diff --git a/python/sglang/srt/models/gemma3_mm.py b/python/sglang/srt/models/gemma3_mm.py
index a112417c5..7d776da0d 100644
--- a/python/sglang/srt/models/gemma3_mm.py
+++ b/python/sglang/srt/models/gemma3_mm.py
@@ -288,13 +288,22 @@ class Gemma3ForConditionalGeneration(PreTrainedModel):
                     "MM inputs where only some items are precomputed."
                 )
             return torch.concat([item.precomputed_features for item in items])
-        pixel_values = torch.stack(
-            flatten_nested_list([item.pixel_values for item in items]), dim=0
-        )
-        pixel_values = pixel_values.to(device=self.vision_tower.device)
-        pixel_values = pixel_values.to(dtype=self.language_model.dtype())
 
-        vision_outputs = self.vision_tower(pixel_values=pixel_values)
+        # Process images one by one to handle flatten_batch=True constraint in vision_tower
+        all_pixel_values = flatten_nested_list([item.pixel_values for item in items])
+        vision_outputs_list = []
+
+        for pixel_value in all_pixel_values:
+            # Add batch dimension for single image processing
+            pixel_value_batch = pixel_value.unsqueeze(0)
+            pixel_value_batch = pixel_value_batch.to(device=self.vision_tower.device)
+            pixel_value_batch = pixel_value_batch.to(dtype=self.language_model.dtype())
+
+            vision_output = self.vision_tower(pixel_values=pixel_value_batch)
+            vision_outputs_list.append(vision_output)
+
+        # Concatenate all vision outputs
+        vision_outputs = torch.cat(vision_outputs_list, dim=0)
         image_features = self.multi_modal_projector(vision_outputs)
         return image_features