diff --git a/tests/test_models.py b/tests/test_models.py
index 822e0f2f..5c79dd2e 100644
--- a/tests/test_models.py
+++ b/tests/test_models.py
@@ -1,15 +1,24 @@
 import pytest
 import torch
+import platform
+import os
+import fnmatch
 
 from timm import list_models, create_model
 
-MAX_FWD_SIZE = 320
+
+if 'GITHUB_ACTIONS' in os.environ and 'Linux' in platform.system():
+    # GitHub Linux runner is slower and hits memory limits sooner than MacOS, exclude bigger models
+    EXCLUDE_FILTERS = ['*efficientnet_l2*']
+else:
+    EXCLUDE_FILTERS = []
+MAX_FWD_SIZE = 384
 MAX_BWD_SIZE = 128
 MAX_FWD_FEAT_SIZE = 448
 
 
 @pytest.mark.timeout(120)
-@pytest.mark.parametrize('model_name', list_models())
+@pytest.mark.parametrize('model_name', list_models(exclude_filters=EXCLUDE_FILTERS))
 @pytest.mark.parametrize('batch_size', [1])
 def test_model_forward(model_name, batch_size):
     """Run a single forward pass with each model"""
@@ -28,7 +37,8 @@ def test_model_forward(model_name, batch_size):
 
 
 @pytest.mark.timeout(120)
-@pytest.mark.parametrize('model_name', list_models(exclude_filters='dla*'))  # DLA models have an issue TBD
+# DLA models have an issue TBD, add them to exclusions
+@pytest.mark.parametrize('model_name', list_models(exclude_filters=EXCLUDE_FILTERS + ['dla*']))
 @pytest.mark.parametrize('batch_size', [2])
 def test_model_backward(model_name, batch_size):
     """Run a single forward pass with each model"""
@@ -65,7 +75,8 @@ def test_model_default_cfgs(model_name, batch_size):
     pool_size = cfg['pool_size']
     input_size = model.default_cfg['input_size']
 
-    if all([x <= MAX_FWD_FEAT_SIZE for x in input_size]) and 'efficientnet_l2' not in model_name:
+    if all([x <= MAX_FWD_FEAT_SIZE for x in input_size]) and \
+            not any([fnmatch.fnmatch(model_name, x) for x in EXCLUDE_FILTERS]):
         # pool size only checked if default res <= 448 * 448 to keep resource down
         input_size = tuple([min(x, MAX_FWD_FEAT_SIZE) for x in input_size])
         outputs = model.forward_features(torch.randn((batch_size, *input_size)))