diff --git a/vllm/model_executor/models/glm4v.py b/vllm/model_executor/models/glm4v.py
index 7584b5188cf2a..537aeabf72d5a 100644
--- a/vllm/model_executor/models/glm4v.py
+++ b/vllm/model_executor/models/glm4v.py
@@ -6,7 +6,7 @@
 """Inference-only CogAgent model compatible with THUDM weights."""
 from argparse import Namespace
 from collections.abc import Mapping, Sequence
-from typing import Literal, Optional, TypedDict, Union
+from typing import Annotated, Literal, Optional, Union
 
 import torch
 from torch import nn
@@ -38,6 +38,7 @@ from vllm.multimodal.processing import (BaseMultiModalProcessor,
 from vllm.multimodal.profiling import BaseDummyInputsBuilder
 from vllm.sequence import IntermediateTensors
 from vllm.transformers_utils.configs import ChatGLMConfig
+from vllm.utils.tensor_schema import TensorSchema, TensorShape
 
 from .chatglm import ChatGLMBaseModel, ChatGLMModel
 from .interfaces import (MultiModalEmbeddings, SupportsLoRA,
@@ -45,10 +46,16 @@ from .interfaces import (MultiModalEmbeddings, SupportsLoRA,
 from .utils import flatten_bn, merge_multimodal_embeddings
 
 
-class GLMVImagePixelInputs(TypedDict):
-    type: Literal["pixel_values"]
-    data: torch.Tensor
-    """Shape: `(batch_size, num_channels, height, width)`"""
+class GLMVImagePixelInputs(TensorSchema):
+    """
+    Dimensions:
+        - b: Batch size
+        - c: Number of channels (3)
+        - h: Height of image
+        - w: Width of image
+    """
+    type: Literal["pixel_values"] = "pixel_values"
+    data: Annotated[torch.Tensor, TensorShape("b", 3, "h", "w")]
 
 
 class EVA2CLIPPatchEmbedding(nn.Module):
@@ -562,19 +569,6 @@ class GLM4VForCausalLM(ChatGLMBaseModel, SupportsLoRA, SupportsPP,
 
         self.transformer: GLM4VModel
 
-    def _validate_pixel_values(self, data: torch.Tensor) -> torch.Tensor:
-        h = w = self.config.vision_config["image_size"]
-        expected_dims = (3, h, w)
-        actual_dims = tuple(data.shape[1:])
-
-        if actual_dims != expected_dims:
-            expected_expr = ("batch_size", *map(str, expected_dims))
-            raise ValueError(
-                f"The expected shape of pixel values is {expected_expr}. "
-                f"You supplied {tuple(data.shape)}.")
-
-        return data
-
     def _parse_and_validate_image_input(
             self, **kwargs: object) -> Optional[GLMVImagePixelInputs]:
         pixel_values = kwargs.pop("pixel_values", None)
@@ -584,11 +578,14 @@ class GLM4VForCausalLM(ChatGLMBaseModel, SupportsLoRA, SupportsPP,
                 raise ValueError("Incorrect type of pixel values. "
                                  f"Got type: {type(pixel_values)}")
 
-            return GLMVImagePixelInputs(
-                type="pixel_values",
-                data=self._validate_pixel_values(
-                    flatten_bn(pixel_values, concat=True)),
-            )
+            expected_h = expected_w = self.config.vision_config["image_size"]
+            return GLMVImagePixelInputs(type="pixel_values",
+                                        data=flatten_bn(pixel_values,
+                                                        concat=True),
+                                        resolve_bindings={
+                                            "h": expected_h,
+                                            "w": expected_w
+                                        })
 
         return None