diff --git a/fastdeploy/engine/config.py b/fastdeploy/engine/config.py
index 02df10328..3326e8321 100644
--- a/fastdeploy/engine/config.py
+++ b/fastdeploy/engine/config.py
@@ -854,6 +854,11 @@ class Config:
                 self.max_num_batched_tokens >= self.max_model_len
             ), f"max_num_batched_tokens: {self.max_num_batched_tokens} " \
                 f"should be larger than or equal to max_model_len: {self.max_model_len}"
+        else:
+            assert (
+                self.max_num_batched_tokens >= self.cache_config.block_size
+            ), f"max_num_batched_tokens: {self.max_num_batched_tokens} " \
+                f"should be larger than or equal to block_size: {self.cache_config.block_size}"
 
         if self.max_num_partial_prefills > 1:
             assert (self.cache_config.enable_chunked_prefill is True), \
diff --git a/fastdeploy/engine/engine.py b/fastdeploy/engine/engine.py
index 6a5d30d21..414a7b209 100644
--- a/fastdeploy/engine/engine.py
+++ b/fastdeploy/engine/engine.py
@@ -134,6 +134,7 @@ class LLMEngine(object):
         for idx in range(1, self.cfg.max_num_partial_prefills + 1):
             self.partial_chunked_tokens[idx] = (self.cfg.max_num_batched_tokens // idx) \
                 // self.cfg.cache_config.block_size * self.cfg.cache_config.block_size
+            self.partial_chunked_tokens[idx] = max(1, self.partial_chunked_tokens[idx])
 
         self._finalizer = weakref.finalize(self, self._exit_sub_services)
 
diff --git a/fastdeploy/worker/worker_process.py b/fastdeploy/worker/worker_process.py
index e273d0714..6123a37b4 100644
--- a/fastdeploy/worker/worker_process.py
+++ b/fastdeploy/worker/worker_process.py
@@ -394,6 +394,18 @@ class PaddleDisWorkerProc():
                 time.sleep(0.01)
             num_blocks_global = self.get_profile_block_num_signal.value.min(
             ).item()
+
+            if num_blocks_global < 0:
+                logger.error(
+                    f"The total number of blocks cannot be less than zero."
+                    f"Please increase gpu_memory_utilization"
+                    f"Or decrease max_num_batched_tokens(max model length) ")
+                raise ValueError(
+                    f"The total number of blocks cannot be less than zero."
+                    f"Please increase gpu_memory_utilization"
+                    f"Or decrease max_num_batched_tokens(max model length) ")
+        
+
             self.get_profile_block_num_signal.value[
                 self.local_rank] = num_blocks_global
         else: