diff --git a/vllm/v1/spec_decode/eagle.py b/vllm/v1/spec_decode/eagle.py
index 4b5c9b7ec640..f7179385ebb7 100644
--- a/vllm/v1/spec_decode/eagle.py
+++ b/vllm/v1/spec_decode/eagle.py
@@ -329,16 +329,24 @@ def load_model(self, target_model: nn.Module) -> None:
 
         self.attn_layer_names = list(draft_attn_layer_names)
 
+        if supports_multimodal(target_model):
+            # handle multimodality
+            self.model.config.image_token_index = (
+                target_model.config.image_token_index)
+            target_language_model = target_model.get_language_model()
+        else:
+            target_language_model = target_model
         # share embed_tokens with the target model if needed
         if get_pp_group().world_size == 1 \
             and self.model.model.embed_tokens.weight.shape \
-                == target_model.model.embed_tokens.weight.shape:
+                == target_language_model.model.embed_tokens.weight.shape:
             logger.info(
                 "Assuming the EAGLE head shares the same vocab embedding" \
                 " with the target model."
             )
             del self.model.model.embed_tokens
-            self.model.model.embed_tokens = target_model.model.embed_tokens
+            self.model.model.embed_tokens = (
+                target_language_model.model.embed_tokens)
         else:
             logger.info(
                 "The EAGLE head's vocab embedding will be loaded separately" \
@@ -349,12 +357,9 @@ def load_model(self, target_model: nn.Module) -> None:
         # some model definition do not define lm_head explicitly
         # and reuse embed_tokens for lm_head, e.g., CohereForCausalLM
         if self.vllm_config.speculative_config.method != "eagle3" and \
-                hasattr(target_model, "lm_head"):
+                hasattr(target_language_model, "lm_head"):
             logger.info("Loading EAGLE LM head weights from the target model.")
-            if supports_multimodal(target_model):
-                self.model.lm_head = target_model.get_language_model().lm_head
-            else:
-                self.model.lm_head = target_model.lm_head
+            self.model.lm_head = target_language_model.lm_head
 
     @torch.inference_mode()
     def dummy_run(