generation_tokens_sum_found = True
+        elif line.startswith("fastdeploy:gpu_cache_usage_perc"):
+            _, value = line.rsplit(" ", 1)
+            assert float(value) >= 0, "gpu_cache_usage_perc 值错误"
+            gpu_cache_usage_perc_found = True
+        elif line.startswith("fastdeploy:request_params_max_tokens_sum"):
+            _, value = line.rsplit(" ", 1)
+            assert float(value) >= 0, "request_params_max_tokens_sum 值错误"
+            request_params_max_tokens_sum_found = True
+        elif line.startswith("fastdeploy:request_success_total"):
+            _, value = line.rsplit(" ", 1)
+            assert float(value) >= 0, "request_success_total 值错误"
+            request_success_total_found = True
 
-    assert num_requests_running_found, "Missing metric: fastdeploy:num_requests_running"
-    assert num_requests_waiting_found, "Missing metric: fastdeploy:num_requests_waiting"
-    assert time_to_first_token_seconds_sum_found, "Missing metric: fastdeploy:time_to_first_token_seconds_sum"
-    assert time_per_output_token_seconds_sum_found, "Missing metric: fastdeploy:time_per_output_token_seconds_sum"
-    assert e2e_request_latency_seconds_sum_found, "Missing metric: fastdeploy:e2e_request_latency_seconds_sum"
-    assert request_inference_time_seconds_sum_found, "Missing metric: fastdeploy:request_inference_time_seconds_sum"
-    assert request_queue_time_seconds_sum_found, "Missing metric: fastdeploy:request_queue_time_seconds_sum"
\ No newline at end of file
+    assert num_requests_running_found, "缺少 fastdeploy:num_requests_running 指标"
+    assert num_requests_waiting_found, "缺少 fastdeploy:num_requests_waiting 指标"
+    assert time_to_first_token_seconds_sum_found, "缺少 fastdeploy:time_to_first_token_seconds_sum 指标"
+    assert time_per_output_token_seconds_sum_found, "缺少 fastdeploy:time_per_output_token_seconds_sum 指标"
+    assert e2e_request_latency_seconds_sum_found, "缺少 fastdeploy:e2e_request_latency_seconds_sum_found 指标"
+    assert request_inference_time_seconds_sum_found, "缺少 fastdeploy:request_inference_time_seconds_sum 指标"
+    assert request_queue_time_seconds_sum_found, "缺少 fastdeploy:request_queue_time_seconds_sum 指标"
+    assert request_prefill_time_seconds_sum_found, "缺少 fastdeploy:request_prefill_time_seconds_sum 指标"
+    assert request_decode_time_seconds_sum_found, "缺少 fastdeploy:request_decode_time_seconds_sum 指标"
+    assert prompt_tokens_total_found, "缺少 fastdeploy:prompt_tokens_total 指标"
+    assert generation_tokens_total_found, "缺少 fastdeploy:generation_tokens_total 指标"
+    assert request_prompt_tokens_sum_found, "缺少 fastdeploy:request_prompt_tokens_sum 指标"
+    assert request_generation_tokens_sum_found, "缺少 fastdeploy:request_generation_tokens_sum 指标"
+    assert gpu_cache_usage_perc_found, "缺少 fastdeploy:gpu_cache_usage_perc 指标"
+    assert request_params_max_tokens_sum_found, "缺少 fastdeploy:request_params_max_tokens_sum 指标"
+    assert request_success_total_found, "缺少 fastdeploy:request_success_total 指标"
+
+# ==========================
+# OpenAI Client chat.completions Test
+# ==========================
+
+@pytest.fixture
+def openai_client():
+    ip = "0.0.0.0"
+    service_http_port = str(FD_API_PORT)
+    client = openai.Client(
+        base_url = "http://{}:{}/v1".format(ip, service_http_port),
+        api_key="EMPTY_API_KEY"
+    )
+    return client
+
+# Non-streaming test
+def test_non_streaming_chat(openai_client):
+    """Test non-streaming chat functionality with the local service"""
+    response = openai_client.chat.completions.create(
+        model="default",
+        messages=[
