diff --git a/vllm/model_executor/models/deepseek_vl2.py b/vllm/model_executor/models/deepseek_vl2.py
index c58b65d49348..ea217e244404 100644
--- a/vllm/model_executor/models/deepseek_vl2.py
+++ b/vllm/model_executor/models/deepseek_vl2.py
@@ -25,7 +25,8 @@ from vllm.multimodal.inputs import (MultiModalFieldConfig, MultiModalKwargs,
 from vllm.multimodal.parse import (ImageEmbeddingItems, ImageProcessorItems,
                                    ImageSize, MultiModalDataItems)
 from vllm.multimodal.processing import (BaseMultiModalProcessor,
-                                        BaseProcessingInfo, PromptReplacement)
+                                        BaseProcessingInfo, ProcessingCache,
+                                        PromptReplacement)
 from vllm.multimodal.profiling import BaseDummyInputsBuilder, ProcessorInputs
 from vllm.sequence import IntermediateTensors
 from vllm.transformers_utils.configs.deepseek_vl2 import (DeepseekVLV2Config,
@@ -138,18 +139,24 @@ class DeepseekVL2ProcessingInfo(BaseProcessingInfo):
     def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
         return {"image": None}
 
-    def get_num_image_tokens(self, *, image_width: int,
-                             image_height: int) -> int:
+    def get_num_image_tokens(self,
+                             *,
+                             image_width: int,
+                             image_height: int,
+                             cropping: bool = True) -> int:
         hf_processor = self.get_hf_processor()
         image_size = hf_processor.image_size
         patch_size = hf_processor.patch_size
         downsample_ratio = hf_processor.downsample_ratio
 
-        best_width, best_height = hf_processor.select_best_resolution(
-            (image_width, image_height))
+        if cropping:
+            best_width, best_height = hf_processor.select_best_resolution(
+                (image_width, image_height))
+            num_width_tiles, num_height_tiles = (best_width // image_size,
+                                                 best_height // image_size)
+        else:
+            num_width_tiles = num_height_tiles = 1
 
-        num_width_tiles, num_height_tiles = (best_width // image_size,
-                                             best_height // image_size)
         h = w = math.ceil((image_size // patch_size) / downsample_ratio)
 
         global_views_tokens = h * (w + 1)
@@ -169,10 +176,12 @@ class DeepseekVL2ProcessingInfo(BaseProcessingInfo):
         seq_len: int,
         mm_counts: Mapping[str, int],
     ) -> Mapping[str, int]:
+        num_images = mm_counts.get("image", 0)
         max_image_size = self.get_image_size_with_most_features()
         max_image_tokens = self.get_num_image_tokens(
             image_height=max_image_size.height,
-            image_width=max_image_size.width)
+            image_width=max_image_size.width,
+            cropping=num_images <= 2)
 
         return {"image": max_image_tokens}
 
@@ -207,6 +216,30 @@ class DeepseekVL2DummyInputsBuilder(
 class DeepseekVL2MultiModalProcessor(
         BaseMultiModalProcessor[DeepseekVL2ProcessingInfo]):
 
+    def __init__(
+            self,
+            info: DeepseekVL2ProcessingInfo,
+            dummy_inputs: "BaseDummyInputsBuilder[DeepseekVL2ProcessingInfo]",
+            *,
+            cache: Optional[ProcessingCache] = None,
+            enable_sanity_checks: bool = True) -> None:
+        super().__init__(
+            info,
+            dummy_inputs,
+            cache=cache,
+            enable_sanity_checks=enable_sanity_checks,
+        )
+
+        mm_limit = self.info.ctx.model_config.multimodal_config.limit_per_prompt
+        if self.cache is not None and mm_limit["image"] > 2:
+            # The processor output depends on the number of images passed,
+            # making it incompatible with processing cache which is supposed
+            # to be invariant of how many images are passed per prompt
+            self.cache = None
+            logger.warning_once(
+                f"{type(self).__name__} does not support processing cache with "
+                "image limit larger than 2.")
+
     def _call_hf_processor(
         self,
         prompt: str,
@@ -271,6 +304,7 @@ class DeepseekVL2MultiModalProcessor(
                 num_image_tokens = self.info.get_num_image_tokens(
                     image_width=image_size.width,
                     image_height=image_size.height,
+                    cropping=len(images) <= 2,
                 )
             return [image_token_id] * num_image_tokens
 
diff --git a/vllm/model_executor/models/h2ovl.py b/vllm/model_executor/models/h2ovl.py
index 01b721fa79e1..bab9c256b9aa 100644
--- a/vllm/model_executor/models/h2ovl.py
+++ b/vllm/model_executor/models/h2ovl.py
@@ -477,13 +477,15 @@ class H2OVLMultiModalProcessor(InternVLMultiModalProcessor[H2OVLProcessingInfo]
             enable_sanity_checks=enable_sanity_checks,
         )
 
-        if self.cache is not None:
+        mm_limit = self.info.ctx.model_config.multimodal_config.limit_per_prompt
+        if self.cache is not None and mm_limit["image"] >= 2:
             # The processor output depends on the number of images passed,
             # making it incompatible with processing cache which is supposed
             # to be invariant of how many images are passed per prompt
             self.cache = None
             logger.warning_once(
-                f"{type(self).__name__} does not support processing cache.")
+                f"{type(self).__name__} does not support processing cache with "
+                "multi-image support enabled.")
 
     def _get_prompt_replacements(
         self,