diff --git a/py_boost/gpu/base.py b/py_boost/gpu/base.py
index 6c3cf7c..d946a0f 100644
--- a/py_boost/gpu/base.py
+++ b/py_boost/gpu/base.py
@@ -4,7 +4,7 @@
 try:
     import cupy as cp
 except Exception:
-    pass
+    cp = None
 import numpy as np
 
 from .serialization import dump, load
@@ -37,30 +37,29 @@ def __init__(self):
         self.seed = 42
 
     def to_device(self):
-        """Move trained ensemble data to current GPU device
-
-        Returns:
-
-        """
+        """Move trained ensemble data to GPU, undoing a partial failed transfer."""
         if not self._on_device:
-            for tree in self.models:
-                tree.to_device()
-            self.base_score = cp.asarray(self.base_score)
+            try:
+                for tree in self.models:
+                    tree.to_device()
+                self.base_score = cp.asarray(self.base_score)
+            except Exception:
+                self.to_cpu()
+                raise
 
             self._on_device = True
 
     def to_cpu(self):
-        """Move trained ensemble data to CPU memory
-
-        Returns:
-
-        """
-        if self._on_device:
-            for tree in self.models:
+        """Move all GPU arrays to CPU, including an incomplete device transfer."""
+        # A failed transfer can leave GPU arrays while _on_device is still False.
+        # With no CuPy installed, CPU-only export/serialization needs no cleanup.
+        if cp is not None:
+            for tree in self.models or []:
                 tree.to_cpu()
-            self.base_score = self.base_score.get()
+            if isinstance(self.base_score, cp.ndarray):
+                self.base_score = self.base_score.get()
 
