diff --git a/.github/workflows/nightly_test_a2.yaml b/.github/workflows/nightly_test_a2.yaml
index 6e9bcc0d311..1e2c40e7e6d 100644
--- a/.github/workflows/nightly_test_a2.yaml
+++ b/.github/workflows/nightly_test_a2.yaml
@@ -52,6 +52,9 @@ jobs:
           - name: qwen3-8b
             os: linux-aarch64-a2-1
             tests: tests/e2e/nightly/single_node/models/test_qwen3_8b.py
+          - name: qwen3next
+            os: linux-aarch64-a2-4
+            ests: tests/e2e/nightly/single_node/models/test_qwen3_next.py
           - name: qwen3-32b
             os: linux-aarch64-a2-4
             tests: tests/e2e/nightly/single_node/models/test_qwen3_32b.py
diff --git a/tests/e2e/nightly/single_node/models/test_qwen3_next.py b/tests/e2e/nightly/single_node/models/test_qwen3_next.py
new file mode 100644
index 00000000000..22e869d090d
--- /dev/null
+++ b/tests/e2e/nightly/single_node/models/test_qwen3_next.py
@@ -0,0 +1,113 @@
+import json
+import os
+from typing import Any
+
+import openai
+import pytest
+from vllm.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",
+]
+
+MODES = ["aclgraph"]
+
+TENSOR_PARALLELS = [4]
+MAX_NUM_BATCHED_TOKENS = [1024, 4096, 8192, 32768]
+
+prompts = [
+    "San Francisco is a",
+]
+
+api_keyword_args = {
+    "max_tokens": 10,
+}
+
+batch_size_dict = {
+    "linux-aarch64-a2-4": 64,
+    "linux-aarch64-a3-4": 64,
+}
+VLLM_CI_RUNNER = os.getenv("VLLM_CI_RUNNER", "linux-aarch64-a2-4")
+performance_batch_size = batch_size_dict.get(VLLM_CI_RUNNER, 1)
+
+aisbench_cases = [{
+    "case_type": "performance",
+    "dataset_path": "vllm-ascend/GSM8K-in3500-bs400",
+    "request_conf": "vllm_api_stream_chat",
+    "dataset_conf": "gsm8k/gsm8k_gen_0_shot_cot_str_perf",
+    "num_prompts": 4 * performance_batch_size,
+    "max_out_len": 1500,
+    "batch_size": performance_batch_size,
+    "baseline": 1,
+    "threshold": 0.97
+}, {
+    "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,
+    "top_k": 20,
+    "baseline": 95,
+    "threshold": 5
+}]
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize("model", MODELS)
+@pytest.mark.parametrize("mode", MODES)
+@pytest.mark.parametrize("tp_size", TENSOR_PARALLELS)
+@pytest.mark.parametrize("max_num_batched_tokens", MAX_NUM_BATCHED_TOKENS)
+async def test_models(model: str, mode: str, tp_size: int,
+                      max_num_batched_tokens: int) -> None:
+    port = get_open_port()
+    env_dict = {
+        "OMP_NUM_THREADS": "10",
+        "OMP_PROC_BIND": "false",
+        "HCCL_BUFFSIZE": "1024",
+        "PYTORCH_NPU_ALLOC_CONF": "expandable_segments:True",
+    }
+    compilation_config = {"cudagraph_mode": "FULL_DECODE_ONLY"}
+    server_args = [
+        "--tensor-parallel-size",
+        str(tp_size),
+        "--port",
+        str(port),
+        "--max-model-len",
+        "40960",
+        "--max-num-batched-tokens",
+        str(max_num_batched_tokens),
+        "--trust-remote-code",
+        "--gpu-memory-utilization",
+        "0.8",
+        "--max-num-seqs",
+        "64",
+    ]
+    if mode == "aclgraph":
+        server_args.extend(
+            ["--compilation-config",
+             json.dumps(compilation_config)])
+    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)
+        if mode == "single":
+            return
+        # aisbench test
+        run_aisbench_cases(model, port, aisbench_cases)