diff --git a/docs/training/torch.mdx b/docs/training/torch.mdx
index 9a9dbe5..e9c571f 100644
--- a/docs/training/torch.mdx
+++ b/docs/training/torch.mdx
@@ -96,3 +96,80 @@ dataloader = torch.utils.data.DataLoader(
 for batch in dataloader:
     print(batch.schema)
 ```
+
+## Using multiple DataLoader workers
+
+PyTorch's `DataLoader` can fan out reads across worker processes by setting `num_workers > 0`. LanceDB tables and `Permutation` objects are picklable, so each worker reopens its own connection after the worker process starts.
+
+Because LanceDB is multi-threaded internally, use the `spawn` start method (not `fork`) when running with multiple workers. See [the performance guide](/performance) for more on safe multiprocessing patterns.
+
+```py Python icon=Python 
+from lancedb.permutation import Permutation
+
+permutation = Permutation.identity(table)
+dataloader = torch.utils.data.DataLoader(
+    permutation,
+    batch_size=1024,
+    shuffle=True,
+    num_workers=4,
+    multiprocessing_context="spawn",
+    persistent_workers=True,
+)
+```
+
+### Remote tables in DataLoader workers
+
+Tables opened from a remote LanceDB Enterprise connection (`db://...`) also work with multi-worker DataLoaders. The connection details needed to reopen the table — `db_url`, `api_key`, `region`, `host_override`, and the serializable parts of `client_config` — travel with the pickled table and are used to rebuild the connection in each worker.
+
+```py Python icon=Python 
+import lancedb
+from lancedb.permutation import Permutation
+
+db = lancedb.connect(
+    "db://my-database",
+    api_key="sk-...",
+    region="us-east-1",
+)
+table = db.open_table("my_table")
+
+permutation = Permutation.identity(table).select_columns(["id", "image"])
+dataloader = torch.utils.data.DataLoader(
+    permutation,
+    batch_size=512,
+    num_workers=4,
+    multiprocessing_context="spawn",
+)
+```
+
+<Note>
+This embeds the API key in the pickle sent to each worker. If you'd rather load credentials inside the worker — for example, from an environment variable or a secret manager — use the connection factory escape hatch described below. A factory is also required when your `client_config` uses a non-serializable `header_provider`.
+</Note>
+
+### Providing a custom connection factory
+
+`Permutation.with_connection_factory` lets you control how each worker reopens the base table. The factory takes the base table name and returns a LanceDB table. It must be picklable, which in practice means a top-level function, a `functools.partial` of one, or an instance of a picklable class with `__call__` — lambdas and closures over local variables will not work.
+
+```py Python icon=Python 
+import os
+import lancedb
+from lancedb.permutation import Permutation
+
+def open_table(name: str):
+    db = lancedb.connect(
+        "db://my-database",
+        api_key=os.environ["LANCEDB_API_KEY"],
+        region="us-east-1",
+    )
+    return db.open_table(name)
+
+permutation = (
+    Permutation.identity(table)
+    .with_connection_factory(open_table)
+)
+dataloader = torch.utils.data.DataLoader(
+    permutation,
+    batch_size=512,
+    num_workers=4,
+    multiprocessing_context="spawn",
+)
+```