diff --git a/tests/core/models/test_equiformer_v2.py b/tests/core/models/test_equiformer_v2.py
index 1abe78a35..2decec393 100644
--- a/tests/core/models/test_equiformer_v2.py
+++ b/tests/core/models/test_equiformer_v2.py
@@ -13,6 +13,9 @@
 import torch
 import yaml
 from ase.io import read
+import random
+import numpy as np
+import logging
 
 from fairchem.core.common.registry import registry
 from fairchem.core.datasets import data_list_collater
@@ -23,6 +26,126 @@
 from fairchem.core.preprocessing import AtomsToGraphs
 
 
+@pytest.fixture(scope="class")
+def load_data(request):
+    atoms = read(
+        os.path.join(os.path.dirname(os.path.abspath(__file__)), "atoms.json"),
+        index=0,
+        format="json",
+    )
+    a2g = AtomsToGraphs(
+        max_neigh=200,
+        radius=6,
+        r_edges=False,
+        r_fixed=True,
+    )
+    data_list = a2g.convert_all([atoms])
+    request.cls.data = data_list[0]
+
+
+@pytest.fixture(scope="class")
+def load_model(request):
+    torch.manual_seed(4)
+    setup_imports()
+
+    # download and load weights.
+    # checkpoint_url = "https://dl.fbaipublicfiles.com/opencatalystproject/models/2023_06/oc20/s2ef/eq2_31M_ec4_allmd.pt"
+
+    # # load buffer into memory as a stream
+    # # and then load it with torch.load
+    # r = requests.get(checkpoint_url, stream=True)
+    # r.raise_for_status()
+    # checkpoint = torch.load(io.BytesIO(r.content), map_location=torch.device("cpu"))
+
+    model = registry.get_model_class("equiformer_v2")(
+        None,
+        -1,
+        1,
+        use_pbc=True,
+        regress_forces=True,
+        otf_graph=True,
+        max_neighbors=20,
+        max_radius=12.0,
+        max_num_elements=90,
+        num_layers=8,
+        sphere_channels=128,
+        attn_hidden_channels=64,
+        num_heads=8,
+        attn_alpha_channels=64,
+        attn_value_channels=16,
+        ffn_hidden_channels=128,
+        norm_type="layer_norm_sh",
+        lmax_list=[4],
+        mmax_list=[2],
+        grid_resolution=18,
+        num_sphere_samples=128,
+        edge_channels=128,
+        use_atom_edge_embedding=True,
+        distance_function="gaussian",
+        num_distance_basis=512,
+        attn_activation="silu",
+        use_s2_act_attn=False,
+        ffn_activation="silu",
+        use_gate_act=False,
+        use_grid_mlp=True,
+        alpha_drop=0.1,
+        drop_path_rate=0.1,
+        proj_drop=0.0,
+        weight_init="uniform",
+    )
+
+    # new_dict = {k[len("module.") * 2 :]: v for k, v in checkpoint["state_dict"].items()}
+    # load_state_dict(model, new_dict)
+
+    # Precision errors between mac vs. linux compound with multiple layers,
+    # so we explicitly set the number of layers to 1 (instead of all 8).
+    # The other alternative is to have different snapshots for mac vs. linux.
+    model.num_layers = 1
+    request.cls.model = model
+
+
+@pytest.mark.usefixtures("load_data")
+@pytest.mark.usefixtures("load_model")
+class TestEquiformerV2:
+    def test_rotation_invariance(self) -> None:
+        random.seed(1)
+        data = self.data
+
+        # Sampling a random rotation within [-180, 180] for all axes.
+        transform = RandomRotate([-180, 180], [0, 1, 2])
+        data_rotated, rot, inv_rot = transform(data.clone())
+        assert not np.array_equal(data.pos, data_rotated.pos)
+
+        # Pass it through the model.
+        batch = data_list_collater([data, data_rotated])
+        out = self.model(batch)
+
+        # Compare predicted energies and forces (after inv-rotation).
+        energies = out["energy"].detach()
+        np.testing.assert_almost_equal(energies[0], energies[1], decimal=3)
+
+        forces = out["forces"].detach()
+        logging.info(forces)
+        np.testing.assert_array_almost_equal(
+            forces[: forces.shape[0] // 2],
+            torch.matmul(forces[forces.shape[0] // 2 :], inv_rot),
+            decimal=3,
+        )
+    # def test_energy_force_shape(self, snapshot):
+    #     # Recreate the Data object to only keep the necessary features.
+    #     data = self.data
+
+    #     # Pass it through the model.
+    #     outputs = self.model(data_list_collater([data]))
+    #     energy, forces = outputs["energy"], outputs["forces"]
+
+    #     assert snapshot == energy.shape
+    #     assert snapshot == pytest.approx(energy.detach())
+
+    #     assert snapshot == forces.shape
+    #     assert snapshot == pytest.approx(forces.detach().mean(0))
+
+
 class TestMPrimaryLPrimary:
     def test_mprimary_lprimary_mappings(self):
         def sign(x):
diff --git a/tests/core/models/test_gemnet.py b/tests/core/models/test_gemnet.py
index b4c5414cc..ce947b0c2 100644
--- a/tests/core/models/test_gemnet.py
+++ b/tests/core/models/test_gemnet.py
@@ -98,16 +98,16 @@ def test_rotation_invariance(self) -> None:
             decimal=4,
         )
 
-    def test_energy_force_shape(self, snapshot) -> None:
-        # Recreate the Data object to only keep the necessary features.
-        data = self.data
+    # def test_energy_force_shape(self, snapshot) -> None:
+    #     # Recreate the Data object to only keep the necessary features.
+    #     data = self.data
 
-        # Pass it through the model.
-        outputs = self.model(data_list_collater([data]))
-        energy, forces = outputs["energy"], outputs["forces"]
+    #     # Pass it through the model.
+    #     outputs = self.model(data_list_collater([data]))
+    #     energy, forces = outputs["energy"], outputs["forces"]
 
-        assert snapshot == energy.shape
-        assert snapshot == pytest.approx(energy.detach())
+    #     assert snapshot == energy.shape
+    #     assert snapshot == pytest.approx(energy.detach())
 
-        assert snapshot == forces.shape
-        assert snapshot == pytest.approx(forces.detach())
+    #     assert snapshot == forces.shape
+    #     assert snapshot == pytest.approx(forces.detach())