diff --git a/pytorch_lightning/core/saving.py b/pytorch_lightning/core/saving.py
index c07dd944ee..7124007cd3 100644
--- a/pytorch_lightning/core/saving.py
+++ b/pytorch_lightning/core/saving.py
@@ -17,7 +17,7 @@ import csv
 import inspect
 import os
 from argparse import Namespace
-from typing import Union, Dict, Any, Optional, Callable, MutableMapping
+from typing import Union, Dict, Any, Optional, Callable, MutableMapping, IO
 from warnings import warn
 
 import fsspec
@@ -52,7 +52,7 @@ class ModelIO(object):
     @classmethod
     def load_from_checkpoint(
         cls,
-        checkpoint_path: str,
+        checkpoint_path: Union[str, IO],
         map_location: Optional[Union[Dict[str, str], str, torch.device, int, Callable]] = None,
         hparams_file: Optional[str] = None,
         strict: bool = True,
@@ -65,7 +65,7 @@ class ModelIO(object):
         Any arguments specified through \*args and \*\*kwargs will override args stored in `hparams`.
 
         Args:
-            checkpoint_path: Path to checkpoint. This can also be a URL.
+            checkpoint_path: Path to checkpoint. This can also be a URL, or file-like object
             map_location:
                 If your checkpoint saved a GPU model and you now load on CPUs
                 or a different number of GPUs, use this to map to the new setup.
diff --git a/pytorch_lightning/utilities/cloud_io.py b/pytorch_lightning/utilities/cloud_io.py
index 863cd3617d..33845384fa 100644
--- a/pytorch_lightning/utilities/cloud_io.py
+++ b/pytorch_lightning/utilities/cloud_io.py
@@ -14,14 +14,17 @@
 
 import io
 from distutils.version import LooseVersion
-from typing import Union
+from typing import Union, IO
 from pathlib import Path
 from urllib.parse import urlparse
 import torch
 import fsspec
 
 
-def load(path_or_url: str, map_location=None):
+def load(path_or_url: Union[str, IO, Path], map_location=None):
+    if not isinstance(path_or_url, (str, Path)):
+        # any sort of BytesIO or similiar
+        return torch.load(path_or_url, map_location=map_location)
     if path_or_url.startswith("http"):
         return torch.hub.load_state_dict_from_url(path_or_url, map_location=map_location)
     fs = get_filesystem(path_or_url)