diff --git a/vllm/model_executor/models/qwen3_5.py b/vllm/model_executor/models/qwen3_5.py
index daca52821e0f..1ae8aa75ab62 100644
--- a/vllm/model_executor/models/qwen3_5.py
+++ b/vllm/model_executor/models/qwen3_5.py
@@ -221,12 +221,8 @@ def forward(
             ba, _ = self.in_proj_ba(hidden_states)
             z, _ = self.in_proj_z(hidden_states)
         else:
-            mixed_qkvz, ba = torch.ops.vllm.gdn_in_proj(
-                hidden_states,
-                sum(self.in_proj_qkvz.output_sizes) // self.tp_size,
-                sum(self.in_proj_ba.output_sizes) // self.tp_size,
-                self.prefix,
-            )
+            mixed_qkvz, _ = self.in_proj_qkvz(hidden_states)
+            ba, _ = self.in_proj_ba(hidden_states)
             qkv_size = (self.key_dim * 2 + self.value_dim) // self.tp_size
             z_size = self.value_dim // self.tp_size
             mixed_qkv, z = mixed_qkvz.split([qkv_size, z_size], dim=-1)
diff --git a/vllm/model_executor/models/qwen3_next.py b/vllm/model_executor/models/qwen3_next.py
index 5dfcd677b9a1..251c93364a9f 100644
--- a/vllm/model_executor/models/qwen3_next.py
+++ b/vllm/model_executor/models/qwen3_next.py
@@ -81,11 +81,7 @@
 from vllm.sequence import IntermediateTensors
 from vllm.transformers_utils.configs.qwen3_next import Qwen3NextConfig
 from vllm.triton_utils import tl, triton
-from vllm.utils.multi_stream_utils import maybe_execute_in_parallel
-from vllm.utils.torch_utils import (
-    aux_stream,
-    direct_register_custom_op,
-)
+from vllm.utils.torch_utils import direct_register_custom_op
 from vllm.v1.attention.backend import AttentionMetadata
 from vllm.v1.attention.backends.gdn_attn import GDNAttentionMetadata
 
@@ -421,12 +417,6 @@ def __init__(
         self.act = ACT2FN[config.hidden_act]
         self.layer_norm_epsilon = config.rms_norm_eps
         self.prefix = prefix
-        self.aux_stream = aux_stream()
-        self.events = (
-            [torch.cuda.Event(), torch.cuda.Event()]
-            if current_platform.is_cuda_alike()
-            else [None, None]
-        )
 
         self.config = config
         self.model_config = vllm_config.model_config
@@ -659,12 +649,8 @@ def forward(
         # ============================================================
         # Part 1: Input Projection
         # ============================================================
-        projected_states_qkvz, projected_states_ba = torch.ops.vllm.gdn_in_proj(
-            hidden_states,
-            sum(self.in_proj_qkvz.output_sizes) // self.tp_size,
-            sum(self.in_proj_ba.output_sizes) // self.tp_size,
-            self.prefix,
-        )
+        projected_states_qkvz, _ = self.in_proj_qkvz(hidden_states)
+        projected_states_ba, _ = self.in_proj_ba(hidden_states)
         query, key, value, z, b, a = self.fix_query_key_value_ordering(
             projected_states_qkvz, projected_states_ba
         )
@@ -804,18 +790,6 @@ def _warmup_prefill_kernels(self, mixed_qkv: torch.Tensor) -> None:
 
         torch.accelerator.empty_cache()
 
-    def _forward_in_proj(
-        self, hidden_states: torch.Tensor
-    ) -> tuple[torch.Tensor, torch.Tensor]:
-        projected_states_qkvz, projected_states_ba = maybe_execute_in_parallel(
-            lambda: self.in_proj_qkvz(hidden_states)[0],
-            lambda: self.in_proj_ba(hidden_states)[0],
-            self.events[0],
-            self.events[1],
-            self.aux_stream,
-        )
-        return projected_states_qkvz, projected_states_ba
-
     def _forward_core(
         self,
         mixed_qkv: torch.Tensor,
@@ -1697,32 +1671,6 @@ def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
         return self.model.get_expert_mapping()
 
 
-def gdn_in_proj(
-    hidden_states: torch.Tensor,
-    qkvz_output_size: int,
-    ba_output_size: int,
-    layer_name: str,
-) -> tuple[torch.Tensor, torch.Tensor]:
-    """
-    Custom op for the input projection.
-    """
-    forward_context: ForwardContext = get_forward_context()
-    self = forward_context.no_compile_layers[layer_name]
-    return self._forward_in_proj(hidden_states)
-
-
-def gdn_in_proj_fake(
-    hidden_states: torch.Tensor,
-    qkvz_output_size: int,
-    ba_output_size: int,
-    layer_name: str,
-) -> tuple[torch.Tensor, torch.Tensor]:
-    """Fake implementation for torch.compile."""
-    return hidden_states.new_empty(
-        hidden_states.shape[0], qkvz_output_size
-    ), hidden_states.new_empty(hidden_states.shape[0], ba_output_size)
-
-
 def gdn_attention_core(
     mixed_qkv: torch.Tensor,
     b: torch.Tensor,
@@ -1756,12 +1704,6 @@ def gdn_attention_core_fake(
     return
 
 
-direct_register_custom_op(
-    op_name="gdn_in_proj",
-    op_func=gdn_in_proj,
-    fake_impl=gdn_in_proj_fake,
-)
-
 direct_register_custom_op(
     op_name="gdn_attention_core",
     op_func=gdn_attention_core,