diff --git a/CHANGELOG.md b/CHANGELOG.md
index 5cc510647..87edde806 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -13,9 +13,11 @@ Remember to align the itemized text with the first line of an item within a list
     bumped to 0.4.0 but this has been rolled back in this release to give users
     of both TensorFlow and JAX more time to migrate to a newer TensorFlow
     release.
+  * `jax.experimental.mesh_utils` can now create an efficient mesh for TPU v5e.
   * jax now depends on jaxlib directly. This change was enabled by the CUDA
     plugin switch: there are no longer multiple jaxlib variants. You can install
     a CPU-only jax with `pip install jax`, no extras required.
+
 * Deprecations
   * Internal pretty-printing tools `jax.core.pp_*` are deprecated, and will be removed
     in a future release.
diff --git a/jax/experimental/mesh_utils.py b/jax/experimental/mesh_utils.py
index 0060a954f..7157be1e2 100644
--- a/jax/experimental/mesh_utils.py
+++ b/jax/experimental/mesh_utils.py
@@ -31,6 +31,7 @@ logger = logging.getLogger(__name__)
 _TPU_V2 = 'TPU v2'
 _TPU_V3 = 'TPU v3'
 _TPU_V4 = 'TPU v4'
+_TPU_V5_LITE = "TPU v5 lite"
 
 # Maps physical topology -> mesh shape -> transpose to use for jekbradbury's
 # famous contiguous mesh trick.
@@ -64,7 +65,8 @@ _TRANSPOSE_TRICKS: dict[
 
 # Physical ordering of core IDs in a tray that creates a ring
 _TRAY_RING_ORDER = (0, 1, 2, 3, 6, 7, 4, 5)
-
+_TRAY_2x2_RING_ORDER = (0, 1, 3, 2)
+_TRAY_4x4_RING_ORDER = (0, 1, 2, 3, 7, 6, 5, 9, 10, 11, 15, 14, 13, 12, 8, 4)
 
 def _tpu_v2_v3_create_device_mesh(
     mesh_shape: Sequence[int],
@@ -94,6 +96,45 @@ def _tpu_v2_v3_create_device_mesh(
     return np.asarray(devices).reshape(mesh_shape)
 
 
+def _vlc_create_device_mesh(
+    mesh_shape: Sequence[int], devices: Sequence[Any], **unused_kwargs
+) -> np.ndarray | None:
+  """Creates rotated pincer device assignment for selected topologies.
+
+  Args:
+    mesh_shape: Logical mesh shape used by the model.
+    devices: TPU devices.
+    **unused_kwargs: ...
+
+  Returns:
+    None or reordered devices reshaped as `mesh_shape`.
+  """
+  max_x, max_y, max_z = max(getattr(d, "coords", (0, 0, 0)) for d in devices)
+  bound_x, bound_y, bound_z = max_x + 1, max_y + 1, max_z + 1
+  # Our ring re-ordering makes sense only if the passed-in devices are
+  # sequential, which may not always be the case. reversed() changes z-minor to
+  # x-minor.
+  sequential_devices = sorted(
+      devices,
+      key=lambda d: tuple(reversed(getattr(d, "coords", (0, 0, 0)))))
+
+  if bound_x == bound_y == 2 and bound_z == 1 and len(devices) == 4:  # VLC2x2
+    device_mesh = np.asarray(sequential_devices)
+    device_mesh = device_mesh[np.array(_TRAY_2x2_RING_ORDER)]
+    device_mesh = device_mesh.reshape(mesh_shape)
+    return device_mesh
+
+  if bound_x == bound_y == 4 and bound_z == 1 and len(devices) == 16:  # VLP4x4
+    # Only uses ring order if the whole mesh is a replica group.
+    if max(mesh_shape) == len(devices):
+      device_mesh = np.asarray(sequential_devices)
+      device_mesh = device_mesh[np.array(_TRAY_4x4_RING_ORDER)]
+      device_mesh = device_mesh.reshape(mesh_shape)
+      return device_mesh
+
+  return None
+
+
 # Registers functions to create device mesh for specific device kinds. Takes
 # precedence over the more general logic in create_device_mesh(). Handler may
 # return None; in that case, it will fall back to using the default logic.
@@ -103,6 +144,7 @@ device_kind_handler_dict: dict[
 ] = {
     _TPU_V2: _tpu_v2_v3_create_device_mesh,
     _TPU_V3: _tpu_v2_v3_create_device_mesh,
+    _TPU_V5_LITE: _vlc_create_device_mesh,
 }