diff --git a/quanto/tensor/core.py b/quanto/tensor/core.py
index 5d338f36..da99afc6 100644
--- a/quanto/tensor/core.py
+++ b/quanto/tensor/core.py
@@ -300,6 +300,11 @@ def __torch_dispatch__(cls, op, types, args, kwargs=None):
     def numpy(self):
         return self.dequantize().cpu().numpy()
 
+    def to(self, *args, **kwargs):
+        self._data = self._data.to(*args, **kwargs)
+        self._scale = self._scale.to(*args, **kwargs)
+        return self
+
 
 class AffineQuantizer(Function):
     """A standard affine quantizer."""
@@ -407,3 +412,7 @@ def __torch_dispatch__(cls, op, types, args, kwargs=None):
             return QBitsTensor(t._qtype, data, scale, zeropoint)
         args, kwargs = pytree.tree_map_only(QBitsTensor, lambda x: x.qtensor(), (args, kwargs or {}))
         return op(*args, **kwargs)
+
+    def to(self, *args, **kwargs):
+        self._zeropoint = self._zeropoint.to(*args, **kwargs)
+        return super().to(*args, **kwargs)
diff --git a/test/nn/test_qlinear.py b/test/nn/test_qlinear.py
index 80b0c96a..99ddd0ef 100644
--- a/test/nn/test_qlinear.py
+++ b/test/nn/test_qlinear.py
@@ -2,7 +2,7 @@
 import torch
 from helpers import assert_similar, random_qtensor
 
-from quanto import Calibration, QTensor, qfloat8_e4m3fn, qfloat8_e5m2, qint4, qint8
+from quanto import Calibration, QBitsTensor, QTensor, qfloat8_e4m3fn, qfloat8_e5m2, qint4, qint8
 from quanto.nn import QLinear
 
 
@@ -27,6 +27,29 @@ def _test_quantize_linear(batch_size, tokens, embeddings, use_bias, weights, act
     assert_similar(out, qout, atol=atol)
 
 
+@pytest.mark.parametrize("use_bias", [True, False], ids=["bias", "no-bias"])
+@pytest.mark.parametrize("weights", [qint4, qint8], ids=["w-int4", "w-int8"])
+@pytest.mark.parametrize(
+    "activations",
+    [None, qfloat8_e5m2, qfloat8_e4m3fn],
+    ids=["None", "a-float8-e5m2", "a-float8-e4m3"],
+)
+def test_move_qlinear(use_bias, weights, activations, device):
+    linear = torch.nn.Linear(32, 32, bias=use_bias)
+    qlinear = QLinear.from_module(linear, weights=weights, activations=activations)
+    # QAT optional for weight only quantization
+    qinputs = random_qtensor((1, 32, 32))
+    with torch.no_grad(), Calibration():
+        qlinear(qinputs)
+    qlinear.freeze()
+    qlinear.to(device)
+    if isinstance(qlinear.weight, QTensor):
+        assert qlinear.weight._data.device.type == device.type
+        assert qlinear.weight._scale.device.type == device.type
+    if isinstance(qlinear.weight, QBitsTensor):
+        assert qlinear.weight._zeropoint.device.type == device.type
+
+
 @pytest.mark.parametrize("batch_size", [1, 10])
 @pytest.mark.parametrize("tokens, embeddings", [(32, 32), (10, 32)])
 @pytest.mark.parametrize("use_bias", [True, False], ids=["bias", "no-bias"])
diff --git a/test/tensor/test_qtensor.py b/test/tensor/test_qtensor.py
index 62b791e0..84e5fe2f 100644
--- a/test/tensor/test_qtensor.py
+++ b/test/tensor/test_qtensor.py
@@ -8,6 +8,14 @@
 from quanto import QTensor, absmax_scale, qfloat8_e4m3fn, qfloat8_e5m2, qint8, qint16, qint32
 
 
+def test_qtensor_move(device):
+    input_shape = (2, 4, 8)
+    qa = random_qtensor(input_shape, dtype=torch.float32)
+    qa = qa.to(device)
+    assert qa._data.device.type == device.type
+    assert qa._scale.device.type == device.type
+
+
 @pytest.mark.parametrize("input_shape", [(10,), (1, 10), (10, 32, 32)])
 @pytest.mark.parametrize("dtype", [torch.float16, torch.float32], ids=["fp16", "fp32"])
 @pytest.mark.parametrize("qtype", [qint8], ids=["qint8"])