diff --git a/tests/kernels/core/test_layernorm.py b/tests/kernels/core/test_layernorm.py
index f8f9660942af..6d799fbc1072 100644
--- a/tests/kernels/core/test_layernorm.py
+++ b/tests/kernels/core/test_layernorm.py
@@ -14,9 +14,11 @@
 HIDDEN_SIZES = [8, 768, 769, 5120, 5125, 8192]  # Arbitrary values for testing
 ADD_RESIDUAL = [False, True]
 SEEDS = [0]
-CUDA_DEVICES = [
-    f"cuda:{i}" for i in range(1 if torch.accelerator.device_count() == 1 else 2)
-]
+DEVICES = ["cpu"]
+if torch.cuda.is_available():
+    DEVICES = [
+        f"cuda:{i}" for i in range(1 if torch.accelerator.device_count() == 1 else 2)
+    ]
 
 
 @pytest.mark.parametrize("num_tokens", NUM_TOKENS)
@@ -24,7 +26,7 @@
 @pytest.mark.parametrize("add_residual", ADD_RESIDUAL)
 @pytest.mark.parametrize("dtype", DTYPES)
 @pytest.mark.parametrize("seed", SEEDS)
-@pytest.mark.parametrize("device", CUDA_DEVICES)
+@pytest.mark.parametrize("device", DEVICES)
 @pytest.mark.parametrize("strided_input", [False, True])
 @torch.inference_mode()
 def test_rms_norm(
@@ -46,12 +48,19 @@ def test_rms_norm(
     x = torch.randn(num_tokens, last_dim, dtype=dtype)
     x = x[..., :hidden_size]
     assert x.is_contiguous() != strided_input
-    x *= scale
+    x = x * scale if add_residual else x
     residual = torch.randn_like(x) * scale if add_residual else None
 
     # NOTE(woosuk): The reference implementation should be executed first
     # because the custom kernel is in-place.
-    ref_out = layer.forward_native(x, residual)
+    ref_out = layer.forward_static(
+        x,
+        variance_epsilon=layer.variance_epsilon,
+        orig_dtype=x.dtype,
+        weight=layer.weight,
+        hidden_size=layer.hidden_size,
+        residual=residual,
+    )
     out = layer(x, residual)
     # NOTE(woosuk): LayerNorm operators (including RMS) typically have larger
     # numerical errors than other operators because they involve reductions.
@@ -79,7 +88,7 @@ def test_rms_norm(
 @pytest.mark.parametrize("dtype", DTYPES)
 @pytest.mark.parametrize("quant_scale", [0.01, 1.0, 10.0])
 @pytest.mark.parametrize("seed", SEEDS)
-@pytest.mark.parametrize("device", CUDA_DEVICES)
+@pytest.mark.parametrize("device", DEVICES)
 @pytest.mark.parametrize("strided_input", [False, True])
 def test_fused_rms_norm_quant(
     num_tokens: int,