diff --git a/torchtitan/config_manager.py b/torchtitan/config_manager.py
index 4a2d9d561..a96fdd1ca 100644
--- a/torchtitan/config_manager.py
+++ b/torchtitan/config_manager.py
@@ -359,6 +359,14 @@ def __init__(self):
                 If using looped schedules, this still specifies the number of physical ranks, not the number
                 of stages.  Stages per rank are inferred from split points degree, and schedule.""",
         )
+        self.parser.add_argument(
+            "--experimental.pipeline_parallel_batch_split_dim",
+            type=int,
+            default=0,
+            help="""
+                The dimension to split the batch on for pipeline parallelism. Defaults to 0 (batch dimension), but can
+                also be set to 1 (sequence dimension).""",
+        )
         self.parser.add_argument(
             "--experimental.pipeline_parallel_split_points",
             type=string_list,
diff --git a/torchtitan/distributed/pipeline.py b/torchtitan/distributed/pipeline.py
index e471a6700..6c242320f 100644
--- a/torchtitan/distributed/pipeline.py
+++ b/torchtitan/distributed/pipeline.py
@@ -6,6 +6,8 @@
 import os
 from typing import Callable
 
+from torch.distributed.pipelining.microbatch import TensorChunkSpec
+
 from torch.distributed.pipelining.schedules import (
     _PipelineSchedule,
     _PipelineScheduleRuntime,
@@ -119,17 +121,18 @@ def build_pipeline_schedule(
             f"of stages ({num_total_stages}) which may result in a bubble in the pipeline."
         )
 
-    # validate that the batch size is divisible by the number of microbatches otherwise we'll hang or error during training
-    if job_config.training.batch_size % n_microbatches != 0:
-        raise ValueError(
-            f"Batch size {job_config.training.batch_size} must be divisible by number of microbatches {n_microbatches}. "
-            "Update the config arguments for either batch_size or pipeline_parallel_microbatches."
-        )
+    # determine microbatch chunking specification
+    if job_config.experimental.pipeline_parallel_batch_split_dim != 0:
+        dim = job_config.experimental.pipeline_parallel_batch_split_dim
+        args_chunk_spec = (TensorChunkSpec(dim),)
+    else:
+        args_chunk_spec = None
 
     schedule = schedule_class(
         stages if looped_schedule else stages[0],
         n_microbatches=n_microbatches,
         loss_fn=loss_fn,
+        args_chunk_spec=args_chunk_spec,
     )
     logger.info(
         f"Using pipeline schedule {job_config.experimental.pipeline_parallel_schedule} "