diff --git a/python/sglang/srt/managers/schedule_batch.py b/python/sglang/srt/managers/schedule_batch.py
index 9681a1d70..8f3441b1b 100644
--- a/python/sglang/srt/managers/schedule_batch.py
+++ b/python/sglang/srt/managers/schedule_batch.py
@@ -43,6 +43,7 @@ import logging
 import re
 import time
 from enum import Enum, auto
+from functools import lru_cache
 from http import HTTPStatus
 from itertools import chain
 from typing import TYPE_CHECKING, Any, List, Optional, Set, Tuple, Union
@@ -95,10 +96,28 @@ if TYPE_CHECKING:
 
 INIT_INCREMENTAL_DETOKENIZATION_OFFSET = 5
 
+# Constant used as the base offset for MM (multimodal) pad values.
+# This ensures pad_values don't overlap with valid text token IDs.
+MM_PAD_SHIFT_VALUE = 1_000_000
 
 logger = logging.getLogger(__name__)
 
 
+@lru_cache(maxsize=1)
+def sanity_check_mm_pad_shift_value(vocab_size: int) -> None:
+    if vocab_size > MM_PAD_SHIFT_VALUE:
+        raise ValueError(
+            f"Model vocab_size ({vocab_size}) exceeds MM_PAD_SHIFT_VALUE ({MM_PAD_SHIFT_VALUE}). "
+            f"MM pad_values may overlap with valid token IDs. "
+            f"Please increase MM_PAD_SHIFT_VALUE in schedule_batch.py."
+        )
+
+
+def _compute_pad_value(hash: int) -> int:
+    """Compute pad value from hash."""
+    return MM_PAD_SHIFT_VALUE + (hash % (1 << 30))
+
+
 class BaseFinishReason:
     def __init__(self, is_error: bool = False):
         self.is_error = is_error
@@ -262,7 +281,7 @@ class MultimodalDataItem:
             import uuid
 
             self.hash = uuid.uuid4().int
-            self.pad_value = self.hash % (1 << 30)
+            self.pad_value = _compute_pad_value(self.hash)
             return
         if self.hash is None:
             if self.feature is not None:
@@ -271,7 +290,7 @@ class MultimodalDataItem:
                 hashed_feature = self.precomputed_embeddings
             self.hash = hash_feature(hashed_feature)
         assert self.hash is not None
-        self.pad_value = self.hash % (1 << 30)
+        self.pad_value = _compute_pad_value(self.hash)
 
     def is_modality(self, modality: Modality) -> bool:
         return self.modality == modality
diff --git a/python/sglang/srt/model_executor/model_runner.py b/python/sglang/srt/model_executor/model_runner.py
index 11d60729d..01d6c8866 100644
--- a/python/sglang/srt/model_executor/model_runner.py
+++ b/python/sglang/srt/model_executor/model_runner.py
@@ -106,6 +106,7 @@ from sglang.srt.layers.sampler import create_sampler
 from sglang.srt.layers.torchao_utils import apply_torchao_config_to_model
 from sglang.srt.lora.lora_manager import LoRAManager
 from sglang.srt.lora.lora_registry import LoRARef
+from sglang.srt.managers.schedule_batch import sanity_check_mm_pad_shift_value
 from sglang.srt.mem_cache.allocator import BaseTokenToKVPoolAllocator
 from sglang.srt.mem_cache.memory_pool import ReqToTokenPool
 from sglang.srt.model_executor.cpu_graph_runner import CPUGraphRunner
@@ -390,6 +391,9 @@ class ModelRunner(ModelRunnerKVCacheMixin):
         self.initialize(min_per_gpu_memory)
         self.check_quantized_moe_compatibility()
 
+        if self.is_multimodal:
+            sanity_check_mm_pad_shift_value(self.model_config.vocab_size)
+
         # Temporary cached values
         self.support_pp = (
             "pp_proxy_tensors" in inspect.signature(self.model.forward).parameters