[Nightly][Test] Add Qwen3-Next-80B-A3B-Instruct-W8A8 nightly test (#5616)

### What this PR does / why we need it?
There was an accuracy issue with **Qwen3-Next-80B-A3B-Instruct-W8A8**
model in the old version of **Triton-Ascend**, so, we are now adding one
nightly test to maintain it.
### Does this PR introduce _any_ user-facing change?
N/A
### How was this patch tested?

- vLLM version: v0.13.0
- vLLM main:
7157596103

Signed-off-by: IncSec <1790766300@qq.com>

.github/workflows/nightly_test_a3.yaml

@@ -132,6 +132,9 @@ jobs:
           - name: qwen3-235b-w8a8
             os: linux-aarch64-a3-16
             tests: tests/e2e/nightly/single_node/models/test_qwen3_235b_w8a8.py
+          - name: qwen3-next-w8a8
+            os: linux-aarch64-a3-4
+            tests: tests/e2e/nightly/single_node/models/test_qwen3_next_w8a8.py
           # TODO: Replace deepseek3.2-exp with deepseek3.2 after nightly tests pass
           # - name: deepseek3_2-exp-w8a8
           #   os: linux-aarch64-a3-16

tests/e2e/nightly/single_node/models/test_qwen3_next_w8a8.py

@@ -0,0 +1,104 @@
+# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
+# Copyright 2023 The vLLM team.
+#
+# 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.
+# This file is a part of the vllm-ascend project.
+#
+from typing import Any
+
+import openai
+import pytest
+from vllm.utils.network_utils import get_open_port
+
+from tests.e2e.conftest import RemoteOpenAIServer
+from tools.aisbench import run_aisbench_cases
+
+MODELS = [
+    "vllm-ascend/Qwen3-Next-80B-A3B-Instruct-W8A8",
+]
+
+prompts = [
+    "San Francisco is a",
+]
+
+api_keyword_args = {
+    "max_tokens": 10,
+}
+
+aisbench_cases = [{
+    "case_type": "accuracy",
+    "dataset_path": "vllm-ascend/gsm8k-lite",
+    "request_conf": "vllm_api_general_chat",
+    "dataset_conf": "gsm8k/gsm8k_gen_0_shot_cot_chat_prompt",
+    "max_out_len": 32768,
+    "batch_size": 32,
+    "baseline": 95,
+    "threshold": 5
+}]
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize("model", MODELS)
+async def test_models(model: str) -> None:
+    port = get_open_port()
+    env_dict = {
+        "OMP_NUM_THREADS": "10",
+        "OMP_PROC_BIND": "false",
+        "HCCL_BUFFSIZE": "1024",
+    }
+    server_args = [
+        "--quantization",
+        "ascend",
+        "--async-scheduling",
+        "--no-enable-prefix-caching",
+        "--data-parallel-size",
+        "1",
+        "--tensor-parallel-size",
+        "4",
+        "--enable-expert-parallel",
+        "--port",
+        str(port),
+        "--max-model-len",
+        "40960",
+        "--max-num-batched-tokens",
+        "8192",
+        "--max-num-seqs",
+        "32",
+        "--trust-remote-code",
+        "--gpu-memory-utilization",
+        "0.65",
+        "--compilation-config",
+        '{"cudagraph_capture_sizes": [32], "cudagraph_mode":"FULL_DECODE_ONLY"}',
+    ]
+    request_keyword_args: dict[str, Any] = {
+        **api_keyword_args,
+    }
+    with RemoteOpenAIServer(model,
+                            server_args,
+                            server_port=port,
+                            env_dict=env_dict,
+                            auto_port=False) as server:
+        client = server.get_async_client()
+        batch = await client.completions.create(
+            model=model,
+            prompt=prompts,
+            **request_keyword_args,
+        )
+        choices: list[openai.types.CompletionChoice] = batch.choices
+        assert choices[0].text, "empty response"
+        print(choices)
+        # aisbench test
+        run_aisbench_cases(model,
+                           port,
+                           aisbench_cases,
+                           server_args=server_args)