diff --git a/src/lightning/fabric/CHANGELOG.md b/src/lightning/fabric/CHANGELOG.md
index 8ffc4721a9f9f..c0f623dda4730 100644
--- a/src/lightning/fabric/CHANGELOG.md
+++ b/src/lightning/fabric/CHANGELOG.md
@@ -35,7 +35,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Fixed
 
--
+- Fixed an attribute error when loading a checkpoint into a quantized model using the `_lazy_load()` function ([#20121](https://github.com/Lightning-AI/lightning/pull/20121))
+
 
 -
 
diff --git a/src/lightning/fabric/utilities/load.py b/src/lightning/fabric/utilities/load.py
index 9862cc2bd981e..2fa5c6e96db21 100644
--- a/src/lightning/fabric/utilities/load.py
+++ b/src/lightning/fabric/utilities/load.py
@@ -160,7 +160,7 @@ def __getattr__(self, name: str) -> Any:
             return getattr(self.metatensor, name)
 
         # materializing these is needed for quantization (see lit-gpt)
-        if name in {"contiguous", "cuda", "half"}:
+        if name in {"contiguous", "cuda", "half", "data"}:
             return getattr(self._load_tensor(), name)
 
         raise AttributeError(f"'{type(self).__name__}' object has no attribute '{name}'")
diff --git a/tests/tests_fabric/plugins/precision/test_bitsandbytes.py b/tests/tests_fabric/plugins/precision/test_bitsandbytes.py
index d5311616828ee..b8b9020b201a7 100644
--- a/tests/tests_fabric/plugins/precision/test_bitsandbytes.py
+++ b/tests/tests_fabric/plugins/precision/test_bitsandbytes.py
@@ -22,6 +22,7 @@
 from lightning.fabric.connector import _Connector
 from lightning.fabric.plugins.precision.bitsandbytes import _BITSANDBYTES_AVAILABLE, BitsandbytesPrecision
 from lightning.fabric.utilities.init import _materialize_meta_tensors
+from lightning.fabric.utilities.load import _lazy_load
 
 from tests_fabric.helpers.runif import RunIf
 
@@ -264,3 +265,10 @@ def forward(self, x):
     assert model.linear.weight.shape == (128, 1)
     # Shapes match during forward (weight is being dequantized during forward)
     model(torch.randn(2, 16, device=fabric.device))
+
+    # Test with lazy load (LitGPT uses this)
+    # TODO: Replace `_lazy_load` with `torch.load(..., mmap=True)` in LitGPT
+    state_dict = _lazy_load(tmp_path / "checkpoint.pt")
+    model.load_state_dict(state_dict)
+    assert model.linear.weight.dtype == torch.uint8
+    assert model.linear.weight.shape == (128, 1)