+            {"role": "system", "content": "You are a helpful AI assistant."},
+            {"role": "user", "content": "List 3 countries and their capitals."},
+        ],
+        temperature=1,
+        max_tokens=1024,
+        stream=False,
+    )
+
+    assert hasattr(response, 'choices')
+    assert len(response.choices) > 0
+    assert hasattr(response.choices[0], 'message')
+    assert hasattr(response.choices[0].message, 'content')
+
+# Streaming test
+def test_streaming_chat(openai_client, capsys):
+    """Test streaming chat functionality with the local service"""
+    response = openai_client.chat.completions.create(
+        model="default",
+        messages=[
+            {"role": "system", "content": "You are a helpful AI assistant."},
+            {"role": "user", "content": "List 3 countries and their capitals."},
+            {"role": "assistant", "content": "China(Beijing), France(Paris), Australia(Canberra)."},
+            {"role": "user", "content": "OK, tell more."},
+        ],
+        temperature=1,
+        max_tokens=1024,
+        stream=True,
+    )
+
+    output = []
+    for chunk in response:
+        if hasattr(chunk.choices[0], 'delta') and hasattr(chunk.choices[0].delta, 'content'):
+            output.append(chunk.choices[0].delta.content)
+    assert len(output) > 2
+
+# ==========================
+# OpenAI Client completions Test
+# ==========================
+
+def test_non_streaming(openai_client):
+    """Test non-streaming chat functionality with the local service"""
+    response = openai_client.completions.create(
+        model="default",
+        prompt="Hello, how are you?",
+        temperature=1,
+        max_tokens=1024,
+        stream=False,
+    )
+
+    # Assertions to check the response structure
+    assert hasattr(response, 'choices')
+    assert len(response.choices) > 0
+
+
+def test_streaming(openai_client, capsys):
+    """Test streaming functionality with the local service"""
+    response = openai_client.completions.create(
+        model="default",
+        prompt="Hello, how are you?",
+        temperature=1,
+        max_tokens=1024,
+        stream=True,
+    )
+    
+    # Collect streaming output
+    output = []
+    for chunk in response:
+        output.append(chunk.choices[0].text)
+    assert len(output) > 0
\ No newline at end of file
diff --git a/test/ci_use/Qwen3-MoE/test_Qwen3-MoE_serving.py b/test/ci_use/Qwen3-MoE/test_Qwen3-MoE_serving.py
new file mode 100644
index 000000000..70f919203
--- /dev/null
+++ b/test/ci_use/Qwen3-MoE/test_Qwen3-MoE_serving.py
@@ -0,0 +1,283 @@
+# 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 pytest
+import requests
+import time
+import subprocess
+import socket
+import os
+import signal
+import sys
+
+
+# Read ports from environment variables; use default values if not set
+FD_API_PORT = int(os.getenv("FD_API_PORT", 8188))
+FD_ENGINE_QUEUE_PORT = int(os.getenv("FD_ENGINE_QUEUE_PORT", 8133))
+FD_METRICS_PORT = int(os.getenv("FD_METRICS_PORT", 8233))
+
+# List of ports to clean before and after tests
+PORTS_TO_CLEAN = [FD_API_PORT, FD_ENGINE_QUEUE_PORT, FD_METRICS_PORT]
+
+def is_port_open(host: str, port: int, timeout=1.0):
+    """
+    Check if a TCP port is open on the given host.
+    Returns True if connection succeeds, False otherwise.
+    """
+    try:
+        with socket.create_connection((host, port), timeout):
+            return True
+    except Exception:
+        return False
+
+def kill_process_on_port(port: int):
+    """
+    Kill processes that are listening on the given port.
+    Uses `lsof` to find process ids and sends SIGKILL.
