diff --git a/test/nodes/test_batch.py b/test/nodes/test_batch.py
index ca518324e..e96713992 100644
--- a/test/nodes/test_batch.py
+++ b/test/nodes/test_batch.py
@@ -9,6 +9,7 @@
 import torch
 from parameterized import parameterized
 from torch.testing._internal.common_utils import TestCase
+from torchdata.nodes import IterableWrapper
 from torchdata.nodes.batch import Batcher, Unbatcher
 
 from .utils import MockSource, run_test_save_load_state
@@ -28,6 +29,11 @@ def test_batcher(self) -> None:
                 self.assertEqual(results[i][j]["test_tensor"], torch.tensor([i * batch_size + j]))
                 self.assertEqual(results[i][j]["test_str"], f"str_{i * batch_size + j}")
 
+    def test_batcher_batch_size_zero_raises(self):
+        source = IterableWrapper(range(10))
+        with self.assertRaises(ValueError):
+            Batcher(source, batch_size=0)
+
     def test_batcher_drop_last_false(self) -> None:
         batch_size = 6
         src = MockSource(num_samples=20)
diff --git a/torchdata/nodes/batch.py b/torchdata/nodes/batch.py
index 7e7ca47de..858021511 100644
--- a/torchdata/nodes/batch.py
+++ b/torchdata/nodes/batch.py
@@ -25,6 +25,8 @@ class Batcher(BaseNode[List[T]]):
 
     def __init__(self, source: BaseNode[T], batch_size: int, drop_last: bool = True):
         super().__init__()
+        if batch_size <= 0:
+            raise ValueError("batch_size must be a positive integer")
         self.source = source
         self.batch_size = batch_size
         self.drop_last = drop_last