diff --git a/examples/online_serving/openai_chat_completion_client_for_multimodal.py b/examples/online_serving/openai_chat_completion_client_for_multimodal.py
index ac5f79b56e49f..37216a5cfe574 100644
--- a/examples/online_serving/openai_chat_completion_client_for_multimodal.py
+++ b/examples/online_serving/openai_chat_completion_client_for_multimodal.py
@@ -266,10 +266,52 @@ def run_audio(model: str) -> None:
     print("Chat completion output from base64 encoded audio:", result)
 
 
+def run_multi_audio(model: str) -> None:
+    from vllm.assets.audio import AudioAsset
+
+    # Two different audios to showcase batched inference.
+    audio_url = AudioAsset("winning_call").url
+    audio_base64 = encode_base64_content_from_url(audio_url)
+    audio_url2 = AudioAsset("azacinto_foscolo").url
+    audio_base64_2 = encode_base64_content_from_url(audio_url2)
+
+    # OpenAI-compatible schema (`input_audio`)
+    chat_completion_from_base64 = client.chat.completions.create(
+        messages=[
+            {
+                "role": "user",
+                "content": [
+                    {"type": "text", "text": "Are these two audios the same?"},
+                    {
+                        "type": "input_audio",
+                        "input_audio": {
+                            "data": audio_base64,
+                            "format": "wav",
+                        },
+                    },
+                    {
+                        "type": "input_audio",
+                        "input_audio": {
+                            "data": audio_base64_2,
+                            "format": "wav",
+                        },
+                    },
+                ],
+            }
+        ],
+        model=model,
+        max_completion_tokens=64,
+    )
+
+    result = chat_completion_from_base64.choices[0].message.content
+    print("Chat completion output from input audio:", result)
+
+
 example_function_map = {
     "text-only": run_text_only,
     "single-image": run_single_image,
     "multi-image": run_multi_image,
+    "multi-audio": run_multi_audio,
     "video": run_video,
     "audio": run_audio,
 }
diff --git a/vllm/model_executor/models/gemma3n_mm.py b/vllm/model_executor/models/gemma3n_mm.py
index c25bbcd420c39..d831e9084db57 100644
--- a/vllm/model_executor/models/gemma3n_mm.py
+++ b/vllm/model_executor/models/gemma3n_mm.py
@@ -5,6 +5,7 @@ from typing import Any, Literal, Optional, TypedDict, Union, cast
 
 import numpy as np
 import torch
+# yapf: disable
 from torch import nn
 from transformers import AutoModel, BatchFeature
 from transformers.models.gemma3n import (Gemma3nAudioConfig,
@@ -30,7 +31,6 @@ from vllm.multimodal.inputs import (MultiModalDataDict, MultiModalFieldConfig,
                                     MultiModalKwargsItems)
 from vllm.multimodal.parse import (ImageProcessorItems, MultiModalDataItems,
                                    MultiModalDataParser)
-# yapf: disable
 from vllm.multimodal.processing import (BaseMultiModalProcessor,
                                         BaseProcessingInfo,
                                         MultiModalPromptUpdates,
@@ -62,7 +62,8 @@ class Gemma3nImagePixelInputs(TypedDict):
 
 
 class Gemma3nAudioInputs(TypedDict):
-    input_features: torch.Tensor
+    input_features: Union[torch.Tensor, list[torch.Tensor]]
+    input_features_padded: torch.Tensor
     """Shape: `(batch_size * num_audio, seq_length, num_features)`"""
     input_features_mask: torch.Tensor
     """Shape: `(batch_size * num_audio, seq_length)`"""
@@ -188,8 +189,13 @@ class Gemma3nMultiModalProcessor(BaseMultiModalProcessor[Gemma3nProcessingInfo]
             mm_kwargs,
             tok_kwargs,
         )
+
         if 'input_features' in processed_outputs:
-            # Avoid padding since we need the output of each item to be
+            # Padding enables audio_tower to run in batched mode
+            processed_outputs["input_features_padded"] = \
+                processed_outputs["input_features"]
+
+            # Unpad features here since we need the output of each item to be
             # independent of other items for the cache to work correctly
             unpadded_features = [
                 f[mask] for f, mask in zip(
@@ -206,9 +212,11 @@ class Gemma3nMultiModalProcessor(BaseMultiModalProcessor[Gemma3nProcessingInfo]
         hf_processor_mm_kwargs: Mapping[str, object],
     ) -> Mapping[str, MultiModalFieldConfig]:
 
-        return dict(pixel_values=MultiModalFieldConfig.batched("image"),
-                    input_features=MultiModalFieldConfig.batched("audio"),
-                    input_features_mask=MultiModalFieldConfig.batched("audio"))
+        return dict(
+            pixel_values=MultiModalFieldConfig.batched("image"),
+            input_features=MultiModalFieldConfig.batched("audio"),
+            input_features_padded=MultiModalFieldConfig.batched("audio"),
+            input_features_mask=MultiModalFieldConfig.batched("audio"))
 
     def _get_prompt_updates(
         self,
@@ -516,9 +524,14 @@ class Gemma3nForConditionalGeneration(nn.Module, SupportsMultiModal,
         if input_features_mask is None:
             return None
 
+        input_features_padded = kwargs.pop("input_features_padded", None)
+        if input_features_padded is None:
+            return None
+
         return Gemma3nAudioInputs(
             input_features=input_features,
             input_features_mask=input_features_mask,
+            input_features_padded=input_features_padded,
         )
 
     def _parse_and_validate_multimodal_inputs(self, **kwargs: object) -> dict:
@@ -564,7 +577,8 @@ class Gemma3nForConditionalGeneration(nn.Module, SupportsMultiModal,
         audio_input: Gemma3nAudioInputs,
     ) -> list[torch.Tensor]:
         assert self.audio_tower is not None
-        input_features = audio_input["input_features"].squeeze(1)
+        # Run on padded features to enable batching
+        input_features = audio_input["input_features_padded"].squeeze(1)
         input_features_mask = audio_input["input_features_mask"].squeeze(1)
         audio_outputs, audio_mask = self.audio_tower(input_features,
                                                      ~input_features_mask)