diff --git a/MaxText/train.py b/MaxText/train.py
index 943822550..bd80c11ac 100644
--- a/MaxText/train.py
+++ b/MaxText/train.py
@@ -25,8 +25,9 @@
 import sys
 from etils import epath
 import functools
+import time
 
-from typing import Sequence
+from typing import Sequence, Optional
 from absl import app
 from flax import linen as nn
 from flax.linen import partitioning as nn_partitioning
@@ -166,8 +167,30 @@ def clear_buffered_metrics():
   _buffered_step = None
   _buffered_metrics = None
 
-def save_checkpoint(checkpoint_manager, step, state, dataset_type="c4", data_iterator=None):
-  """Wrapper for saving checkpoint"""
+def save_checkpoint(
+    checkpoint_manager,
+    step,
+    state,
+    dataset_type="c4",
+    data_iterator=None,
+    config: Optional[pyconfig.config] = None,
+) -> bool:
+  """Wrapper for saving checkpoint."""
+  if config and config.enable_checkpointing:
+    if (step % config.checkpoint_period == 0) or (
+        config.enable_emergency_checkpoint
+        and step % config.local_checkpoint_period == 0
+    ):
+      blocking_until_ready_start = time.time()
+      max_logging.log(f"Waiting for step {step} to finish before checkpoint...")
+      # We block here on the step finishing so that our checkpointing metrics
+      # measure only checkpointing time, not training time.
+      jax.block_until_ready(state)
+      max_logging.log(
+          f"Waited {time.time() - blocking_until_ready_start} seconds for step "
+          f"{step} to finish before starting checkpointing."
+      )
+
   # specify chunk_byte_size to force orbax to control maximum file size in checkpoint
   save_args = jax.tree.map(
       lambda _: orbax.checkpoint.SaveArgs(chunk_byte_size=_CHUNK_BYTE_SIZE), state
@@ -617,7 +640,7 @@ def train_loop(config, state=None):
     last_step_completion = new_time
 
     if checkpoint_manager is not None:
-      if save_checkpoint(checkpoint_manager, int(step), state, config.dataset_type, data_iterator):
+      if save_checkpoint(checkpoint_manager, int(step), state, config.dataset_type, data_iterator, config):
         max_logging.log(f"saved a checkpoint at step {step}")
 
       # Upon preemption, exit when and only when all ongoing saves are complete.