diff --git a/vllm/config.py b/vllm/config.py
index 67a4ec0761cc3..df892ca7936d8 100644
--- a/vllm/config.py
+++ b/vllm/config.py
@@ -968,6 +968,11 @@ class SchedulerConfig:
         policy: The scheduling policy to use. "fcfs" (default) or "priority".
         use_padding_aware_scheduling: If True, scheduler will consider padded
             tokens in prefill.
+        max_padding_ratio: Prevents scheduling batches with large paddings. 
+            0.2 means that up to 20% tokens in batch can be dedicated
+            to padding, and the remaining 80% must be usable data
+            Requires padding-aware scheduling.
+            Must be in range (0, 1). Not set by default
     """
 
     def __init__(self,
@@ -986,7 +991,8 @@ def __init__(self,
                  multi_step_stream_outputs: bool = False,
                  send_delta_data: bool = False,
                  policy: str = "fcfs",
-                 use_padding_aware_scheduling=False) -> None:
+                 use_padding_aware_scheduling=False,
+                 max_padding_ratio=None) -> None:
         if max_num_batched_tokens is None:
             if enable_chunked_prefill:
                 if num_scheduler_steps > 1:
@@ -1038,6 +1044,7 @@ def __init__(self,
         self.send_delta_data = send_delta_data
         self.policy = policy
         self.use_padding_aware_scheduling = use_padding_aware_scheduling
+        self.max_padding_ratio = max_padding_ratio
         self._verify_args()
 
     def _verify_args(self) -> None:
@@ -1072,6 +1079,10 @@ def _verify_args(self) -> None:
             and not self.use_padding_aware_scheduling:
             raise ValueError("max_num_prefill_seqs can be only "
                              "used with padding-aware-scheduling. ")
+        if self.max_padding_ratio is not None \
+            and not self.use_padding_aware_scheduling:
+            raise ValueError("max_padding_ratio can be only "
+                             "used with padding-aware-scheduling. ")
         if self.use_padding_aware_scheduling and self.chunked_prefill_enabled:
             raise ValueError("Padding-aware scheduling currently "
                              "does not work with chunked prefill ")
diff --git a/vllm/core/scheduler.py b/vllm/core/scheduler.py
index 1c69c72933b79..1608c13aa7b40 100644
--- a/vllm/core/scheduler.py
+++ b/vllm/core/scheduler.py
@@ -105,6 +105,7 @@ def num_curr_seqs(self):
 @dataclass
 class PaddingAwareSchedulingBudget(SchedulingBudget):
     max_num_prefill_seqs: Optional[int] = None
+    max_padding_ratio: Optional[int] = None
     _prefill_request_ids_max_seq_lens: Dict[str,
                                             int] = field(default_factory=dict)
     _max_seq_len: int = 0
@@ -179,6 +180,14 @@ def can_schedule(self,
         if self.max_num_prefill_seqs is not None and result:
             result = self._num_curr_prefill_seqs + num_new_seqs \
                 <= self.max_num_prefill_seqs
+        if self._num_curr_prefill_seqs != 0 and self.max_padding_ratio is not None and result:
+            num_tokens = self.num_batched_tokens + num_new_tokens
+            padding_ratio = 1 - num_tokens / num_new_padded_tokens
+            result = padding_ratio < self.max_padding_ratio
+            if not result:
+                print(
+                    f"[PaddingAwareSchedulerDebug] CANNOT schedule, exceeded max padding ratio {self.max_padding_ratio} (num_tokens = {num_tokens}, num_padded_tokens = {num_new_padded_tokens}, ratio = {padding_ratio:.2f})"  # noqa: E501
+                )
         return result
 
     @property
@@ -1099,8 +1108,9 @@ def _schedule_default(self) -> SchedulerOutputs:
             budget = PaddingAwareSchedulingBudget(
                 token_budget=self.scheduler_config.max_num_batched_tokens,
                 max_num_seqs=self.scheduler_config.max_num_seqs,
-                max_num_prefill_seqs=self.scheduler_config.max_num_prefill_seqs
-            )
+                max_num_prefill_seqs=self.scheduler_config.
+                max_num_prefill_seqs,
+                max_padding_ratio=self.scheduler_config.max_padding_ratio)
         else:
             budget = SchedulingBudget(
                 token_budget=self.scheduler_config.max_num_batched_tokens,
diff --git a/vllm/engine/arg_utils.py b/vllm/engine/arg_utils.py
index cdf1401816800..012f24162e114 100644
--- a/vllm/engine/arg_utils.py
+++ b/vllm/engine/arg_utils.py
@@ -114,6 +114,7 @@ class EngineArgs:
     disable_sliding_window: bool = False
     use_v2_block_manager: bool = True
     use_padding_aware_scheduling: bool = False
+    max_padding_ratio: Optional[float] = None
     swap_space: float = 4  # GiB
     cpu_offload_gb: float = 0  # GiB
     gpu_memory_utilization: float = 0.90
@@ -400,6 +401,15 @@ def add_cli_args(parser: FlexibleArgumentParser) -> FlexibleArgumentParser:
             help=('Use padding-aware scheduling. If True, the scheduler '
                   'will consider padded tokens in prefill. '
                   'By default this is set to False. '))
+        parser.add_argument(
+            '--max-padding-ratio',
+            default=EngineArgs.max_padding_ratio,
+            action='store_true',
+            help=('Prevents scheduling batches with large paddings.'
+                  '0.2 means that up to 20% tokens in batch can be dedicated'
+                  'to padding, and the remaining 80% must be usable data'
+                  'Requires padding-aware scheduling. '
+                  'Must be in range (0, 1). Not set by default'))
         parser.add_argument(
             '--num-lookahead-slots',
             type=int,
@@ -1066,7 +1076,8 @@ def create_engine_config(self) -> EngineConfig:
             send_delta_data=(envs.VLLM_USE_RAY_SPMD_WORKER
                              and parallel_config.use_ray),
             policy=self.scheduling_policy,
-            use_padding_aware_scheduling=self.use_padding_aware_scheduling)
+            use_padding_aware_scheduling=self.use_padding_aware_scheduling,
+            max_padding_ratio=self.max_padding_ratio)
         lora_config = LoRAConfig(
             max_lora_rank=self.max_lora_rank,
             max_loras=self.max_loras,