diff --git a/vllm/config.py b/vllm/config.py
index 7f8f93642854..f57aa4048ae9 100644
--- a/vllm/config.py
+++ b/vllm/config.py
@@ -33,8 +33,10 @@ logger = init_logger(__name__)
 _EMBEDDING_MODEL_MAX_NUM_BATCHED_TOKENS = 32768
 _MULTIMODAL_MODEL_MAX_NUM_BATCHED_TOKENS = 5120
 
-Task = Literal["generate", "embedding"]
-TaskOption = Literal["auto", Task]
+TaskOption = Literal["auto", "generate", "embedding"]
+
+# "draft" is only used internally for speculative decoding
+_Task = Literal["generate", "embedding", "draft"]
 
 
 class ModelConfig:
@@ -115,7 +117,7 @@ class ModelConfig:
 
     def __init__(self,
                  model: str,
-                 task: TaskOption,
+                 task: Union[TaskOption, _Task],
                  tokenizer: str,
                  tokenizer_mode: str,
                  trust_remote_code: bool,
@@ -255,18 +257,21 @@ class ModelConfig:
 
     def _resolve_task(
         self,
-        task_option: TaskOption,
+        task_option: Union[TaskOption, _Task],
         hf_config: PretrainedConfig,
-    ) -> Tuple[Set[Task], Task]:
+    ) -> Tuple[Set[_Task], _Task]:
+        if task_option == "draft":
+            return {"draft"}, "draft"
+
         architectures = getattr(hf_config, "architectures", [])
 
-        task_support: Dict[Task, bool] = {
+        task_support: Dict[_Task, bool] = {
             # NOTE: Listed from highest to lowest priority,
             # in case the model supports multiple of them
             "generate": ModelRegistry.is_text_generation_model(architectures),
             "embedding": ModelRegistry.is_embedding_model(architectures),
         }
-        supported_tasks_lst: List[Task] = [
+        supported_tasks_lst: List[_Task] = [
             task for task, is_supported in task_support.items() if is_supported
         ]
         supported_tasks = set(supported_tasks_lst)
@@ -1002,7 +1007,7 @@ class SchedulerConfig:
     """
 
     def __init__(self,
-                 task: Task,
+                 task: _Task,
                  max_num_batched_tokens: Optional[int],
                  max_num_seqs: int,
                  max_model_len: int,
@@ -1269,7 +1274,7 @@ class SpeculativeConfig:
             ngram_prompt_lookup_min = 0
             draft_model_config = ModelConfig(
                 model=speculative_model,
-                task=target_model_config.task,
+                task="draft",
                 tokenizer=target_model_config.tokenizer,
                 tokenizer_mode=target_model_config.tokenizer_mode,
                 trust_remote_code=target_model_config.trust_remote_code,