diff --git a/vllm/model_executor/models/ultravox.py b/vllm/model_executor/models/ultravox.py
index f59c233883ce2..ab9a74767335f 100644
--- a/vllm/model_executor/models/ultravox.py
+++ b/vllm/model_executor/models/ultravox.py
@@ -2,6 +2,7 @@
 
 # Adapted from https://github.com/fixie-ai/ultravox/blob/ecd58c4041030bae2ad15aa6bcf04ab43199ea02/ultravox/model/ultravox_model.py
 """PyTorch Ultravox model."""
+import math
 from functools import cached_property
 from typing import (Any, Iterable, List, Literal, Mapping, Optional, Set,
                     Tuple, TypedDict, Union)
@@ -46,14 +47,14 @@
 
 class UltravoxAudioFeatureInputs(TypedDict):
     type: Literal["audio_features"]
-    data: torch.Tensor
+    data: NestedTensors
     """Shape: `(batch_size, num_audios, 80, M)`"""
-    lens: torch.Tensor
+    lens: NestedTensors
     """
     Length of the audio frames. Used for attention mask in WhisperEncoder.
     Shape: `(batch_size)`
     """
-    token_len: torch.Tensor
+    token_len: NestedTensors
     """
     Length of the audio tokens. Used for flattening the audio features.
     Shape: `(batch_size)`
@@ -110,7 +111,11 @@ def get_mm_max_tokens_per_item(
         seq_len: int,
         mm_counts: Mapping[str, int],
     ) -> Mapping[str, int]:
-        return {}
+        feature_extractor = self.get_feature_extractor()
+        max_audio_tokens = math.ceil(feature_extractor.chunk_length *
+                                     _AUDIO_TOKENS_PER_SECOND)
+
+        return {"audio": max_audio_tokens}
 
 
 class UltravoxDummyInputsBuilder(BaseDummyInputsBuilder[UltravoxProcessingInfo]
@@ -422,6 +427,9 @@ class UltravoxModel(nn.Module, SupportsMultiModal, SupportsPP, SupportsLoRA):
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         super().__init__()
         config = vllm_config.model_config.hf_config
+        # Due to the batching of audio chunks, the preprocessor cache cannot
+        # do the right thing so disable it.
+        vllm_config.model_config.disable_mm_preprocessor_cache = True
         multimodal_config = vllm_config.model_config.multimodal_config
         self.config = config
         self.multi_modal_config = multimodal_config
@@ -516,10 +524,24 @@ def _process_audio_input(
         if audio_input["type"] == "audio_embeds":
             return audio_input["data"]
 
-        # remove unneeded extra dimension added to all elements of mm_kwargs
-        audio_features = flatten_bn(audio_input["data"])
-        audio_lens = flatten_bn(audio_input["lens"])
-        audio_token_len = flatten_bn(audio_input["token_len"])
+        audio_features = audio_input["data"]
+        if isinstance(audio_features, list):
+            max_len = max(x.shape[-1] for x in audio_features)
+            # Pad and concatenate:
+            # [[B1, 80, M1], [B2, 80, M2]] -> [B1+B2, 80, max(M1, M2)]
+            audio_features = torch.cat(
+                [F.pad(x, (0, max_len - x.shape[-1])) for x in audio_features])
+        else:
+            # Flatten [B, N, 80, M] -> [B * N, 80, M]
+            audio_features = flatten_bn(audio_features)
+
+        if isinstance(audio_input['lens'], list):
+            # [B1, B2] -> [B1+B2]
+            audio_lens = torch.cat(audio_input['lens'])
+            audio_token_len = torch.cat(audio_input['token_len'])
+        else:
+            audio_lens = flatten_bn(audio_input['lens'])
+            audio_token_len = flatten_bn(audio_input['token_len'])
 
         embeddings = self._audio_features_to_embeddings(
             audio_features, audio_lens)