diff --git a/vllm/model_executor/models/kimi_vl.py b/vllm/model_executor/models/kimi_vl.py
index 9c0a6ba92389..1c7ddd7df7f8 100644
--- a/vllm/model_executor/models/kimi_vl.py
+++ b/vllm/model_executor/models/kimi_vl.py
@@ -46,7 +46,7 @@ import copy
 import math
 from collections.abc import Iterable, Mapping, Sequence
 from dataclasses import dataclass
-from typing import Any, Literal, Optional, TypedDict, Union
+from typing import Annotated, Any, Literal, Optional, Union
 
 import torch
 from torch import nn
@@ -79,6 +79,7 @@ from vllm.multimodal.profiling import BaseDummyInputsBuilder
 from vllm.sequence import IntermediateTensors
 from vllm.transformers_utils.configs import KimiVLConfig, MoonViTConfig
 from vllm.transformers_utils.configs.deepseek_vl2 import DeepseekV2Config
+from vllm.utils.tensor_schema import TensorSchema, TensorShape
 
 from .utils import is_pp_missing_parameter, maybe_prefix
 
@@ -118,15 +119,22 @@ class KimiVLMultiModalProjector(nn.Module):
         return hidden_states
 
 
-class KimiVLImagePixelInputs(TypedDict):
-    type: Literal["pixel_values"]
-    pixel_values: Union[torch.Tensor, list[torch.Tensor]]
+class KimiVLImagePixelInputs(TensorSchema):
     """
-    Shape:`(num_patches, num_channels, patch_size, patch_size)`
+    Dimensions:
+        - nc: Number of channels
+        - np: Number of patches
+        - ps: Patch size
+        - ni: Number of images
     """
+    type: Literal["pixel_values"] = "pixel_values"
 
-    image_grid_hws: torch.Tensor
-    """Shape:`(num_images, 2)`"""
+    pixel_values: Annotated[
+        Union[torch.Tensor, list[torch.Tensor]],
+        TensorShape("np", 3, "ps", "ps"),
+    ]
+
+    image_grid_hws: Annotated[torch.Tensor, TensorShape("ni", 2)]
 
 
 # TODO: support embeds too
@@ -348,8 +356,6 @@ class KimiVLForConditionalGeneration(nn.Module, SupportsMultiModal):
             pixel_values = pixel_values.reshape(-1, num_channels, patch_size,
                                                 patch_size)
         pixel_values = pixel_values.to(self.vision_tower.dtype)
-        # image_grid_hws.shape = (N, 2)
-        assert image_grid_hws.ndim == 2, f"unexpected shape for image_grid_hws: {image_grid_hws.shape}"
 
         return KimiVLImagePixelInputs(
             type="pixel_values",