diff --git a/docs/Trainer/Logging.md b/docs/Trainer/Logging.md
index 5fc6cc4f8e..f3e93b1409 100644
--- a/docs/Trainer/Logging.md
+++ b/docs/Trainer/Logging.md
@@ -127,7 +127,13 @@ trainer = Trainer(row_log_interval=10)
 Logs GPU memory when metrics are logged.   
 ``` {.python}
 # DEFAULT
-trainer = Trainer(log_gpu_memory=False)
+trainer = Trainer(log_gpu_memory=None)
+
+# log only the min/max utilization
+trainer = Trainer(log_gpu_memory='min_max')
+
+# log all the GPU memory (if on DDP, logs only that node)
+trainer = Trainer(log_gpu_memory='all')
 ```
 
 ---
diff --git a/pytorch_lightning/root_module/memory.py b/pytorch_lightning/root_module/memory.py
index b4fe297703..6aa4519ec0 100644
--- a/pytorch_lightning/root_module/memory.py
+++ b/pytorch_lightning/root_module/memory.py
@@ -178,6 +178,33 @@ def count_mem_items():  # pragma: no cover
     return nb_params, nb_tensors
 
 
+def get_memory_profile(mode):
+    """
+    'all' means return memory for all gpus
+    'min_max' means return memory for max and min
+    :param mode:
+    :return:
+    """
+    memory_map = get_gpu_memory_map()
+
+    if mode == 'min_max':
+        min_mem = 1000000
+        min_k = None
+        max_mem = 0
+        max_k = None
+        for k, v in memory_map:
+            if v > max_mem:
+                max_mem = v
+                max_k = k
+            if v < min_mem:
+                min_mem = v
+                min_k = k
+
+        memory_map = {min_k: min_mem, max_k: max_mem}
+
+    return memory_map
+
+
 def get_gpu_memory_map():
     """Get the current gpu usage.
 
@@ -196,6 +223,6 @@ def get_gpu_memory_map():
     gpu_memory = [int(x) for x in result.strip().split('\n')]
     gpu_memory_map = {}
     for k, v in zip(range(len(gpu_memory)), gpu_memory):
-        k = 'gpu_%i' % k
+        k = f'gpu_{k}'
         gpu_memory_map[k] = v
     return gpu_memory_map
diff --git a/pytorch_lightning/trainer/trainer.py b/pytorch_lightning/trainer/trainer.py
index c004a4cf8d..cb0e9b858c 100644
--- a/pytorch_lightning/trainer/trainer.py
+++ b/pytorch_lightning/trainer/trainer.py
@@ -15,7 +15,7 @@ import torch.distributed as dist
 from torch.optim.optimizer import Optimizer
 
 from pytorch_lightning.root_module.root_module import LightningModule
-from pytorch_lightning.root_module.memory import get_gpu_memory_map
+from pytorch_lightning.root_module import memory
 from pytorch_lightning.logging import TestTubeLogger
 from pytorch_lightning.trainer.trainer_io import TrainerIO
 from pytorch_lightning.pt_overrides.override_data_parallel import (
@@ -66,7 +66,7 @@ class Trainer(TrainerIO):
                  process_position=0,
                  nb_gpu_nodes=1,
                  gpus=None,
-                 log_gpu_memory=False,
+                 log_gpu_memory=None,
                  show_progress_bar=True,
                  overfit_pct=0.0,
                  track_grad_norm=-1,
@@ -98,7 +98,7 @@ class Trainer(TrainerIO):
         :param process_position: shown in the tqdm bar
         :param nb_gpu_nodes: number of GPU nodes
         :param gpus: int. (ie: 2 gpus) OR list to specify which GPUs [0, 1] or '0,1'
-        :param log_gpu_memory: Bool. If true, adds memory logs
+        :param log_gpu_memory: str. None, 'min_max', 'all'
         :param show_progress_bar: Bool. If true shows tqdm bar
         :param overfit_pct: float. uses this much of all datasets
         :param track_grad_norm: int. -1 no tracking. Otherwise tracks that norm
@@ -1080,8 +1080,8 @@ class Trainer(TrainerIO):
                 metrics = self.__training_tqdm_dict
 
                 # add gpu memory
-                if self.on_gpu and self.log_gpu_memory:
-                    mem_map = get_gpu_memory_map()
+                if self.on_gpu and self.log_gpu_memory is not None:
+                    mem_map = memory.get_memory_profile(mode=self.log_gpu_memory)
                     metrics.update(mem_map)
 
                 # add norms
diff --git a/tests/test_models.py b/tests/test_models.py
index c70663d0a6..9d9536e4bb 100644
--- a/tests/test_models.py
+++ b/tests/test_models.py
@@ -31,6 +31,10 @@ from pytorch_lightning.trainer import trainer_io
 from pytorch_lightning.logging import TestTubeLogger
 from examples import LightningTemplateModel
 
+# generate a list of random seeds for each test
+ROOT_SEED = 1234
+torch.manual_seed(ROOT_SEED)
+np.random.seed(ROOT_SEED)
 RANDOM_SEEDS = list(np.random.randint(0, 10000, 1000))
 
 
@@ -75,8 +79,8 @@ def test_lbfgs_cpu_model():
         overfit_pct=0.20,
         print_nan_grads=True,
         show_progress_bar=False,
-        train_percent_check=0.1,
-        val_percent_check=0.1
+        train_percent_check=0.2,
+        val_percent_check=0.2
     )
 
     model, hparams = get_model(use_test_model=True, lbfgs=True)