diff --git a/backend/backends/hume_backend.py b/backend/backends/hume_backend.py
index 85ddb4cf..f6df707d 100644
--- a/backend/backends/hume_backend.py
+++ b/backend/backends/hume_backend.py
@@ -247,9 +247,13 @@ class HumeTadaBackend:
                 audio = audio.T  # (samples, channels) -> (channels, samples)
             audio = audio.to(device)
 
-            # Encode with forced alignment
+            # Encode with forced alignment.
+            # Must run under inference_mode: encoder params still require
+            # grad by default, and an autograd graph across the DAC/Snake
+            # stack can balloon VRAM far past the model footprint (#890).
             text_arg = [reference_text] if reference_text else None
-            prompt = self.encoder(audio, text=text_arg, sample_rate=sr)
+            with torch.inference_mode():
+                prompt = self.encoder(audio, text=text_arg, sample_rate=sr)
 
             # Serialize EncoderOutput to a dict of CPU tensors for caching
             prompt_dict = {}
diff --git a/backend/tests/test_hume_encode_inference_mode.py b/backend/tests/test_hume_encode_inference_mode.py
new file mode 100644
index 00000000..9da1120d
--- /dev/null
+++ b/backend/tests/test_hume_encode_inference_mode.py
@@ -0,0 +1,68 @@
+"""Ensure TADA voice-prompt encoding disables autograd (#890)."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from unittest.mock import AsyncMock
+
+import numpy as np
+import pytest
+import soundfile as sf
+import torch
+
+from backend.backends.hume_backend import HumeTadaBackend
+
+
+@dataclass
+class _FakeEncoderOutput:
+    emb: torch.Tensor
+
+
+class _GradTrackingEncoder:
+    """Raises unless called under torch.inference_mode()."""
+
+    def __init__(self) -> None:
+        self.called_under_inference_mode = False
+
+    def __call__(self, audio, text=None, sample_rate=None):
+        self.called_under_inference_mode = torch.is_inference_mode_enabled()
+        if not self.called_under_inference_mode:
+            raise AssertionError("encoder forward must run under inference_mode")
+        # Touch a requires_grad tensor the way Snake1d alpha would.
+        alpha = torch.nn.Parameter(torch.ones(1, device=audio.device))
+        _ = audio.mean() * alpha
+        return _FakeEncoderOutput(emb=torch.zeros(1, 4, device=audio.device))
+
+
+@pytest.mark.asyncio
+async def test_create_voice_prompt_runs_encoder_under_inference_mode(tmp_path, monkeypatch):
+    wav = tmp_path / "ref.wav"
+    sf.write(str(wav), np.zeros(24000, dtype=np.float32), 24000)
+
+    backend = HumeTadaBackend()
+    backend.model = object()  # mark loaded
+    backend.model_size = "1B"
+    backend._device = "cpu"
+    encoder = _GradTrackingEncoder()
+    backend.encoder = encoder
+
+    monkeypatch.setattr(backend, "load_model", AsyncMock(return_value=None))
+    monkeypatch.setattr(
+        "backend.backends.hume_backend.get_cached_voice_prompt",
+        lambda key: None,
+    )
+    monkeypatch.setattr(
+        "backend.backends.hume_backend.cache_voice_prompt",
+        lambda key, value: None,
+    )
+
+    prompt, from_cache = await backend.create_voice_prompt(
+        str(wav),
+        reference_text="hello world",
+        use_cache=False,
+    )
+
+    assert from_cache is False
+    assert encoder.called_under_inference_mode is True
+    assert isinstance(prompt["emb"], torch.Tensor)
+    assert prompt["emb"].device.type == "cpu"