diff --git a/keras/src/ops/operation_utils.py b/keras/src/ops/operation_utils.py
index b1ac2621de0a..a997c9e1d448 100644
--- a/keras/src/ops/operation_utils.py
+++ b/keras/src/ops/operation_utils.py
@@ -115,40 +115,77 @@ def compute_pooling_output_shape(
     (32, 2, 2, 3)
     """
     strides = pool_size if strides is None else strides
-    input_shape_origin = list(input_shape)
-    input_shape = np.array(input_shape)
+
+    # Use tuple instead of list for input_shape_origin (to avoid unnecessary copy)
+    input_shape_origin = tuple(input_shape)
+    # Only convert to numpy if needed later
     if data_format == "channels_last":
         spatial_shape = input_shape[1:-1]
     else:
         spatial_shape = input_shape[2:]
     none_dims = []
-    for i in range(len(spatial_shape)):
-        if spatial_shape[i] is None:
-            # Set `None` shape to a manual value so that we can run numpy
-            # computation on `spatial_shape`.
-            spatial_shape[i] = -1
+    have_none = False
+
+    # Fast path: all dimensions known and integers
+    for i, dim in enumerate(spatial_shape):
+        if dim is None:
+            have_none = True
             none_dims.append(i)
-    pool_size = np.array(pool_size)
-    if padding == "valid":
-        output_spatial_shape = (
-            np.floor((spatial_shape - pool_size) / strides) + 1
+
+    # If there are None dimensions, we must use numpy, otherwise stick with ints
+    # This saves most allocations for the common case
+    if have_none:
+        spatial_shape_arr = np.array(
+            [(-1 if dim is None else dim) for dim in spatial_shape],
+            dtype=np.intp
         )
-        for i in range(len(output_spatial_shape)):
-            if i not in none_dims and output_spatial_shape[i] < 0:
-                raise ValueError(
-                    "Computed output size would be negative. Received: "
-                    f"`inputs.shape={input_shape}` and `pool_size={pool_size}`."
-                )
-    elif padding == "same":
-        output_spatial_shape = np.floor((spatial_shape - 1) / strides) + 1
+        pool_size_arr = np.array(pool_size, dtype=np.intp)
+        strides_arr = np.array(strides, dtype=np.intp)
+        if padding == "valid":
+            output_spatial_shape = np.floor_divide(
+                spatial_shape_arr - pool_size_arr, strides_arr
+            ) + 1
+            for i in range(len(output_spatial_shape)):
+                if i not in none_dims and output_spatial_shape[i] < 0:
+                    raise ValueError(
+                        "Computed output size would be negative. Received: "
+                        f"`inputs.shape={input_shape}` and `pool_size={pool_size}`."
+                    )
+        elif padding == "same":
+            output_spatial_shape = np.floor_divide(
+                spatial_shape_arr - 1, strides_arr
+            ) + 1
+        else:
+            raise ValueError(
+                "Argument `padding` must be either 'valid' or 'same'. Received: "
+                f"padding={padding}"
+            )
+        output_spatial_shape = [int(i) if i != -1 else None for i in output_spatial_shape]
     else:
-        raise ValueError(
-            "Argument `padding` must be either 'valid' or 'same'. Received: "
-            f"padding={padding}"
-        )
-    output_spatial_shape = [int(i) for i in output_spatial_shape]
-    for i in none_dims:
-        output_spatial_shape[i] = None
+        # No Nones; use only native int math, no numpy
+        if padding == "valid":
+            output_spatial_shape = []
+            for i, (dim, psize, stride) in enumerate(zip(spatial_shape, pool_size, strides)):
+                val = (dim - psize) // stride + 1
+                if val < 0:
+                    raise ValueError(
+                        "Computed output size would be negative. Received: "
+                        f"`inputs.shape={input_shape}` and `pool_size={pool_size}`."
+                    )
+                output_spatial_shape.append(val)
+        elif padding == "same":
+            output_spatial_shape = [
+                (dim - 1) // stride + 1
+                for dim, stride in zip(spatial_shape, strides)
+            ]
+        else:
+            raise ValueError(
+                "Argument `padding` must be either 'valid' or 'same'. Received: "
+                f"padding={padding}"
+            )
+    # Assign back None for unknown dims
+    for idx in none_dims:
+        output_spatial_shape[idx] = None
     output_spatial_shape = tuple(output_spatial_shape)
     if data_format == "channels_last":
         output_shape = (