diff --git a/tests/kernels/quantization/test_block_fp8.py b/tests/kernels/quantization/test_block_fp8.py
index d0e4f6554a91f..32c77b9a01ece 100644
--- a/tests/kernels/quantization/test_block_fp8.py
+++ b/tests/kernels/quantization/test_block_fp8.py
@@ -54,6 +54,10 @@ def setup_cuda():
     torch.set_default_device("cuda")
 
 
+@pytest.mark.skipif(
+    current_platform.is_fp8_fnuz(),
+    reason="This platform supports e4m3fnuz, not e4m3fn.",
+)
 @pytest.mark.parametrize(
     "num_tokens,d,dtype,group_size,seed",
     itertools.product(NUM_TOKENS, D, DTYPES, GROUP_SIZE, SEEDS),
@@ -78,14 +82,14 @@ def test_per_token_group_quant_fp8(num_tokens, d, dtype, group_size, seed):
 def test_w8a8_block_fp8_matmul(M, N, K, block_size, out_dtype, seed):
     torch.manual_seed(seed)
     factor_for_scale = 1e-2
-    fp8_info = torch.finfo(torch.float8_e4m3fn)
+    fp8_info = torch.finfo(current_platform.fp8_dtype())
     fp8_max, fp8_min = fp8_info.max, fp8_info.min
 
     A_fp32 = (torch.rand(M, K, dtype=torch.float32) - 0.5) * 2 * fp8_max
-    A_fp8 = A_fp32.clamp(min=fp8_min, max=fp8_max).to(torch.float8_e4m3fn)
+    A_fp8 = A_fp32.clamp(min=fp8_min, max=fp8_max).to(current_platform.fp8_dtype())
 
     B_fp32 = (torch.rand(N, K, dtype=torch.float32) - 0.5) * 2 * fp8_max
-    B_fp8 = B_fp32.clamp(min=fp8_min, max=fp8_max).to(torch.float8_e4m3fn)
+    B_fp8 = B_fp32.clamp(min=fp8_min, max=fp8_max).to(current_platform.fp8_dtype())
 
     block_n, block_k = block_size[0], block_size[1]
     n_tiles = (N + block_n - 1) // block_n
@@ -103,6 +107,9 @@ def test_w8a8_block_fp8_matmul(M, N, K, block_size, out_dtype, seed):
     assert rel_diff < 0.001
 
 
+@pytest.mark.skipif(
+    not current_platform.is_cuda(), reason="CUTLASS only supported on CUDA platform."
+)
 @torch.inference_mode()
 def test_w8a8_block_fp8_cutlass_matmul():
     # Test simple case where weight.shape % 128 != 0,
@@ -151,6 +158,10 @@ def test_w8a8_block_fp8_cutlass_matmul():
     assert rel_diff < 0.001
 
 
+@pytest.mark.skipif(
+    current_platform.is_fp8_fnuz(),
+    reason="This platform supports e4m3fnuz, not e4m3fn.",
+)
 @pytest.mark.parametrize(
     "M,N,K,block_size,out_dtype,seed",
     itertools.product(M, N, K, BLOCK_SIZE, OUT_DTYPES, SEEDS),
diff --git a/tests/kernels/quantization/test_cutlass_scaled_mm.py b/tests/kernels/quantization/test_cutlass_scaled_mm.py
index de595b0a34e46..bc4744df7e69e 100644
--- a/tests/kernels/quantization/test_cutlass_scaled_mm.py
+++ b/tests/kernels/quantization/test_cutlass_scaled_mm.py
@@ -15,6 +15,9 @@ from vllm import _custom_ops as ops
 from vllm.platforms import current_platform
 from vllm.utils.math_utils import cdiv
 
+if not current_platform.is_cuda():
+    pytest.skip("These tests use CUTLASS which requires CUDA", allow_module_level=True)
+
 MNK_FACTORS = [
     (1, 256, 128),
     (1, 16384, 1024),
diff --git a/tests/kernels/quantization/test_cutlass_w4a8.py b/tests/kernels/quantization/test_cutlass_w4a8.py
index cccef28f5e931..8cfc993fe8e82 100644
--- a/tests/kernels/quantization/test_cutlass_w4a8.py
+++ b/tests/kernels/quantization/test_cutlass_w4a8.py
@@ -21,6 +21,9 @@ from vllm.model_executor.layers.quantization.utils.quant_utils import (
 from vllm.platforms import current_platform
 from vllm.scalar_type import ScalarType, scalar_types
 
+if not current_platform.is_cuda():
+    pytest.skip("These tests use CUTLASS which requires CUDA", allow_module_level=True)
+
 # TODO: in future PR refactor this and `is_quant_method_supported` in the kernel
 #  unit tests to a common utility function. Currently the use of
 #  `is_quant_method_supported` conflates kernels with quantization methods