diff --git a/pytorch_lightning/models/trainer.py b/pytorch_lightning/models/trainer.py
index 9c4b7d5fe2..ff11150b26 100644
--- a/pytorch_lightning/models/trainer.py
+++ b/pytorch_lightning/models/trainer.py
@@ -610,14 +610,18 @@ class Trainer(TrainerIO):
         if self.proc_rank == 0:
             self.experiment.save()
 
+        # track model now.
+        # if cluster resets state, the model will update with the saved weights
+        self.model = model
+
         # enable cluster checkpointing
+        # also restores training state
         if self.cluster is not None:  # pragma: no cover
             self.enable_auto_hpc_walltime_manager()
 
         # ---------------------------
         # CORE TRAINING LOOP
         # ---------------------------
-        self.model = model
         self.__train()
 
     def __train(self):
diff --git a/tests/test_models.py b/tests/test_models.py
index 5cee2e5ef6..e95870e8c4 100644
--- a/tests/test_models.py
+++ b/tests/test_models.py
@@ -85,15 +85,22 @@ def test_cpu_slurm_save_load():
         checkpoint_callback=ModelCheckpoint(save_dir)
     )
     trainer = Trainer(**trainer_options)
+    model = LightningTestModel(hparams)
 
-    # test HPC loading
-    trainer.hpc_load(save_dir, on_gpu=False)
-    assert trainer.global_step == real_global_step and trainer.global_step > 0
+    # set the epoch start hook so we can predict before the model does the full training
+    def assert_pred_same():
+        assert trainer.global_step == real_global_step and trainer.global_step > 0
 
-    # predict with loaded model to make sure answers are the same
-    trainer.model.eval()
-    new_pred = trainer.model(x)
-    assert torch.all(torch.eq(pred_before_saving, new_pred)).item() == 1
+        # predict with loaded model to make sure answers are the same
+        trainer.model.eval()
+        new_pred = trainer.model(x)
+        assert torch.all(torch.eq(pred_before_saving, new_pred)).item() == 1
+
+    model.on_epoch_start = assert_pred_same
+
+    # by calling fit again, we trigger training, loading weights from the cluster
+    # and our hook to predict using current model before any more weight updates
+    trainer.fit(model)
 
     clear_save_dir()