diff --git a/tests/test_models.py b/tests/test_models.py
index 5ead6de8ba..768acd05b7 100644
--- a/tests/test_models.py
+++ b/tests/test_models.py
@@ -90,7 +90,6 @@ def test_model_saving_loading():
     # fit model
     trainer = Trainer(**trainer_options)
     result = trainer.fit(model)
-    real_global_step = trainer.global_step
 
     # traning complete
     assert result == 1, 'amp + ddp model failed to complete'
@@ -124,7 +123,7 @@ def test_model_saving_loading():
     clear_save_dir()
 
 
-def test_cpu_slurm_saving_loading():
+def test_cpu_slurm_save_load():
     """
     Verify model save/load/checkpoint on CPU
     :return:
@@ -154,38 +153,49 @@ def test_cpu_slurm_saving_loading():
     # traning complete
     assert result == 1, 'amp + ddp model failed to complete'
 
-    # test saving checkpoint
-    ckpt_test = os.path.join(save_dir, 'test.ckpt')
-    trainer.save_checkpoint(ckpt_test)
+    # predict with trained model before saving
+    # make a prediction
+    for batch in model.test_dataloader:
+        break
+
+    x, y = batch
+    x = x.view(x.size(0), -1)
+
+    model.eval()
+    pred_before_saving = model(x)
 
     # test registering a save function
     trainer.enable_auto_hpc_walltime_manager()
 
-    # test model loading with a map_location
-    pretrained_model = load_model(exp, save_dir, True)
-
-    # test model preds
-    run_prediction(model.test_dataloader, pretrained_model)
-
-    trainer.model = pretrained_model
-    trainer.optimizers = pretrained_model.configure_optimizers()
-
     # test HPC saving
+    # simulate snapshot on slurm
     saved_filepath = trainer.hpc_save(save_dir, exp)
     assert os.path.exists(saved_filepath)
 
+    # wipe-out trainer model
+    # we want to see if the weights come back correctly
+    trainer.model = LightningTestModel(hparams)
+
     # test HPC loading
     trainer.global_step = 20000000
     trainer.hpc_load(save_dir, on_gpu=False)
     assert trainer.global_step == real_global_step and trainer.global_step != 20000000
 
-    # test freeze on gpu
-    model.freeze()
-    model.unfreeze()
+    # predict with loaded model to make sure answers are the same
+    new_pred = trainer.model(x)
+    assert torch.all(torch.eq(pred_before_saving, new_pred)).item() == 1
 
     clear_save_dir()
 
 
+def test_model_freeze_unfreeze():
+    hparams = get_hparams()
+    model = LightningTestModel(hparams)
+
+    model.freeze()
+    model.unfreeze()
+
+
 def test_amp_gpu_ddp_slurm_managed():
     """
     Make sure DDP + AMP work