diff --git a/vllm_ascend/quantization/w4a8_dynamic.py b/vllm_ascend/quantization/w4a8_dynamic.py
index 11dc97d51e2..48ade692ec6 100644
--- a/vllm_ascend/quantization/w4a8_dynamic.py
+++ b/vllm_ascend/quantization/w4a8_dynamic.py
@@ -27,7 +27,7 @@
 from vllm_ascend.ascend_config import get_ascend_config
 from vllm_ascend.distributed.parallel_state import get_mc2_group
 from vllm_ascend.ops.moe.experts_selector import select_experts
-from vllm_ascend.utils import ACL_FORMAT_FRACTAL_NZ, is_enable_nz
+from vllm_ascend.utils import ACL_FORMAT_FRACTAL_NZ
 
 
 class AscendW4A8DynamicLinearMethod:
@@ -482,10 +482,9 @@ def process_weights_after_loading(self, layer):
 
         self.update_bias(layer, w13_bias, w2_bias)
 
-        if is_enable_nz():
-            layer.w13_weight.data = torch_npu.npu_format_cast(
-                layer.w13_weight.data, ACL_FORMAT_FRACTAL_NZ)
-            layer.w2_weight.data = torch_npu.npu_format_cast(
-                layer.w2_weight.data, ACL_FORMAT_FRACTAL_NZ)
+        layer.w13_weight.data = torch_npu.npu_format_cast(
+            layer.w13_weight.data, ACL_FORMAT_FRACTAL_NZ)
+        layer.w2_weight.data = torch_npu.npu_format_cast(
+            layer.w2_weight.data, ACL_FORMAT_FRACTAL_NZ)
         layer.w13_weight.data = self.pack_to_int32(layer.w13_weight.data)
         layer.w2_weight.data = self.pack_to_int32(layer.w2_weight.data)
diff --git a/vllm_ascend/quantization/w8a8.py b/vllm_ascend/quantization/w8a8.py
index 9df640c1893..cf5b1912c8e 100644
--- a/vllm_ascend/quantization/w8a8.py
+++ b/vllm_ascend/quantization/w8a8.py
@@ -347,7 +347,7 @@ def process_weights_after_loading(self, layer):
         # converting ACL_FORMAT_FRACTAL_NZ.
         # npu_quant_grouped_matmul_dequant in eager mode does not accept
         # ACL_FORMAT_FRACTAL_NZ.
-        if not is_310p() and is_enable_nz():
+        if not is_310p():
             layer.w13_weight.data = torch_npu.npu_format_cast(
                 layer.w13_weight.data, ACL_FORMAT_FRACTAL_NZ).contiguous()
             layer.w2_weight.data = torch_npu.npu_format_cast(
diff --git a/vllm_ascend/quantization/w8a8_dynamic.py b/vllm_ascend/quantization/w8a8_dynamic.py
index 709f4884eb1..31b9ea912f5 100644
--- a/vllm_ascend/quantization/w8a8_dynamic.py
+++ b/vllm_ascend/quantization/w8a8_dynamic.py
@@ -270,9 +270,8 @@ def process_weights_after_loading(self, layer):
                 1, 2).contiguous()
             layer.w2_weight.data = layer.w2_weight.data.transpose(
                 1, 2).contiguous()
-        if is_enable_nz():
-            torch_npu.npu_format_cast_(layer.w13_weight, ACL_FORMAT_FRACTAL_NZ)
-            torch_npu.npu_format_cast_(layer.w2_weight, ACL_FORMAT_FRACTAL_NZ)
+        torch_npu.npu_format_cast_(layer.w13_weight, ACL_FORMAT_FRACTAL_NZ)
+        torch_npu.npu_format_cast_(layer.w2_weight, ACL_FORMAT_FRACTAL_NZ)
         layer.w13_weight_scale.data = layer.w13_weight_scale.data.view(
             layer.w13_weight_scale.data.shape[0], -1)
         layer.w13_weight_scale_fp32 = layer.w13_weight_scale.data.to(
diff --git a/vllm_ascend/utils.py b/vllm_ascend/utils.py
index 0d128eb8aad..20b89107a4d 100644
--- a/vllm_ascend/utils.py
+++ b/vllm_ascend/utils.py
@@ -81,10 +81,9 @@ def is_enable_nz(dtype: Optional[torch.dtype] = torch.int8,
                 "vllm_config must be provided when _ENABLE_NZ is None")
         _ENABLE_NZ = envs_ascend.VLLM_ASCEND_ENABLE_NZ and vllm_config.model_config.hf_config.model_type != "qwen3_next"
 
-        _IS_EAGLE_MODE = (
-            vllm_config.speculative_config is not None and 
-            getattr(vllm_config.speculative_config, 'method', None) in ("eagle", "eagle3")
-        )
+        _IS_EAGLE_MODE = (vllm_config.speculative_config is not None
+                          and getattr(vllm_config.speculative_config, 'method',
+                                      None) in ("eagle", "eagle3"))
 
     if dtype in [torch.float16, torch.bfloat16]:
         return _ENABLE_NZ if _IS_EAGLE_MODE else False