+    """
+    try:
+        output = subprocess.check_output("lsof -i:{} -t".format(port), shell=True).decode().strip()
+        for pid in output.splitlines():
+            os.kill(int(pid), signal.SIGKILL)
+            print("Killed process on port {}, pid={}".format(port, pid))
+    except subprocess.CalledProcessError:
+        pass
+
+def clean_ports():
+    """
+    Kill all processes occupying the ports listed in PORTS_TO_CLEAN.
+    """
+    for port in PORTS_TO_CLEAN:
+        kill_process_on_port(port)
+
+@pytest.fixture(scope="session", autouse=True)
+def setup_and_run_server():
+    """
+    Pytest fixture that runs once per test session:
+    - Cleans ports before tests
+    - Starts the API server as a subprocess
+    - Waits for server port to open (up to 30 seconds)
+    - Tears down server after all tests finish
+    """
+    print("Pre-test port cleanup...")
+    clean_ports()
+
+    base_path = os.getenv("MODEL_PATH")
+    if base_path:
+        model_path=os.path.join(base_path, "Qwen3-30B-A3B")
+    else:
+        model_path="./Qwen3-30B-A3B"
+
+    log_path = "server.log"
+    cmd = [
+        sys.executable, "-m", "fastdeploy.entrypoints.openai.api_server",
+        "--model", model_path,
+        "--port", str(FD_API_PORT),
+        "--tensor-parallel-size", "1",
+        "--engine-worker-queue-port", str(FD_ENGINE_QUEUE_PORT),
+        "--metrics-port", str(FD_METRICS_PORT),
+        "--max-model-len", "32768",
+        "--max-num-seqs", "50",
+        "--quantization", "wint4"
+    ]
+
+    # Set environment variables
+    env = os.environ.copy()
+    env["ENABLE_FASTDEPLOY_LOAD_MODEL_CONCURRENCY"] = "0"
+    env["NCCL_ALGO"] = "Ring"
+    env["FLAG_SAMPLING_CLASS"] = "rejection"
+
+    # Start subprocess in new process group
+    with open(log_path, "w") as logfile:
+        process = subprocess.Popen(
+            cmd,
+            env=env,
+            stdout=logfile,
+            stderr=subprocess.STDOUT,
+            start_new_session=True  # Enables killing full group via os.killpg
+        )
+
+    # Wait up to 300 seconds for API server to be ready
+    for _ in range(300):
+        if is_port_open("127.0.0.1", FD_API_PORT):
+            print("API server is up on port {}".format(FD_API_PORT))
+            break
+        time.sleep(1)
+    else:
+        print("API server failed to start in time. Cleaning up...")
+        try:
+            os.killpg(process.pid, signal.SIGTERM)
+        except Exception as e:
+            print("Failed to kill process group: {}".format(e))
+        raise RuntimeError("API server did not start on port {}".format(FD_API_PORT))
+
+    yield  # Run tests
+
+    print("\n===== Post-test server cleanup... =====")
+    try:
+        os.killpg(process.pid, signal.SIGTERM)
+        print("API server (pid={}) terminated".format(process.pid))
+    except Exception as e:
+        print("Failed to terminate API server: {}".format(e))
+
+
+@pytest.fixture(scope="session")
+def api_url(request):
+    """
+    Returns the API endpoint URL for chat completions.
+    """
+    return "http://0.0.0.0:{}/v1/chat/completions".format(FD_API_PORT)
+
+
+@pytest.fixture(scope="session")
+def metrics_url(request):
+    """
+    Returns the metrics endpoint URL.
+    """
+    return "http://0.0.0.0:{}/metrics".format(FD_METRICS_PORT)
+
+
+@pytest.fixture
+def headers():
+    """
+    Returns common HTTP request headers.
+    """
+    return {"Content-Type": "application/json"}
+
+@pytest.fixture
+def consistent_payload():
+    """
+    Returns a fixed payload for consistency testing,
+    including a fixed random seed and temperature.
+    """
+    return {
+        "messages": [{"role": "user", "content": "用一句话介绍 PaddlePaddle, 30字以内 /no_think"}],
+        "temperature": 0.8,
+        "top_p": 0,  # fix top_p to reduce randomness
+        "seed": 13  # fixed random seed
+    }
+
+# ==========================
+# Helper function to calculate difference rate between two texts
+# ==========================
+def calculate_diff_rate(text1, text2):
+    """
+    Calculate the difference rate between two strings
+    based on the normalized Levenshtein edit distance.
