diff --git a/tests/tests_pytorch/loops/test_trainer_iterable_dataset_double_iter.py b/tests/tests_pytorch/loops/test_trainer_iterable_dataset_double_iter.py
new file mode 100644
index 0000000000000..e8eacd133afb3
--- /dev/null
+++ b/tests/tests_pytorch/loops/test_trainer_iterable_dataset_double_iter.py
@@ -0,0 +1,58 @@
+import multiprocessing as mp
+import os
+from collections.abc import Iterator
+from queue import Queue
+
+import numpy as np
+from torch.utils.data import DataLoader, IterableDataset
+
+from lightning import Trainer
+from lightning.pytorch.demos.boring_classes import BoringModel
+
+
+class QueueDataset(IterableDataset):
+    def __init__(self, queue: Queue) -> None:
+        super().__init__()
+        self.queue = queue
+
+    def __iter__(self) -> Iterator:
+        for _ in range(5):
+            tensor, _ = self.queue.get(timeout=5)
+            yield tensor
+
+
+def create_queue():
+    q = mp.Queue()
+    arr = np.random.random([1, 32]).astype(np.float32)
+    for ind in range(10):
+        q.put((arr, ind))
+    return q
+
+
+def train_model(queue, maxEpochs, ckptPath):
+    dataloader = DataLoader(QueueDataset(queue), num_workers=1, batch_size=None, persistent_workers=True)
+    trainer = Trainer(max_epochs=maxEpochs, enable_progress_bar=False, devices=1)
+    if os.path.exists(ckptPath):
+        trainer.fit(BoringModel(), dataloader, ckpt_path=ckptPath)
+    else:
+        trainer.fit(BoringModel(), dataloader)
+        trainer.save_checkpoint(ckptPath)
+    return trainer
+
+
+def test_training():
+    """Test that reproduces issue in calling iter twice on a queue-based IterableDataset leads to Queue Empty errors
+    when resuming from a checkpoint."""
+    queue = create_queue()
+
+    ckpt_path = "model.ckpt"
+    trainer = train_model(queue, 1, ckpt_path)
+    assert trainer is not None
+
+    assert os.path.exists(ckpt_path), f"Checkpoint file '{ckpt_path}' wasn't created"
+
+    ckpt_size = os.path.getsize(ckpt_path)
+    assert ckpt_size > 0, f"Checkpoint file is empty (size: {ckpt_size} bytes)"
+
+    trainer = train_model(queue, 2, ckpt_path)
+    assert trainer is not None