diff --git a/deeppavlov/core/trainers/nn_trainer.py b/deeppavlov/core/trainers/nn_trainer.py
index 1765a108e6..2eded68c4a 100644
--- a/deeppavlov/core/trainers/nn_trainer.py
+++ b/deeppavlov/core/trainers/nn_trainer.py
@@ -25,6 +25,7 @@
 from deeppavlov.core.data.data_learning_iterator import DataLearningIterator
 from deeppavlov.core.trainers.fit_trainer import FitTrainer
 from deeppavlov.core.trainers.utils import parse_metrics, NumpyArrayEncoder
+from deeppavlov.core.models.serializable import Serializable
 
 log = getLogger(__name__)
 
@@ -72,6 +73,8 @@ class NNTrainer(FitTrainer):
         log_on_k_batches: count of random train batches to calculate metrics in log (default is ``1``)
         max_test_batches: maximum batches count for pipeline testing and evaluation, overrides ``log_on_k_batches``,
             ignored if negative (default is ``-1``)
+        save_every_n_batches: how often (in batches) to save model into f'{save_path}_{current_step}, the best model
+            is still saved to `save_path`, ignored if negative or zero (default is ``-1``)
         **kwargs: additional parameters whose names will be logged but otherwise ignored
 
 
@@ -103,6 +106,7 @@ def __init__(self, chainer_config: dict, *,
                  validate_first: bool = True,
                  validation_patience: int = 5, val_every_n_epochs: int = -1, val_every_n_batches: int = -1,
                  log_every_n_batches: int = -1, log_every_n_epochs: int = -1, log_on_k_batches: int = 1,
+                 save_every_n_batches: int = -1,
                  **kwargs) -> None:
         super().__init__(chainer_config, batch_size=batch_size, metrics=metrics, evaluation_targets=evaluation_targets,
                          show_examples=show_examples, tensorboard_log_dir=tensorboard_log_dir,
@@ -134,6 +138,7 @@ def _improved(op):
         self.log_every_n_epochs = log_every_n_epochs
         self.log_every_n_batches = log_every_n_batches
         self.log_on_k_batches = log_on_k_batches if log_on_k_batches >= 0 else None
+        self.save_every_n_batches = save_every_n_batches
 
         self.max_epochs = epochs
         self.epoch = start_epoch_num
@@ -296,6 +301,14 @@ def train_on_batches(self, iterator: DataLearningIterator) -> None:
                     self._validate(iterator,
                                    tensorboard_tag='every_n_batches', tensorboard_index=self.train_batches_seen)
 
+                if self.save_every_n_batches > 0 and self.train_batches_seen % self.save_every_n_batches == 0:
+                    # can't specify save path for chainer from here!
+                    # have to duplicate code from chainer.save
+                    main_component = self._chainer.get_main_component()
+                    if isinstance(main_component, Serializable) and hasattr(main_component, 'save_path'):
+                        log.info(f'Saving model at step: {self.train_batches_seen}')
+                        main_component.save(f'{main_component.save_path}_{self.train_batches_seen}')
+
                 self._send_event(event_name='after_batch')
 
                 if 0 < self.max_batches <= self.train_batches_seen: