diff --git a/flatnav_python/setup.py b/flatnav_python/setup.py
index e85ccec..f3075de 100644
--- a/flatnav_python/setup.py
+++ b/flatnav_python/setup.py
@@ -49,7 +49,7 @@ def platform_has_avx_support():
     "-funroll-loops",  # Unroll loops
     "-mavx",  # Enable AVX instructions
     "-mavx512f",  # Enable AVX-512 instructions
-    "-march=native",  # Enable architecture-specific optimizations
+    # "-march=native",  # Enable architecture-specific optimizations
 ]
 
 if not platform_has_avx_support():
diff --git a/flatnav_python/unit_tests/test_index.py b/flatnav_python/unit_tests/test_index.py
index def8f68..da01ac9 100644
--- a/flatnav_python/unit_tests/test_index.py
+++ b/flatnav_python/unit_tests/test_index.py
@@ -9,7 +9,7 @@
     compute_recall,
     create_index,
 )
-
+import pytest 
 
 def test_flatnav_l2_index_random_dataset():
     dataset_to_index = generate_random_data(dataset_length=30_000, dim=784)
@@ -34,7 +34,7 @@ def test_flatnav_l2_index_random_dataset():
         ground_truth=ground_truth,
     )
 
-
+@pytest.mark.skip(reason="Difficult to run on GitHub actions env due to data download")
 def test_flatnav_l2_index_mnist_dataset():
     training_set, queries, ground_truth, _ = get_ann_benchmark_dataset(
         dataset_name="mnist-784-euclidean"
@@ -85,7 +85,7 @@ def test_flatnav_ip_index_random_dataset():
         ground_truth=ground_truth,
     )
 
-
+@pytest.mark.skip(reason="Difficult to run on GitHub actions env due to data download")
 def test_flatnav_index_with_reordering():
     training_set, queries, ground_truth, _ = get_ann_benchmark_dataset(
         dataset_name="mnist-784-euclidean"
diff --git a/flatnav_python/unit_tests/test_parallel_insertions.py b/flatnav_python/unit_tests/test_parallel_insertions.py
index 53f0cce..18cfb5d 100644
--- a/flatnav_python/unit_tests/test_parallel_insertions.py
+++ b/flatnav_python/unit_tests/test_parallel_insertions.py
@@ -3,15 +3,16 @@
 import flatnav
 from flatnav.index import index_factory
 from flatnav.index import L2Index
-from .test_utils import get_ann_benchmark_dataset, compute_recall, create_index
+from .test_utils import generate_random_data, compute_recall, create_index
 import os
 import numpy as np
 
 
 def test_parallel_insertions_yield_similar_recall():
-    training_set, queries, ground_truth, _ = get_ann_benchmark_dataset(
-        dataset_name="mnist-784-euclidean"
-    )
+    training_set = generate_random_data(dataset_length=30_000, dim=784)
+    queries = generate_random_data(dataset_length=5_000, dim=784)
+    ground_truth = np.random.randint(low=0, high=50, size=(5_000, 100))
+    
     index = create_index(
         distance_type="l2",
         dim=training_set.shape[1],