diff --git a/nanochat/dataloader.py b/nanochat/dataloader.py
index 3d479a1..6c864d3 100644
--- a/nanochat/dataloader.py
+++ b/nanochat/dataloader.py
@@ -38,7 +38,8 @@ def tokenizing_distributed_data_loader(B, T, split, tokenizer_threads=4, tokeniz
             batch_index += 1
         # Move tokens from the deque into the scratch buffer
         tokens = [token_buffer.popleft() for _ in range(needed_tokens)]
-        scratch = torch.tensor(tokens, dtype=torch.int64, pin_memory=True)
+        # CUDA supports memory pinning for faster transfers between CPU and GPU:
+        scratch = torch.tensor(tokens, dtype=torch.int64, pin_memory=(device == "cuda"))
         # Create the inputs/targets as 1D tensors
         inputs_cpu = scratch[:-1].to(dtype=torch.int32)
         targets_cpu = scratch[1:]
diff --git a/scripts/mid_train.py b/scripts/mid_train.py
index c731d57..2835ebf 100644
--- a/scripts/mid_train.py
+++ b/scripts/mid_train.py
@@ -119,7 +119,8 @@ def mid_data_generator(split):
     assert dataset_size > 0
     needed_tokens = device_batch_size * max_seq_len + 1 # to form one training batch of inputs,targets
     token_buffer = deque()
-    scratch = torch.empty(needed_tokens, dtype=torch.int64, pin_memory=True)
+    # CUDA supports memory pinning for faster transfers between CPU and GPU:
+    scratch = torch.empty(needed_tokens, dtype=torch.int64, pin_memory=(device_type == "cuda"))
     cursor = ddp_rank # increments by ddp_world_size each time, so each rank processes unique documents
     it = 0 # iteration counter
     while True: