diff --git a/tests/entrypoints/openai/test_realtime_validation.py b/tests/entrypoints/openai/test_realtime_validation.py
index af15b7099..8f12a3764 100644
--- a/tests/entrypoints/openai/test_realtime_validation.py
+++ b/tests/entrypoints/openai/test_realtime_validation.py
@@ -121,3 +121,75 @@ async def test_multi_chunk_streaming(
                 " it sleeps with quite a flow, and everywhere that Mary went,"
                 " the lamb was sure to go."
             )
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize("model_name", [MODEL_NAME])
+async def test_empty_commit_does_not_crash_engine(
+    model_name, mary_had_lamb_audio_chunks, rocm_aiter_fa_attention
+):
+    """Test that committing without audio does not crash the engine.
+
+    Regression test for https://github.com/vllm-project/vllm/issues/34532.
+    An empty commit (no prior input_audio_buffer.append) used to trigger
+    ``AssertionError: For realtime you must provide a multimodal_embedding
+    at every step`` which killed the entire engine process, disconnecting
+    every connected client.
+    """
+    server_args = ["--enforce-eager", "--max-model-len", "2048"]
+
+    if model_name.startswith("mistralai"):
+        server_args += MISTRAL_FORMAT_ARGS
+
+    add_attention_backend(server_args, rocm_aiter_fa_attention)
+
+    with RemoteOpenAIServer(model_name, server_args) as remote_server:
+        ws_url = _get_websocket_url(remote_server)
+
+        # --- First connection: empty commit (no audio appended) ----------
+        async with websockets.connect(ws_url) as ws:
+            event = await receive_event(ws, timeout=30.0)
+            assert event["type"] == "session.created"
+
+            await send_event(ws, {"type": "session.update", "model": model_name})
+
+            # Start generation without sending any audio
+            await send_event(ws, {"type": "input_audio_buffer.commit"})
+
+            # Immediately signal end-of-audio
+            await send_event(ws, {"type": "input_audio_buffer.commit", "final": True})
+
+            # We should get *some* response (error or empty transcription),
+            # but the engine must NOT crash.
+            event = await receive_event(ws, timeout=30.0)
+            assert event["type"] in (
+                "error",
+                "transcription.done",
+                "transcription.delta",
+            )
+
+        # --- Second connection: normal transcription ---------------------
+        # Verifies the engine is still alive after the empty commit above.
+        async with websockets.connect(ws_url) as ws:
+            event = await receive_event(ws, timeout=30.0)
+            assert event["type"] == "session.created"
+
+            await send_event(ws, {"type": "session.update", "model": model_name})
+
+            await send_event(ws, {"type": "input_audio_buffer.commit"})
+
+            for chunk in mary_had_lamb_audio_chunks:
+                await send_event(
+                    ws, {"type": "input_audio_buffer.append", "audio": chunk}
+                )
+
+            await send_event(ws, {"type": "input_audio_buffer.commit", "final": True})
+
+            done_received = False
+            while not done_received:
+                event = await receive_event(ws, timeout=60.0)
+                if event["type"] == "transcription.done":
+                    done_received = True
+                elif event["type"] == "error":
+                    pytest.fail(f"Engine error after empty commit: {event}")
+            assert done_received
diff --git a/vllm/model_executor/models/voxtral_realtime.py b/vllm/model_executor/models/voxtral_realtime.py
index 726f67096..cc556ac82 100644
--- a/vllm/model_executor/models/voxtral_realtime.py
+++ b/vllm/model_executor/models/voxtral_realtime.py
@@ -299,13 +299,29 @@ class VoxtralRealtimeGeneration(VoxtralForConditionalGeneration, SupportsRealtim
         # Multi-modal token ID may exceed vocab size
         handle_oov_mm_token: bool = True,
     ) -> torch.Tensor:
-        """Pass post-conv embeddings directly as input"""
-        # for realtime we simply flatten the multimodal embeddings
-        # to be in tensor format, we treat the input ids later
-        assert multimodal_embeddings is not None
-        assert len(multimodal_embeddings) > 0, (
-            "For realtime you must provide a multimodal_embedding at every step."
-        )
+        """Pass post-conv embeddings directly as input.
+
+        For realtime models, multimodal embeddings are required at every
+        decode step.  If they are missing (e.g. due to an empty audio
+        commit, encoder-cache eviction under GPU memory pressure, or a
+        client disconnect), return zero embeddings instead of crashing
+        the engine so that all other in-flight requests stay alive.
+        """
+        if multimodal_embeddings is None or len(multimodal_embeddings) == 0:
+            logger.warning(
+                "Realtime model received empty multimodal embeddings "
+                "for %d input tokens. Returning zero embeddings to "
+                "avoid engine crash.",
+                input_ids.shape[0],
+            )
+            pool_size = self.config.audio_config.block_pool_size
+            embed_dim = self.config.audio_config.d_model * pool_size
+            return torch.zeros(
+                input_ids.shape[0],
+                embed_dim,
+                dtype=self.whisper_encoder.dtype,
+                device=input_ids.device,
+            )
         mm_embeds_flat = _flatten_embeddings(multimodal_embeddings)
         return mm_embeds_flat
 
@@ -367,9 +383,12 @@ class VoxtralRealtimeGeneration(VoxtralForConditionalGeneration, SupportsRealtim
         """Transform audio waveforms -> initial whisper post-conv embeddings"""
         audio_inputs = self._parse_and_validate_audio_arrays(**kwargs)
 
-        assert audio_inputs is not None, (
-            "For realtime you must provide an audio input at every step."
-        )
+        if audio_inputs is None:
+            logger.warning(
+                "Realtime model received no audio inputs in "
+                "embed_multimodal. Returning empty embeddings."
+            )
+            return []
 
         def _truncate_left(
             sample: torch.Tensor, mult_of: int, pos: int