diff --git a/tests/fluxion/layers/test_converter.py b/tests/fluxion/layers/test_converter.py
index fabe0ac..89350e2 100644
--- a/tests/fluxion/layers/test_converter.py
+++ b/tests/fluxion/layers/test_converter.py
@@ -1,20 +1,25 @@
 import torch
 import pytest
+from warnings import warn
+
 import refiners.fluxion.layers as fl
 from refiners.fluxion.layers.chain import Distribute
 
 
-@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA is not available")
-def test_converter_device_single_tensor() -> None:
+def test_converter_device_single_tensor(test_device: torch.device) -> None:
+    if test_device.type != "cuda":
+        warn("only running on CUDA, skipping")
+        pytest.skip()
+
     chain = fl.Chain(
         fl.Converter(set_device=True, set_dtype=False),
-        fl.Linear(in_features=1, out_features=1, device="cuda:0"),
+        fl.Linear(in_features=1, out_features=1, device=test_device),
     )
 
     tensor = torch.randn(1, 1)
     converted_tensor = chain(tensor)
 
-    assert converted_tensor.device == torch.device(device="cuda:0")
+    assert converted_tensor.device == torch.device(device=test_device)
 
 
 def test_converter_dtype_single_tensor() -> None:
@@ -29,13 +34,16 @@ def test_converter_dtype_single_tensor() -> None:
     assert converted_tensor.dtype == torch.float64
 
 
-@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA is not available")
-def test_converter_multiple_tensors() -> None:
+def test_converter_multiple_tensors(test_device: torch.device) -> None:
+    if test_device.type != "cuda":
+        warn("only running on CUDA, skipping")
+        pytest.skip()
+
     chain = fl.Chain(
         fl.Converter(set_device=True, set_dtype=True),
         Distribute(
-            fl.Linear(in_features=1, out_features=1, device="cuda:0", dtype=torch.float64),
-            fl.Linear(in_features=1, out_features=1, device="cuda:0", dtype=torch.float64),
+            fl.Linear(in_features=1, out_features=1, device=test_device, dtype=torch.float64),
+            fl.Linear(in_features=1, out_features=1, device=test_device, dtype=torch.float64),
         ),
     )
 
@@ -44,9 +52,9 @@ def test_converter_multiple_tensors() -> None:
 
     converted_tensor1, converted_tensor2 = chain(tensor1, tensor2)
 
-    assert converted_tensor1.device == torch.device(device="cuda:0")
+    assert converted_tensor1.device == torch.device(device=test_device)
     assert converted_tensor1.dtype == torch.float64
-    assert converted_tensor2.device == torch.device(device="cuda:0")
+    assert converted_tensor2.device == torch.device(device=test_device)
     assert converted_tensor2.dtype == torch.float64