diff --git a/tests/kernels/ir/test_layernorm.py b/tests/kernels/ir/test_layernorm.py
index c87fb72845..fc79677165 100644
--- a/tests/kernels/ir/test_layernorm.py
+++ b/tests/kernels/ir/test_layernorm.py
@@ -12,7 +12,7 @@ from tests.ir.ir_test_utils import (
     clone_args,
     supported_providers,
 )
-from tests.kernels.allclose_default import get_default_rtol
+from tests.utils import set_random_seed
 from vllm import ir
 from vllm.platforms import current_platform
 
@@ -52,6 +52,7 @@ def test_rms_norm_registration():
 )
 class TestRMSNorm:
     def test_native_semantics(self, dtype, n_tokens, hidden_size, epsilon):
+        set_random_seed(0)
         x, weight, epsilon = ir.ops.rms_norm.generate_inputs(
             num_tokens=4,
             hidden_size=8,
@@ -66,9 +67,13 @@ class TestRMSNorm:
         assert out.dtype == x.dtype
         assert out.device == x.device
 
-        # Check the scaling property of rms norm
+        # Check the scaling property of rms norm. This holds only
+        # approximately: epsilon does not scale with x, so
+        # rms_norm(2x) = x / sqrt(mean(x^2) + epsilon/4), which differs from
+        # rms_norm(x) by the epsilon term. Use the op's declared tolerance
+        # rather than a tighter hard-coded one.
         out2 = rms_norm_native(x * 2.0, weight, epsilon=epsilon)
-        torch.testing.assert_close(out2, out, rtol=get_default_rtol(out), atol=1e-3)
+        assert_close(ir.ops.rms_norm, out2, out)
 
         # Mean square should be approximately 1 (ignoring epsilon and weight scaling)
         combined_norm = out.float() / weight.float()
@@ -266,6 +271,7 @@ def test_vllm_c_fused_add_rms_norm_accepts_nd_input():
 )
 class TestFusedAddRMSNorm:
     def test_native_semantics(self, dtype, n_tokens, hidden_size, epsilon):
+        set_random_seed(0)
         x, x_residual, weight, eps = ir.ops.fused_add_rms_norm.generate_inputs(
             num_tokens=4,
             hidden_size=8,
@@ -304,7 +310,7 @@ class TestFusedAddRMSNorm:
         out2, _ = fused_add_rms_norm_native(
             x * 2.0, torch.zeros_like(x), weight, epsilon=epsilon
         )
-        torch.testing.assert_close(out2, out1, rtol=get_default_rtol(out), atol=1e-3)
+        assert_close(ir.ops.fused_add_rms_norm, out2, out1)
 
         # Check behavior with and without weight
         weight1 = torch.ones_like(weight)