diff --git a/vllm/model_executor/models/mllama4.py b/vllm/model_executor/models/mllama4.py
index ac9b968f7a0cd..ecbbb5f57bec8 100644
--- a/vllm/model_executor/models/mllama4.py
+++ b/vllm/model_executor/models/mllama4.py
@@ -19,7 +19,7 @@
 import math
 from collections.abc import Iterable, Mapping
 from itertools import tee
-from typing import Literal, Optional, TypedDict, Union
+from typing import Annotated, Literal, Optional, Union
 
 import torch
 from torch import nn
@@ -53,6 +53,7 @@ from vllm.multimodal.processing import (BaseMultiModalProcessor,
 from vllm.multimodal.profiling import BaseDummyInputsBuilder
 from vllm.multimodal.utils import run_dp_sharded_vision_model
 from vllm.sequence import IntermediateTensors
+from vllm.utils.tensor_schema import TensorSchema, TensorShape
 
 from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP
 from .llama4 import Llama4ForCausalLM
@@ -60,28 +61,34 @@ from .utils import (AutoWeightsLoader, flatten_bn, maybe_prefix,
                     merge_multimodal_embeddings)
 
 
-class Llama4ImagePatchInputs(TypedDict):
-    type: Literal["pixel_values"]
-    flat_data: torch.Tensor
+class Llama4ImagePatchInputs(TensorSchema):
     """
-    Shape:
-    `(batch_size * num_chunks, num_channels, image size, image size)`
+    Dimensions:
+        - batch_size: Batch size
+        - total_num_chunks: Batch size * number of chunks
+        - num_channels: Number of channels
+        - image_size: Size of each image
     """
-    patches_per_image: torch.Tensor
+
+    type: Literal["pixel_values"] = "pixel_values"
+
+    flat_data: Annotated[torch.Tensor,
+                         TensorShape("total_num_chunks", "num_channels",
+                                     "image_size", "image_size")]
+
+    patches_per_image: Annotated[torch.Tensor, TensorShape("batch_size")]
     """
     The number of total patches for each image in the batch.
-
+    
     This is used to split the embeddings which has the first two dimensions
     flattened just like `flat_data`.
     """
 
-    aspect_ratios: Union[torch.Tensor, list[torch.Tensor]]
+    aspect_ratios: Annotated[torch.Tensor, TensorShape("batch_size", 2)]
     """
     A list of aspect ratios corresponding to the number of tiles
     in each dimension that each image in the batch corresponds to.
-
-    Shape:
-    `(batch_size, ratio)` where ratio is a pair `(ratio_h, ratio_w)`
+    Each aspect ratio is a pair (ratio_h, ratio_w).
     """
 
 
@@ -623,7 +630,7 @@ class Mllama4MultiModalProcessor(BaseMultiModalProcessor[Mllama4ProcessingInfo]
                 for (r_h, r_w) in aspect_ratios
             ]
 
-            processed_outputs["aspect_ratios"] = aspect_ratios
+            processed_outputs["aspect_ratios"] = torch.tensor(aspect_ratios)
             processed_outputs["patches_per_image"] = torch.tensor(
                 patches_per_image)
 
@@ -770,11 +777,9 @@ class Llama4ForConditionalGeneration(nn.Module, SupportsMultiModal,
         # TODO: confirm handling for variable lengths
         flat_pixel_values = flatten_bn(pixel_values, concat=True)
         patches_per_image = flatten_bn(kwargs.pop("patches_per_image"))
-
-        aspect_ratios = kwargs.pop("aspect_ratios", None)
-        if not isinstance(aspect_ratios, (torch.Tensor, list)):
-            raise ValueError("Incorrect type of aspect_ratios. "
-                             f"Got type: {type(aspect_ratios)}")
+        aspect_ratios = kwargs.pop("aspect_ratios")
+        if aspect_ratios.ndim == 3:
+            aspect_ratios = aspect_ratios.squeeze(1)
 
         return Llama4ImagePatchInputs(
             type="pixel_values",