-            self._on_device = False
+        self._on_device = False
 
     def __getstate__(self):
         """Get state dict on CPU for picking
diff --git a/tests/test_device_transfer.py b/tests/test_device_transfer.py
new file mode 100644
index 0000000..9081a0e
--- /dev/null
+++ b/tests/test_device_transfer.py
@@ -0,0 +1,215 @@
+"""GPU transfer recovery must not retain live arrays after a failed upload."""
+
+import pickle
+import subprocess
+import sys
+
+import numpy as np
+import pytest
+
+
+@pytest.fixture(scope='module')
+def cp():
+    cupy = pytest.importorskip('cupy')
+    try:
+        count = cupy.cuda.runtime.getDeviceCount()
+    except cupy.cuda.runtime.CUDARuntimeError as exc:
+        pytest.skip(f'CUDA device unavailable: {exc}')
+    if not count:
+        pytest.skip('A CUDA device is required')
+    return cupy
+
+
+@pytest.fixture(scope='module', params=[False, True], ids=['compact', 'debug'])
+def fitted(request, cp):
+    from py_boost import GradientBoosting
+
+    rng = np.random.default_rng(12)
+    X = rng.normal(size=(128, 4)).astype(np.float32)
+    X[::11, 0] = np.nan
+    clean = np.nan_to_num(X)
+    y = np.column_stack([clean[:, 0] + .5 * clean[:, 1], clean[:, 2] - clean[:, 3]])
+    model = GradientBoosting('mse', ntrees=3, max_depth=2, max_bin=16,
+                             min_data_in_leaf=2, es=0, verbose=0, debug=request.param).fit(X, y)
+    expected = model.predict(X, batch_size=32)
+    model.to_cpu()
+    return X, expected, pickle.dumps(model)
+
+
+@pytest.fixture
+def model_data(fitted, cp):
+    X, expected, serialized = fitted
+    model = pickle.loads(serialized)
+    yield model, X, expected
+    # Also clean up when these regressions are run against the broken version.
+    for tree in model.models:
+        tree.to_cpu()
+    if isinstance(model.base_score, cp.ndarray):
+        model.base_score = model.base_score.get()
+    cp.get_default_memory_pool().free_all_blocks()
+
+
+def gpu_array_counts(model, cp):
+    return [sum(isinstance(value, cp.ndarray) for value in vars(tree).values())
+            for tree in model.models]
+
+
+def assert_cpu(model, cp):
+    assert not model._on_device
+    assert isinstance(model.base_score, np.ndarray)
+    assert not any(gpu_array_counts(model, cp))
+
+
+@pytest.mark.parametrize('stage', ['first-tree', 'second-tree', 'base-score'])
+@pytest.mark.parametrize('nondefault_stream', [False, True], ids=['default-stream', 'nondefault-stream'])
+def test_upload_oom_rolls_back_and_allows_retry(model_data, cp, monkeypatch, stage, nondefault_stream):
+    model, X, expected = model_data
+    # Measure real pool allocation sizes instead of assuming allocator rounding.
+    probe = cp.cuda.MemoryPool()
+    sizes = []
+    with cp.cuda.using_allocator(probe.malloc):
+        for tree in model.models:
+            tree.to_device()
+            sizes.append(probe.used_bytes())
+    full_counts = gpu_array_counts(model, cp)
+    for tree in model.models:
+        tree.to_cpu()
+    probe.free_all_blocks()
+
+    pool = cp.cuda.MemoryPool()
+    # One small allocation fits, then the next attribute fails. For the other
+    # stages, allow one whole tree or all trees before the failing allocation.
+    limit = {'first-tree': 512, 'second-tree': sizes[0] + 512, 'base-score': sizes[-1]}[stage]
+    pool.set_limit(size=limit)
+    asarray = cp.asarray
+    observed = {}
+
+    def upload(array, *args, **kwargs):
+        try:
+            return asarray(array, *args, **kwargs)
+        except cp.cuda.memory.OutOfMemoryError as exc:
+            observed['counts'] = gpu_array_counts(model, cp)
+            observed['base_score'] = array is model.base_score
+            observed['error'] = exc
+            raise
+
+    stream = cp.cuda.Stream(non_blocking=True) if nondefault_stream else cp.cuda.Stream.null
+    with monkeypatch.context() as patch:
+        patch.setattr(cp, 'asarray', upload)
+        with stream, cp.cuda.using_allocator(pool.malloc):
+            with pytest.raises(cp.cuda.memory.OutOfMemoryError) as caught:
+                model.to_device()
+        stream.synchronize()
+
+    assert caught.value is observed['error']
+    if stage == 'first-tree':
+        assert 0 < observed['counts'][0] < full_counts[0]
+        assert observed['counts'][1:] == [0, 0]
+    elif stage == 'second-tree':
+        assert observed['counts'][0] == full_counts[0]
+        assert 0 < observed['counts'][1] < full_counts[1]
+        assert observed['counts'][2] == 0
+    else:
+        assert observed['counts'] == full_counts
+        assert observed['base_score']
+
+    # Check automatic rollback before any explicit to_cpu or garbage collection.
+    assert_cpu(model, cp)
+    assert pool.used_bytes() == 0
+    pool.free_all_blocks()
+    assert pool.total_bytes() == 0
+    model.to_cpu()
+    restored = pickle.loads(pickle.dumps(model))
+    assert_cpu(restored, cp)
+    try:
+        np.testing.assert_array_equal(model.predict(X, batch_size=32), expected)
+        np.testing.assert_array_equal(restored.predict(X, batch_size=32), expected)
+    finally:
+        model.to_cpu()
+        restored.to_cpu()
+
+
+@pytest.mark.parametrize('base_on_gpu', [False, True])
+@pytest.mark.parametrize('flag', [False, True])
+def test_to_cpu_recovers_mixed_state(model_data, cp, base_on_gpu, flag):
+    model, X, expected = model_data
+    pool = cp.cuda.MemoryPool()
+    with cp.cuda.using_allocator(pool.malloc):
+        model.models[0].values = cp.asarray(model.models[0].values)
+        if base_on_gpu:
+            model.base_score = cp.asarray(model.base_score)
+    model._on_device = flag
+    assert pool.used_bytes() > 0
+    model.to_cpu()
+    model.to_cpu()
+    assert_cpu(model, cp)
+    assert pool.used_bytes() == 0
+    pool.free_all_blocks()
+    np.testing.assert_array_equal(model.predict(X, batch_size=32), expected)
+
+
+def test_pickle_recovers_partial_state(model_data, cp):
+    model, X, expected = model_data
+    pool = cp.cuda.MemoryPool()
+    with cp.cuda.using_allocator(pool.malloc):
+        model.models[0].values = cp.asarray(model.models[0].values)
+        model.base_score = cp.asarray(model.base_score)
+    assert not model._on_device
+    restored = pickle.loads(pickle.dumps(model))
+    assert_cpu(model, cp)
+    assert_cpu(restored, cp)
+    assert pool.used_bytes() == 0
+    pool.free_all_blocks()
+    try:
+        np.testing.assert_array_equal(restored.predict(X, batch_size=32), expected)
+    finally:
+        restored.to_cpu()
+
+
+def test_upload_preserves_other_exceptions(model_data, cp, monkeypatch):
+    model, _, _ = model_data
+    failure = RuntimeError('transfer failed')
+    asarray = cp.asarray
+
+    def upload(array, *args, **kwargs):
+        if array is model.base_score:
+            raise failure
+        return asarray(array, *args, **kwargs)
+
+    with monkeypatch.context() as patch:
+        patch.setattr(cp, 'asarray', upload)
+        with pytest.raises(RuntimeError) as caught:
+            model.to_device()
+    assert caught.value is failure
+    assert_cpu(model, cp)
+
+
+def test_cpu_state_remains_serializable_without_cupy():
+    # The CPU export/inference workflow can load an ensemble without CuPy.
+    # Deny imports in a fresh process so an installed CuPy cannot hide a regression.
+    script = r'''
+import importlib.abc
+import pickle
+import sys
+class NoCuPy(importlib.abc.MetaPathFinder):
+    def find_spec(self, fullname, path=None, target=None):
+        if fullname == 'cupy' or fullname.startswith('cupy.'):
+            raise ModuleNotFoundError('CuPy is unavailable')
+sys.meta_path.insert(0, NoCuPy())
+import numpy as np
+from py_boost.gpu.base import Ensemble
+from py_boost.gpu.tree import Tree
+for trees in (None, [], [Tree(max_nodes=1, nout=1, ngroups=1)]):
+    model = Ensemble()
+    model.models = trees
+    if trees is not None:
+        model.base_score = np.array([.25], dtype=np.float32)
+    model.to_cpu()
+    restored = pickle.loads(pickle.dumps(model))
+    restored.to_cpu()
+    assert not restored._on_device
+    np.testing.assert_equal(restored.base_score, model.base_score)
+assert 'cupy' not in sys.modules
+'''
+    result = subprocess.run([sys.executable, '-c', script], capture_output=True, text=True, timeout=60)
+    assert result.returncode == 0, result.stdout + result.stderr