diff --git a/vllm/model_executor/models/molmo.py b/vllm/model_executor/models/molmo.py
index 6a08d2793fd03..5fc28ed0e493e 100644
--- a/vllm/model_executor/models/molmo.py
+++ b/vllm/model_executor/models/molmo.py
@@ -5,7 +5,7 @@ import math
 from collections.abc import Iterable, Mapping, Sequence
 from dataclasses import dataclass
 from functools import cached_property, partial
-from typing import Optional, TypedDict, Union
+from typing import Annotated, Optional, Union
 
 import numpy as np
 import torch
@@ -51,6 +51,7 @@ from vllm.multimodal.processing import (BaseMultiModalProcessor,
                                         PromptUpdateDetails)
 from vllm.multimodal.profiling import BaseDummyInputsBuilder
 from vllm.sequence import IntermediateTensors
+from vllm.utils.tensor_schema import TensorSchema, TensorShape
 
 from .interfaces import (MultiModalEmbeddings, SupportsLoRA,
                          SupportsMultiModal, SupportsPP, SupportsQuant)
@@ -70,23 +71,25 @@ IM_END_TOKEN = "<im_end>"
 POOLING_SIZE = 2
 
 
-class MolmoImageInputs(TypedDict):
-    images: Union[torch.Tensor, list[torch.Tensor]]
-    """Shape: `(batch_size * num_images, num_crops, num_patch, patch_dim)`"""
-
-    image_masks: Optional[Union[torch.Tensor, list[torch.Tensor]]]
-    """Shape: `(batch_size * num_images, num_crops, num_patch)`"""
-
-    feat_is_patch: Union[torch.Tensor, list[torch.Tensor]]
+class MolmoImageInputs(TensorSchema):
     """
-    A boolean mask indicating which image features correspond
-    to patch tokens.
-
-    Shape: `(batch_size * num_images, num_crops, num_patch)`
+    Dimensions:
+        - bn: Batch size * number of images
+        - nc: Number of crops
+        - np: Number of patches
+        - pd: Patch dimension
     """
+    images: Annotated[Union[torch.Tensor, list[torch.Tensor]],
+                      TensorShape("bn", "nc", "np", "pd")]
 
-    num_crops: torch.Tensor
-    """Shape: `(batch_size * num_images)`"""
+    image_masks: Annotated[Optional[Union[torch.Tensor, list[torch.Tensor]]],
+                           TensorShape("bn", "nc", "np")]
+
+    feat_is_patch: Annotated[Union[torch.Tensor, list[torch.Tensor]],
+                             TensorShape("bn", "nc", "np")]
+    # A boolean mask indicating which image features correspond to patch tokens.
+
+    num_crops: Annotated[torch.Tensor, TensorShape("bn")]
 
 
 @dataclass
@@ -1410,28 +1413,17 @@ class MolmoForCausalLM(nn.Module, SupportsMultiModal, SupportsPP, SupportsLoRA,
         **kwargs: object,
     ) -> Optional[MolmoImageInputs]:
         images = kwargs.pop("images", None)
+        image_masks = kwargs.pop("image_masks", None)
+        feat_is_patch = kwargs.pop("feat_is_patch", None)
+        num_crops = kwargs.pop("num_crops", None)
+
         if images is None:
             return None
 
-        if not isinstance(images, (torch.Tensor, list)):
-            raise ValueError("Incorrect type of images. "
-                             f"Got type: {type(images)}")
-
-        image_masks = kwargs.pop("image_masks", None)
-        if not (image_masks is None or isinstance(image_masks,
-                                                  (torch.Tensor, list))):
-            raise ValueError("Incorrect type of image_masks. "
-                             f"Got type: {type(image_masks)}")
-
-        feat_is_patch = kwargs.pop("feat_is_patch", None)
-        if not isinstance(feat_is_patch, (torch.Tensor, list)):
-            raise ValueError("Incorrect type of feat_is_patch. "
-                             f"Got type: {type(feat_is_patch)}")
-
-        num_crops = kwargs.pop("num_crops", None)
         if not isinstance(num_crops, (torch.Tensor, list)):
             raise ValueError("Incorrect type of num_crops. "
                              f"Got type: {type(num_crops)}")
+        num_crops = flatten_bn(num_crops, concat=True)
 
         img_patch_id = kwargs.pop("img_patch_id", None)
         if not isinstance(img_patch_id, torch.Tensor):
@@ -1439,8 +1431,6 @@ class MolmoForCausalLM(nn.Module, SupportsMultiModal, SupportsPP, SupportsLoRA,
                              f"Got type: {type(img_patch_id)}")
         self.img_patch_id = img_patch_id.flatten().unique().item()
 
-        num_crops = flatten_bn(num_crops, concat=True)
-
         return MolmoImageInputs(
             images=images,
             image_masks=image_masks,