diff --git a/composer/devices/device_tpu.py b/composer/devices/device_tpu.py
index b91d1bc478..813fc49924 100644
--- a/composer/devices/device_tpu.py
+++ b/composer/devices/device_tpu.py
@@ -26,6 +26,7 @@ class DeviceTPU(Device):
     More details.
     """
 
+    dist_backend = 'xla'
     name = 'tpu'
 
     def __init__(self):
diff --git a/composer/trainer/trainer.py b/composer/trainer/trainer.py
index 7411dc4393..0d2349bf93 100644
--- a/composer/trainer/trainer.py
+++ b/composer/trainer/trainer.py
@@ -2567,6 +2567,11 @@ def _train_microbatch(self, use_grad_scaling: bool, current_batch_size: int,
                 microbatch_loss.mul_(microbatch_num_samples / current_batch_size)
                 microbatch_loss.backward(create_graph=self._backwards_create_graph)
 
+            if self.state.device.dist_backend == 'xla':
+                # For xla devices, the program between any pair of mark_steps() calls is compiled. With out this, the
+                # microbatching loop is unrolled, drastically increasing compile time.
+                xm.mark_step()
+
             self.engine.run_event(Event.AFTER_BACKWARD)
 
             # Use microbatch outputs to update training metrics
diff --git a/composer/utils/dist.py b/composer/utils/dist.py
index 65edb5e80c..5b8dd5df68 100644
--- a/composer/utils/dist.py
+++ b/composer/utils/dist.py
@@ -37,6 +37,7 @@
 import logging
 import os
 import pickle
+import sys
 import time
 from contextlib import contextmanager
 from typing import TYPE_CHECKING, Any, List, Optional, Sequence, TypeVar, Union, cast
@@ -44,8 +45,12 @@
 import torch
 import torch.distributed as dist
 import torch.utils.data
+from packaging import version
 
-from composer.utils.device import get_device, is_hpu_installed
+from composer.utils.device import get_device, is_hpu_installed, is_tpu_installed
+
+if is_tpu_installed():
+    import torch_xla
 
 if TYPE_CHECKING:
     from composer.devices import Device
@@ -534,7 +539,15 @@ def initialize_dist(device: Union[str, Device], timeout: float = 300.0):
 
     dist_env_vars_match_defaults = all(os.environ.get(k, v) == v for (k, v) in dist_env_var_defaults.items())
 
-    if dist_env_vars_match_defaults:
+    if device_obj.dist_backend == 'xla':
+        if not 'torch_xla' in sys.modules:
+            raise RuntimeError('PyTorch XLA package not found. In order to use XLA based devices '
+                               'PyTorch XLA must be installed.')
+        if version.parse(torch_xla.__version__) < version.parse('2.1.0'):
+            raise RuntimeError(f'PyTorch XLA version must be at least 2.1.0, found {torch_xla.__version__}.')
+        # XLA initialization requires the init_method to be set
+        dist.init_process_group(device_obj.dist_backend, init_method='xla://')
+    elif dist_env_vars_match_defaults:
         # Fill in the remaining single-rank variables
         os.environ.update(dist_env_var_defaults)
         dist.init_process_group(device_obj.dist_backend, store=dist.HashStore(), world_size=1, rank=0)