diff --git a/torch/distributed/_tensor/_utils.py b/torch/distributed/_tensor/_utils.py
index eaabdf02cc8e6..592cb6bd6e25e 100644
--- a/torch/distributed/_tensor/_utils.py
+++ b/torch/distributed/_tensor/_utils.py
@@ -43,42 +43,6 @@ def compute_local_shape(
         return tuple(local_shape)
 
 
-# TODO: audit existing code base to see if we can safely remove this API.
-def compute_local_offset(
-    global_shape: ShapeType, mesh: DeviceMesh, placements: Sequence[Placement]
-) -> Tuple[int, ...]:
-    """
-    Compute the offsets of a local shard of the given DTensor on its current
-    global rank. This is mostly used by distributed checkpointing to know the
-    exact offsets of the local shard.
-    """
-    my_coordinate = mesh.get_coordinate()
-
-    if my_coordinate is None:
-        # if rank not in the mesh, return empty offset
-        return ()
-    else:
-        local_offsets = [0] * len(global_shape)
-        local_shape = list(global_shape)
-
-        for idx, placement in enumerate(placements):
-            mesh_dim_size = mesh.size(idx)
-            if isinstance(placement, Shard):
-                shard_dim = placement.dim
-                assert shard_dim < len(
-                    local_shape
-                ), f"Sharding dim {shard_dim} greater than tensor ndim {len(local_shape)}"
-                shard_size, shard_offset = placement._local_shard_size_on_dim(
-                    local_shape[shard_dim],
-                    mesh_dim_size,
-                    my_coordinate[idx],
-                    return_offset=True,
-                )
-                local_shape[shard_dim] = shard_size
-                local_offsets[shard_dim] = shard_offset
-        return tuple(local_offsets)
-
-
 def compute_local_shape_and_global_offset(
     global_shape: ShapeType, mesh: DeviceMesh, placements: Sequence[Placement]
 ) -> Tuple[Tuple[int, ...], Tuple[int, ...]]: