diff --git a/monai/data/dataloader.py b/monai/data/dataloader.py
index 53670046d8..ed9599eab8 100644
--- a/monai/data/dataloader.py
+++ b/monai/data/dataloader.py
@@ -83,7 +83,7 @@ def __init__(self, dataset: Dataset, num_workers: int = 0, **kwargs) -> None:
             # disable unnecessary multiprocessing caching
             from monai.data.dataset import CacheDataset  # avoid circular import
 
-            if isinstance(dataset, CacheDataset) and dataset.runtime_cache:
+            if isinstance(dataset, CacheDataset):
                 dataset.disable_share_memory_cache()
 
             _g.manual_seed(init_seed)
diff --git a/monai/data/dataset.py b/monai/data/dataset.py
index 7a1b7f5eb9..638d4e9d27 100644
--- a/monai/data/dataset.py
+++ b/monai/data/dataset.py
@@ -763,7 +763,7 @@ def __init__(
                 will take the minimum of (cache_num, data_length x cache_rate, data_length).
             num_workers: the number of worker threads if computing cache in the initialization.
                 If num_workers is None then the number returned by os.cpu_count() is used.
-                If a value less than 1 is speficied, 1 will be used instead.
+                If a value less than 1 is specified, 1 will be used instead.
             progress: whether to display a progress bar.
             copy_cache: whether to `deepcopy` the cache content before applying the random transforms,
                 default to `True`. if the random transforms don't modify the cached content
@@ -778,15 +778,18 @@ def __init__(
             hash_func: if `hash_as_key`, a callable to compute hash from data items to be cached.
                 defaults to `monai.data.utils.pickle_hashing`.
             runtime_cache: whether to compute cache at the runtime, default to `False` to prepare
-                the cache content at initializaiton, if `True`, it will cache during the first epoch
+                the cache content at initialization, if `True`, it will cache during the first epoch
                 of model training, so it can start the first mini-batch earlier. please note that:
                 1. when using this option in multi-gpu distributed training,
                 `torch.cuda.set_device()` must be called before initializing this class.
-                2. to execute `runtime cache` on GPU memory, must co-work with
+                2. if caching data that is in GPU memory during multi-gpu distributed training, this option
+                should not be used, since the underlying shared cache only works for CPU shared memory.
+                3. to execute `runtime cache` on GPU memory, must co-work with
                 `monai.data.DataLoader`, and can't work with `monai.data.DistributedSampler`
                 as GPU Tensor usually can't be shared in the multiprocessing context.
                 (try ``cache_dataset.disable_share_memory_cache()`` in case of GPU caching issues.)
 
+
         """
         if not isinstance(transform, Compose):
             transform = Compose(transform)
@@ -827,7 +830,7 @@ def _compute_cache(indices=None):
                 cache = Manager().list([None for _ in range(self.cache_num)])
                 if self._is_dist:
                     obj_list = [cache]
-                    # broadcast the ProxyList to all the ranks, then share the same cache content at runtime
+                    # broadcast the ListProxy to all the ranks, then share the same cache content at runtime
                     dist.broadcast_object_list(obj_list, src=0)
                     cache = obj_list[0]
             else:
@@ -848,11 +851,18 @@ def _compute_cache(indices=None):
 
     def disable_share_memory_cache(self):
         """
-        If the cache content is multiprocessing share memory list, convert it to a regular ptython list.
-        Because multiprocessing ProxyList is not supported for the GPU caching, may need to explicitly diasble it.
+        If the cache content is a multiprocessing shared memory ListProxy, convert it to a regular python list.
+        Because multiprocessing ListProxy is not supported for the GPU caching,  explicitly disable it.
 
         """
-        self._cache = list(self._cache)
+        if self.runtime_cache:
+            if not self._is_dist:
+                self._cache = list(self._cache)
+            else:
+                warnings.warn(
+                    "Unable to disable shared cache in DDP, when runtime_cache==True."
+                    "Please use runtime_cache=False option to explicitly not use the shared cache."
+                )
 
     def _fill_cache(self, indices=None) -> List:
         """