diff --git a/devtools/conda-envs/test_cuda_env.yaml b/devtools/conda-envs/test_cuda_env.yaml
new file mode 100644
index 00000000..3b854646
--- /dev/null
+++ b/devtools/conda-envs/test_cuda_env.yaml
@@ -0,0 +1,50 @@
+# environment for testing cuda
+name: openff-nagl-test-cuda
+channels:
+  - openeye
+  - dglteam/label/cu117
+  - pytorch
+  - nvidia
+  - conda-forge
+  - defaults
+dependencies:
+    # Base depends
+  - python
+  - pip
+
+  # UI
+  - click
+  - click-option-group
+  - tqdm
+  - rich
+
+  # chemistry
+  - openff-recharge
+  - openff-toolkit-base >=0.11.1
+  - openff-units
+  - pydantic <3
+  - rdkit
+  - openeye-toolkits
+
+  # database
+  - pyarrow
+
+  # gcn
+  - cudatoolkit
+  - dgl ==1.1.2
+  - pytorch >=2.0
+  - pytorch-lightning
+  - pytorch-cuda ==11.7
+
+  # parallelism
+  - dask-jobqueue
+
+  # Testing
+  - pytest
+  - pytest-cov
+  - pytest-xdist
+  - codecov
+
+  # Pip-only installs
+  - pip:
+    - rich
diff --git a/openff/nagl/nn/_dataset.py b/openff/nagl/nn/_dataset.py
index deeae0fa..b14d9836 100644
--- a/openff/nagl/nn/_dataset.py
+++ b/openff/nagl/nn/_dataset.py
@@ -623,9 +623,7 @@ def __init__(
         super().__init__(
             dataset=dataset,
             batch_size=batch_size,
-            num_workers=1,  # otherwise shared memory issues
             collate_fn=self._collate,
-            # pin_memory=True,
             **kwargs,
         )
 
diff --git a/openff/nagl/tests/training/test_training.py b/openff/nagl/tests/training/test_training.py
index c92ece21..603561ee 100644
--- a/openff/nagl/tests/training/test_training.py
+++ b/openff/nagl/tests/training/test_training.py
@@ -5,6 +5,7 @@
 
 import torch
 import numpy as np
+import pytorch_lightning as pl
 
 from openff.nagl.training.training import DGLMoleculeDataModule, DataHash, TrainingGNNModel
 from openff.nagl.nn._models import GNNModel
@@ -334,3 +335,45 @@ def test_weighted_mixed_training_step(self, mock_training_model, dgl_methane):
         loss = loss["loss"]
         assert torch.isclose(loss, torch.tensor([expected_loss], dtype=torch.float32))
         assert torch.isclose(loss, torch.tensor([123.534743]))
+
+
+@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
+def test_train_model_no_error(example_training_config, tmpdir):
+
+    data_module = DGLMoleculeDataModule(example_training_config)
+
+    with tmpdir.as_cwd():
+        shutil.copy(
+            EXAMPLE_FEATURIZED_LAZY_DATA.resolve(),
+            "."
+        )
+        shutil.copy(
+            EXAMPLE_FEATURIZED_LAZY_DATA_SHORT.resolve(),
+            "."
+        )
+        shutil.copytree(
+            EXAMPLE_UNFEATURIZED_PARQUET_DATASET.resolve(),
+            EXAMPLE_UNFEATURIZED_PARQUET_DATASET.stem
+        )
+        shutil.copytree(
+            EXAMPLE_UNFEATURIZED_PARQUET_DATASET_SHORT.resolve(),
+            EXAMPLE_UNFEATURIZED_PARQUET_DATASET_SHORT.stem
+        )
+        for stage in ["train", "val", "test"]:
+            config = data_module._dataset_configs[stage]
+            config = config.copy(
+                update={
+                    "use_cached_data": True,
+                    "cache_directory": ".",
+                }
+            )
+            data_module._dataset_configs[stage] = config
+
+        data_module.prepare_data()
+        assert isinstance(data_module.train_dataloader(), DGLMoleculeDataLoader)
+
+        model = TrainingGNNModel(example_training_config)
+        trainer = pl.Trainer(
+            accelerator="gpu", devices=1, max_epochs=2,
+        )
+        trainer.fit(model, datamodule=data_module)