[CI] Revert flaky test_quark_int8_w8a8_moe

tests/quantization/test_quark.py

@@ -1020,32 +1020,32 @@ def test_quark_int8_w_per_tensor_a_per_tensor(monkeypatch, dist_init, workspace_
         assert torch.isfinite(logits).all()
 
 
-def test_quark_int8_w8a8_moe(monkeypatch, dist_init, workspace_init):
+@pytest.mark.parametrize("tp", [1])
+def test_quark_int8_w8a8_moe(vllm_runner, tp):
     """Test W8A8 INT8 MoE quantization with a tiny Qwen3 MoE model."""
     model_path = "amd/tiny-qwen3-moe-w8a8-int8"
-    model, vllm_config = load_model_without_vllm_runner(
+    with vllm_runner(
         model_path,
-        model_config_kwargs={"hf_overrides": {"num_hidden_layers": 3}},
-    )
+        enforce_eager=True,
+        tensor_parallel_size=tp,
+        gpu_memory_utilization=0.1,
+    ) as llm:
+
+        def check_model(model):
+            layer = model.model.layers[0]
+            # MoE experts should use QuarkW8A8Int8MoEMethod
+            moe = layer.mlp.experts
+            assert isinstance(moe._quant_method, QuarkW8A8Int8MoEMethod), (
+                f"Expected QuarkW8A8Int8MoEMethod, got {type(moe._quant_method)}"
+            )
+            # Non-MoE linear layers should use QuarkW8A8Int8
+            qkv_proj = layer.self_attn.qkv_proj
+            assert isinstance(qkv_proj.scheme, QuarkW8A8Int8)
 
-    layer = model.model.layers[0]
-    moe = layer.mlp.experts
-    assert isinstance(moe._quant_method, QuarkW8A8Int8MoEMethod), (
-        f"Expected QuarkW8A8Int8MoEMethod, got {type(moe._quant_method)}"
-    )
-    qkv_proj = layer.self_attn.qkv_proj
-    assert isinstance(qkv_proj.scheme, QuarkW8A8Int8)
+        llm.apply_model(check_model)
 
-    monkeypatch.setattr(Attention, "forward", lambda _, q, k, v: q.contiguous())
-    input_ids = torch.tensor([1, 2, 3, 4], device=DEVICE_TYPE)
-    positions = torch.arange(input_ids.numel(), device=DEVICE_TYPE)
-    with (
-        set_current_vllm_config(vllm_config),
-        set_forward_context(None, vllm_config, num_tokens=input_ids.numel()),
-    ):
-        hidden_states = model(input_ids, positions, None)
-        logits = model.compute_logits(hidden_states)
-    assert torch.isfinite(logits).all()
+        output = llm.generate_greedy("Hello", max_tokens=4)
+        assert output
 
 
 @pytest.mark.skipif(