diff --git a/tensorrt_llm/serve/openai_protocol.py b/tensorrt_llm/serve/openai_protocol.py
index 8730976898fd..3c4b13ab53ff 100644
--- a/tensorrt_llm/serve/openai_protocol.py
+++ b/tensorrt_llm/serve/openai_protocol.py
@@ -43,11 +43,18 @@
 from tensorrt_llm.llmapi.reasoning_parser import ReasoningParserFactory
 
 
-def _logit_bias_to_embedding_bias(logit_bias: Optional[Dict[str, float]],
-                                  vocab_size: int) -> Optional[torch.Tensor]:
+def _logit_bias_to_embedding_bias(
+        logit_bias: Optional[Dict[str, float]],
+        vocab_size: Optional[int]) -> Optional[torch.Tensor]:
     """Convert OpenAI logit_bias dict to embedding_bias tensor for sampling."""
     if logit_bias is None:
         return None
+    if vocab_size is None:
+        raise ValueError(
+            "logit_bias requires a tokenizer, but the server was started "
+            "without one (e.g. num_postprocess_workers > 0). "
+            "Remove logit_bias from your request or set num_postprocess_workers=0."
+        )
 
     # Create 1D zeros tensor as expected by executor API (will be unsqueezed to [1, vocab_size] internally)
     embedding_bias = torch.zeros(vocab_size, dtype=torch.float32)
@@ -390,7 +397,7 @@ class CompletionRequest(OpenAIBaseModel):
     # doc: end-completion-extra-params
 
     def to_sampling_params(self,
-                           vocab_size: int = 32000,
+                           vocab_size: Optional[int] = None,
                            gather_generation_logits: bool = False,
                            backend: Optional[str] = None) -> SamplingParams:
         sampling_params = SamplingParams(
@@ -752,7 +759,7 @@ class ChatCompletionRequest(OpenAIBaseModel):
     # doc: end-chat-completion-extra-params
 
     def to_sampling_params(self,
-                           vocab_size: int = 32000,
+                           vocab_size: Optional[int] = None,
                            gather_generation_logits: bool = False,
                            reasoning_parser: Optional[str] = None,
                            backend: Optional[str] = None) -> SamplingParams:
diff --git a/tensorrt_llm/serve/openai_server.py b/tensorrt_llm/serve/openai_server.py
index e248ac96b1fa..278d9ef9ae29 100644
--- a/tensorrt_llm/serve/openai_server.py
+++ b/tensorrt_llm/serve/openai_server.py
@@ -516,6 +516,12 @@ def postproc_worker_enabled(self) -> bool:
 
         return True if self.generator.args.num_postprocess_workers > 0 else False
 
+    @property
+    def _vocab_size(self) -> Optional[int]:
+        if self.tokenizer is not None and self.tokenizer.tokenizer is not None:
+            return self.tokenizer.tokenizer.vocab_size
+        return None
+
     @staticmethod
     def create_error_response(
             message: str,
@@ -1043,7 +1049,7 @@ async def chat_stream_generator(
             # Pass the tokenizer vocabulary size so ``logit_bias`` can be
             # expanded into an embedding bias tensor in the sampler.
             sampling_params = request.to_sampling_params(
-                vocab_size=self.tokenizer.tokenizer.vocab_size,
+                vocab_size=self._vocab_size,
                 gather_generation_logits=self.generator.args.
                 gather_generation_logits,
                 reasoning_parser=self.generator.args.reasoning_parser,
@@ -1375,7 +1381,7 @@ async def generator_wrapper(generator: AsyncIterator[Any]):
             # Pass the tokenizer vocabulary size so ``logit_bias`` can be
             # expanded into an embedding bias tensor in the sampler.
             sampling_params = request.to_sampling_params(
-                vocab_size=self.tokenizer.tokenizer.vocab_size,
+                vocab_size=self._vocab_size,
                 gather_generation_logits=self.generator.args.
                 gather_generation_logits,
                 backend=self.generator.args.backend)
@@ -1510,8 +1516,7 @@ async def create_streaming_generator(promise: RequestOutput,
                 request.stop_token_ids = harmony_stop_tokens
 
             sampling_params = request.to_sampling_params(
-                vocab_size=self.tokenizer.tokenizer.vocab_size,
-                reasoning_parser="gpt_oss")
+                vocab_size=self._vocab_size, reasoning_parser="gpt_oss")
             sampling_params.detokenize = False  # Harmony adapter handles detokenization
             disaggregated_params = to_llm_disaggregated_params(
                 request.disaggregated_params)
diff --git a/tests/unittest/llmapi/apps/test_harmony_parsing.py b/tests/unittest/llmapi/apps/test_harmony_parsing.py
index acf51f73c1d6..7636e0edb90d 100644
--- a/tests/unittest/llmapi/apps/test_harmony_parsing.py
+++ b/tests/unittest/llmapi/apps/test_harmony_parsing.py
@@ -36,7 +36,8 @@
         get_harmony_adapter,
         handle_streaming_response,
     )
-    from tensorrt_llm.serve.openai_protocol import StreamOptions
+    from tensorrt_llm.serve.openai_protocol import StreamOptions, _logit_bias_to_embedding_bias
+    from tensorrt_llm.serve.openai_server import OpenAIServer
 
     _harmony_available = True
 except (ImportError, ModuleNotFoundError):
@@ -1192,5 +1193,13 @@ def test_stream_options_none_defaults_to_include(self):
                 harmony_adapter.cleanup_stream_state(request_id)
 
 
+def test_none_tokenizer_num_postprocess_workers():
+    server = object.__new__(OpenAIServer)
+    server.tokenizer = None
+    assert server._vocab_size is None
+    with pytest.raises(ValueError, match="logit_bias requires a tokenizer"):
+        _logit_bias_to_embedding_bias({"0": 1.0}, vocab_size=None)
+
+
 if __name__ == "__main__":
     pytest.main([__file__, "-v"])