diff --git a/src/kernels/layer.py b/src/kernels/layer.py
index 47d918c..4e67561 100644
--- a/src/kernels/layer.py
+++ b/src/kernels/layer.py
@@ -87,7 +87,7 @@ class Device:
 
     Args:
         type (`str`):
-            The device type (e.g., "cuda", "mps", "cpu").
+            The device type (e.g., "cuda", "mps", "rocm").
         properties ([`CUDAProperties`], *optional*):
             Device-specific properties. Currently only [`CUDAProperties`] is supported for CUDA devices.
 
@@ -531,7 +531,7 @@ def insert(self, device: Device, repos: Dict[Mode, LayerRepositoryProtocol]):
 
 def _validate_device_type(device_type: str) -> None:
     """Validate that the device type is supported."""
-    supported_devices = {"cuda", "rocm", "mps", "cpu"}
+    supported_devices = {"cuda", "rocm", "mps"}
     if device_type not in supported_devices:
         raise ValueError(
             f"Unsupported device type '{device_type}'. Supported device types are: {', '.join(sorted(supported_devices))}"
@@ -789,7 +789,7 @@ def kernelize(
             The mode that the kernel is going to be used in. For example, `Mode.TRAINING | Mode.TORCH_COMPILE`
             kernelizes the model for training with `torch.compile`.
         device (`Union[str, torch.device]`, *optional*):
-            The device type to load kernels for. Supported device types are: "cuda", "rocm", "mps", "cpu".
+            The device type to load kernels for. Supported device types are: "cuda", "mps", "rocm".
             The device type will be inferred from the model parameters when not provided.
         use_fallback (`bool`, *optional*, defaults to `True`):
             Whether to use the original forward method of modules when no compatible kernel could be found.
diff --git a/tests/test_layer.py b/tests/test_layer.py
index 8b920a5..a6b224c 100644
--- a/tests/test_layer.py
+++ b/tests/test_layer.py
@@ -110,24 +110,20 @@ def forward(
 
 @pytest.mark.cuda_only
 @pytest.mark.parametrize("cls", [SiluAndMulWithKernel, SiluAndMulStringDevice])
-@pytest.mark.parametrize("device", ["cuda", "cpu"])
-def test_hub_forward(cls, device):
+def test_hub_forward(cls):
     torch.random.manual_seed(0)
 
     silu_and_mul = SiluAndMul()
-    X = torch.randn((32, 64), device=device)
+    X = torch.randn((32, 64), device="cuda")
     Y = silu_and_mul(X)
 
-    silu_and_mul_with_kernel = kernelize(cls(), device=device, mode=Mode.INFERENCE)
+    silu_and_mul_with_kernel = kernelize(cls(), device="cuda", mode=Mode.INFERENCE)
     Y_kernel = silu_and_mul_with_kernel(X)
 
     torch.testing.assert_close(Y_kernel, Y)
 
     assert silu_and_mul.n_calls == 1
-    if device == "cuda":
-        assert silu_and_mul_with_kernel.n_calls == 0
-    else:
-        assert silu_and_mul_with_kernel.n_calls == 1
+    assert silu_and_mul_with_kernel.n_calls == 0
 
 
 @pytest.mark.rocm_only