diff --git a/vllm/model_executor/models/qwen3_moe.py b/vllm/model_executor/models/qwen3_moe.py
index 34b5af846493..825272535a45 100644
--- a/vllm/model_executor/models/qwen3_moe.py
+++ b/vllm/model_executor/models/qwen3_moe.py
@@ -64,7 +64,7 @@ from vllm.model_executor.model_loader.weight_utils import (
 from vllm.model_executor.models.utils import sequence_parallel_chunk
 from vllm.sequence import IntermediateTensors
 
-from .interfaces import MixtureOfExperts, SupportsLoRA, SupportsPP
+from .interfaces import MixtureOfExperts, SupportsEagle3, SupportsLoRA, SupportsPP
 from .utils import (
     AutoWeightsLoader,
     PPMissingLayer,
@@ -422,6 +422,8 @@ class Qwen3MoeModel(nn.Module):
         self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory(
             ["hidden_states", "residual"], config.hidden_size
         )
+        # Track layers for auxiliary hidden state outputs (EAGLE3)
+        self.aux_hidden_state_layers: tuple[int, ...] = ()
 
     def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
         return self.embed_tokens(input_ids)
@@ -432,7 +434,9 @@ class Qwen3MoeModel(nn.Module):
         positions: torch.Tensor,
         intermediate_tensors: Optional[IntermediateTensors] = None,
         inputs_embeds: Optional[torch.Tensor] = None,
-    ) -> Union[torch.Tensor, IntermediateTensors]:
+    ) -> Union[
+        torch.Tensor, IntermediateTensors, tuple[torch.Tensor, list[torch.Tensor]]
+    ]:
         if get_pp_group().is_first_rank:
             if inputs_embeds is not None:
                 hidden_states = inputs_embeds
@@ -443,13 +447,29 @@ class Qwen3MoeModel(nn.Module):
             assert intermediate_tensors is not None
             hidden_states = intermediate_tensors["hidden_states"]
             residual = intermediate_tensors["residual"]
-        for layer in islice(self.layers, self.start_layer, self.end_layer):
+
+        aux_hidden_states = []
+        for layer_idx, layer in enumerate(
+            islice(self.layers, self.start_layer, self.end_layer),
+            start=self.start_layer,
+        ):
+            # Collect auxiliary hidden states if specified
+            if layer_idx in self.aux_hidden_state_layers:
+                aux_hidden_state = (
+                    hidden_states + residual if residual is not None else hidden_states
+                )
+                aux_hidden_states.append(aux_hidden_state)
             hidden_states, residual = layer(positions, hidden_states, residual)
+
         if not get_pp_group().is_last_rank:
             return IntermediateTensors(
                 {"hidden_states": hidden_states, "residual": residual}
             )
         hidden_states, _ = self.norm(hidden_states, residual)
+
+        # Return auxiliary hidden states if collected
+        if len(aux_hidden_states) > 0:
+            return hidden_states, aux_hidden_states
         return hidden_states
 
     def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
@@ -606,7 +626,9 @@ class Qwen3MoeModel(nn.Module):
         return loaded_params
 
 
-class Qwen3MoeForCausalLM(nn.Module, SupportsPP, SupportsLoRA, MixtureOfExperts):
+class Qwen3MoeForCausalLM(
+    nn.Module, SupportsPP, SupportsLoRA, SupportsEagle3, MixtureOfExperts
+):
     packed_modules_mapping = {
         "qkv_proj": [
             "q_proj",
@@ -702,6 +724,13 @@ class Qwen3MoeForCausalLM(nn.Module, SupportsPP, SupportsLoRA, MixtureOfExperts)
                 moe.n_redundant_experts = self.num_redundant_experts
                 moe.experts.update_expert_map()
 
+    def set_aux_hidden_state_layers(self, layers: tuple[int, ...]) -> None:
+        self.model.aux_hidden_state_layers = layers
+
+    def get_eagle3_aux_hidden_state_layers(self) -> tuple[int, ...]:
+        num_layers = len(self.model.layers)
+        return (2, num_layers // 2, num_layers - 3)
+
     def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
         return self.model.get_input_embeddings(input_ids)