diff --git a/src/instructlab/training/main_ds.py b/src/instructlab/training/main_ds.py
index a0c03e5a..41ee21f5 100644
--- a/src/instructlab/training/main_ds.py
+++ b/src/instructlab/training/main_ds.py
@@ -346,7 +346,12 @@ def train(args, model, tokenizer, train_loader, grad_accum, metric_logger):
 
     for epoch in range(args.num_epochs):
         torch.distributed.barrier()
-        train_loader.batch_sampler.set_epoch(epoch)
+        if args.sampler in ("multipack"):
+            train_loader.batch_sampler.set_epoch(epoch)
+        elif args.sampler in ("distributed"):
+            train_loader.sampler.set_epoch(epoch)
+        else:
+            raise NotADirectoryError
 
         if local_rank == 0:
             inner_pb = tqdm(range(len(train_loader)), desc=f"Epoch {epoch}")
@@ -510,6 +515,8 @@ def main(args):
         is_granite=args.is_granite,
         max_batch_len=args.max_batch_len,
         packing_max_batch_len=packing_max_batch_len,
+        samples_per_gpu=args.samples_per_gpu,
+        sampler=args.sampler,
         seed=args.seed,
     )
 
@@ -674,6 +681,13 @@ def run_training(torch_args: TorchrunArgs, train_args: TrainingArgs):
             "constant_with_warmup",
         ],
     )
+    parser.add_argument(
+        "--sampler",
+        type=str,
+        default="multipack",
+        help="The batch sampler type to use.",
+        choices=["multipack", "distributed"],
+    )
     parser.add_argument("--num_warmup_steps", type=int, default=1000)
     # parser.add_argument("--gradient_accumulation_steps", type=int, default=1)
     parser.add_argument("--save_samples", type=int)
diff --git a/src/instructlab/training/token_dataset.py b/src/instructlab/training/token_dataset.py
index e36b7395..d4acf22d 100644
--- a/src/instructlab/training/token_dataset.py
+++ b/src/instructlab/training/token_dataset.py
@@ -85,6 +85,8 @@ def setup_dataloader(
     is_granite=False,
     max_batch_len=60000,
     packing_max_batch_len=60000,
+    samples_per_gpu=None,
+    sampler="multipack",
     seed=47,
 ) -> DataLoader:
     collate_fn = make_collate_fn(
@@ -94,17 +96,33 @@ def setup_dataloader(
     world_size = int(os.environ["WORLD_SIZE"])
 
     lengths = dataset.get_lengths()
-    sampler = MultipackDistributedBatchSampler(
-        batch_max_length=packing_max_batch_len,
-        lengths=lengths,
-        num_replicas=world_size,
-        rank=rank,
-        seed=seed,
-        padding=not is_granite,
-    )
+    if sampler == "multipack":
+        sampler = MultipackDistributedBatchSampler(
+            batch_max_length=packing_max_batch_len,
+            lengths=lengths,
+            num_replicas=world_size,
+            rank=rank,
+            seed=seed,
+            padding=not is_granite,
+        )
+        sampler = {"batch_sampler": sampler}
+    elif sampler == "distributed":
+        # Third Party
+        from torch.utils.data import DistributedSampler
+
+        sampler = (
+            DistributedSampler(dataset) if torch.distributed.is_initialized() else None
+        )
+        sampler = {
+            "sampler": sampler,
+            "batch_size": samples_per_gpu,
+        }
+    else:
+        raise NotImplementedError
+
     dataloader = DataLoader(
         dataset,
-        batch_sampler=sampler,
+        **sampler,
         num_workers=num_workers,
         collate_fn=collate_fn,
     )