diff --git a/tests/kernels/core/test_fused_quant_layernorm.py b/tests/kernels/core/test_fused_quant_layernorm.py
index b7401e644..d450e81a8 100644
--- a/tests/kernels/core/test_fused_quant_layernorm.py
+++ b/tests/kernels/core/test_fused_quant_layernorm.py
@@ -14,9 +14,10 @@ from vllm.model_executor.layers.quantization.utils.fp8_utils import (
 from vllm.model_executor.layers.quantization.utils.int8_utils import (
     per_token_group_quant_int8,
 )
+from vllm.platforms import current_platform
 
 DTYPES = [torch.bfloat16, torch.float]
-QUANT_DTYPES = [torch.int8, torch.float8_e4m3fn]
+QUANT_DTYPES = [torch.int8, current_platform.fp8_dtype()]
 VEC_HIDDEN_SIZES = [1024, 1025, 1027, 1029]
 # Avoid combinatorial explosion with full Cartesian product
 NUM_TOKENS_HIDDEN_SIZES = [
@@ -61,14 +62,14 @@ def ref_dynamic_per_token_or_block_quant(
     group_size: list[int] | None,
 ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor | None]:
     if scale_ub is not None:
-        assert quant_dtype == torch.float8_e4m3fn
+        assert quant_dtype == current_platform.fp8_dtype()
 
     # Norm
     torch_out, residual = ref_rms_norm(rms_norm_layer, x, residual)
 
     # Quant
     if group_size is not None:
-        if quant_dtype == torch.float8_e4m3fn:
+        if quant_dtype == current_platform.fp8_dtype():
             torch_out, scales = per_token_group_quant_fp8(
                 torch_out, group_size=group_size[1], use_ue8m0=False
             )
@@ -78,7 +79,7 @@ def ref_dynamic_per_token_or_block_quant(
                 torch_out, group_size=group_size[1]
             )
     else:
-        if quant_dtype == torch.float8_e4m3fn:
+        if quant_dtype == current_platform.fp8_dtype():
             torch_out, scales = ops.scaled_fp8_quant(
                 torch_out, scale_ub=scale_ub, use_per_token_if_dynamic=True
             )
@@ -162,6 +163,7 @@ def test_rms_norm(
     if torch.cuda.is_available():
         torch.cuda.manual_seed(seed)
     torch.set_default_device(device)
+    torch.cuda.set_device(device)
 
     if group_size is not None and hidden_size % group_size[1] != 0:
         # skip
@@ -171,7 +173,7 @@ def test_rms_norm(
         # blockwise baseline doesn't support scale_ub
         return
 
-    if has_scale_ub and quant_dtype != torch.float8_e4m3fn:
+    if has_scale_ub and quant_dtype != current_platform.fp8_dtype():
         # skip
         return