diff --git a/tests/collections/llm/test_mnist_model_nemo2.py b/tests/collections/llm/test_mnist_model_nemo2.py
index 58ac1039085f..52fde4c26af9 100644
--- a/tests/collections/llm/test_mnist_model_nemo2.py
+++ b/tests/collections/llm/test_mnist_model_nemo2.py
@@ -471,6 +471,12 @@ def _reset_microbatch_calculator():
     nemo.collections.nlp.modules.common.megatron.megatron_init.initialize_model_parallel_for_nemo
     """  # noqa: D205, D415
     megatron.core.num_microbatches_calculator._GLOBAL_NUM_MICROBATCHES_CALCULATOR = None
+    # Clean up any process gorups created in testing
+    torch.cuda.empty_cache()
+    if parallel_state.is_initialized():
+        parallel_state.destroy_model_parallel()
+    if torch.distributed.is_initialized():
+        torch.distributed.destroy_process_group()
 
 
 @contextmanager
@@ -541,6 +547,7 @@ def test_train_mnist_litautoencoder_with_megatron_strategy_single_gpu(tmpdir):
                 resume_ignore_no_checkpoint=True,  # When false this will throw an error with no existing checkpoint.
             ),
         )
+        trainer._teardown()
     with reset_global_microbatch_calculator():
         pred_strategy = nl.MegatronStrategy(
             tensor_model_parallel_size=1,
@@ -578,3 +585,4 @@ def test_train_mnist_litautoencoder_with_megatron_strategy_single_gpu(tmpdir):
         }, f"We expect forward output from predit_step, not the loss, got: {forward_output}"
         assert forward_output["x_hat"].shape == (len(data_module.mnist_test), 28 * 28)
         assert forward_output["z"].shape == (len(data_module.mnist_test), 3)  # latent bottleneck in model of dim 3
+        predict_trainer._teardown()