+    Returns a float in [0,1], where 0 means identical.
+    """
+    if text1 == text2:
+        return 0.0
+
+    len1, len2 = len(text1), len(text2)
+    dp = [[0] * (len2 + 1) for _ in range(len1 + 1)]
+
+    for i in range(len1 + 1):
+        for j in range(len2 + 1):
+            if i == 0 or j == 0:
+                dp[i][j] = i + j
+            elif text1[i - 1] == text2[j - 1]:
+                dp[i][j] = dp[i - 1][j - 1]
+            else:
+                dp[i][j] = 1 + min(dp[i - 1][j], dp[i][j - 1], dp[i - 1][j - 1])
+
+    edit_distance = dp[len1][len2]
+    max_len = max(len1, len2)
+    return edit_distance / max_len if max_len > 0 else 0.0
+
+# ==========================
+# Consistency test for repeated runs with fixed payload
+# ==========================
+def test_consistency_between_runs(api_url, headers, consistent_payload):
+    """
+    Test that two runs with the same fixed input produce similar outputs.
+    """
+    # First request
+    resp1 = requests.post(api_url, headers=headers, json=consistent_payload)
+    assert resp1.status_code == 200
+    result1 = resp1.json()
+    content1 = result1["choices"][0]["message"]["content"]
+
+    # Second request
+    resp2 = requests.post(api_url, headers=headers, json=consistent_payload)
+    assert resp2.status_code == 200
+    result2 = resp2.json()
+    content2 = result2["choices"][0]["message"]["content"]
+
+    # Calculate difference rate
+    diff_rate = calculate_diff_rate(content1, content2)
+
+    # Verify that the difference rate is below the threshold
+    assert diff_rate < 0.05, "Output difference too large ({:.4%})".format(diff_rate)
+
+# ==========================
+# think Prompt Test
+# ==========================
+
+def test_thinking_prompt(api_url, headers):
+    """
+    Test case to verify normal 'thinking' behavior (no '/no_think' appended).
+    """
+    messages = [
+        {"role": "user", "content": "北京天安门在哪里"}
+    ]
+
+    payload = {
+        "messages": messages,
+        "max_tokens": 100,
+        "temperature": 0.8,
+        "top_p": 0.01
+    }
+
+    resp = requests.post(api_url, headers=headers, json=payload)
+    assert resp.status_code == 200, "Unexpected status code: {}".format(resp.status_code)
+
+    try:
+        response_json = resp.json()
+    except Exception as e:
+        assert False, "Response is not valid JSON: {}".format(e)
+    
+    content = response_json.get("choices", [{}])[0].get("message", {}).get("content", "").lower()
+    assert "天安门" in content or "北京" in content, "Expected a location-related response with reasoning"
+
+# ==========================
+# no_think Prompt Test
+# ==========================
+
+def test_non_thinking_prompt(api_url, headers):
+    """
+    Test case to verify non-thinking behavior (with '/no_think').
+    """
+    messages = [
+        {"role": "user", "content": "北京天安门在哪里 /no_think"}
+    ]
+
+    payload = {
+        "messages": messages,
+        "max_tokens": 100,
+        "temperature": 0.8,
+        "top_p": 0.01
+    }
+
+    resp = requests.post(api_url, headers=headers, json=payload)
+    assert resp.status_code == 200, "Unexpected status code: {}".format(resp.status_code)
+
+    try:
+        response_json = resp.json()
+    except Exception as e:
+        assert False, "Response is not valid JSON: {}".format(e)
+
+    content = response_json.get("choices", [{}])[0].get("message", {}).get("content", "").lower()
+    assert not any(x in content for x in ["根据", "我认为", "推测", "可能"]), \
+        "Expected no reasoning in non-thinking response"
\ No newline at end of file