[CI] Enable linux-aarch64-a2 (64GB) and tp2 * 2 max-parallel to speed up CI (#2065)

### What this PR does / why we need it?
Currently our workflow run time takes about 3 hours in total, which
seriously affects the developer experience, so it is urgent to have a
optimization, after this pr, It is expected that the running time of the
full CI can be shortened to 1h40min.

- Enable linux-aarch64-a2 (64GB) to replace linux-arm64-npu (32GB)
- Change TP4 ---> TP2 * 2 max-parallel
- Move DeepSeek-V2-Lite-W8A8 to single card test

### Does this PR introduce _any_ user-facing change?
No


- vLLM version: v0.10.0
- vLLM main:
a2480251ec

---------

Signed-off-by: wangli <wangli858794774@gmail.com>

tests/e2e/singlecard/quant/test_w8a8.py

@@ -0,0 +1,42 @@
+#
+# 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.
+#
+
+import pytest
+from modelscope import snapshot_download  # type: ignore[import-untyped]
+
+from tests.e2e.conftest import VllmRunner
+
+MODELS = [
+    "vllm-ascend/DeepSeek-V2-Lite-W8A8",
+    "vllm-ascend/Qwen2.5-0.5B-Instruct-W8A8"
+]
+
+
+@pytest.mark.parametrize("model", MODELS)
+def test_quant_W8A8(example_prompts, model):
+    max_tokens = 5
+    model_path = snapshot_download(model)
+    with VllmRunner(
+            model_path,
+            max_model_len=8192,
+            enforce_eager=True,
+            dtype="auto",
+            gpu_memory_utilization=0.7,
+            quantization="ascend",
+    ) as vllm_model:
+        vllm_model.generate_greedy(example_prompts, max_tokens)