diff --git a/vllm/model_executor/layers/mamba/gdn_linear_attn.py b/vllm/model_executor/layers/mamba/gdn_linear_attn.py
index a621ab962f0a..4f3af330c5eb 100644
--- a/vllm/model_executor/layers/mamba/gdn_linear_attn.py
+++ b/vllm/model_executor/layers/mamba/gdn_linear_attn.py
@@ -56,10 +56,12 @@
 from vllm.platforms import current_platform
 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 (
     LayerNameType,
     _encode_layer_name,
     _resolve_layer_name,
+    aux_stream,
     direct_register_custom_op,
 )
 from vllm.v1.attention.backends.gdn_attn import GDNAttentionMetadata
@@ -275,6 +277,19 @@ def __init__(
             self.forward_xpu if current_platform.is_xpu() else self.forward_cuda
         )
 
+        # Dual-stream dispatch for the two input projections (in_proj_qkvz and
+        # in_proj_ba). Re-enabled via the LayerName opaque type (PR #38123) so
+        # torch.compile no longer regresses cold compile times.
+        self.aux_stream = aux_stream()
+        self.events = (
+            [
+                torch.cuda.Event(enable_timing=False),
+                torch.cuda.Event(enable_timing=False),
+            ]
+            if self.aux_stream is not None
+            else [None, None]
+        )
+
         # QKV
         self.conv_dim = self.key_dim * 2 + self.value_dim
         self.conv1d = ColumnParallelLinear(
@@ -541,8 +556,12 @@ def forward_cuda(
             b = b.contiguous()
             a = a.contiguous()
         else:
-            mixed_qkvz, _ = self.in_proj_qkvz(hidden_states)
-            ba, _ = self.in_proj_ba(hidden_states)
+            mixed_qkvz, ba = torch.ops.vllm.gdn_in_proj(
+                hidden_states,
+                self.in_proj_qkvz.weight.shape[0],
+                self.in_proj_ba.weight.shape[0],
+                _encode_layer_name(self.prefix),
+            )
 
             if self.gqa_interleaved_layout:
                 # Qwen3-Next: unpack the interleaved GQA layout
@@ -594,6 +613,23 @@ def forward_cuda(
         core_attn_out = rearrange(core_attn_out, "... h d -> ... (h d)")
         output[:num_tokens], _ = self.out_proj(core_attn_out)
 
+    def _forward_in_proj(
+        self, hidden_states: torch.Tensor
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        """Run in_proj_qkvz and in_proj_ba in parallel on two CUDA streams.
+
+        Falls back to sequential execution when aux_stream is unavailable
+        (e.g. non-CUDA platforms).
+        """
+        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_xpu(
         self,
         hidden_states: torch.Tensor,
@@ -1063,6 +1099,39 @@ def _forward_core_decode_non_spec(
         return
 
 
+def gdn_in_proj(
+    hidden_states: torch.Tensor,
+    qkvz_output_size: int,
+    ba_output_size: int,
+    layer_name: LayerNameType,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    """Custom op wrapping the dual-stream input projection."""
+    layer_name = _resolve_layer_name(layer_name)
+    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: LayerNameType,
+) -> 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),
+    )
+
+
+direct_register_custom_op(
+    op_name="gdn_in_proj",
+    op_func=gdn_in_proj,
+    fake_impl=gdn_in_proj_fake,
+)
+
+
 def gdn_attention_core(
     mixed_qkv: torch.Tensor,
     b: torch.Tensor,