diff --git a/tests/jax/test_custom_call_compute.py b/tests/jax/test_custom_call_compute.py
index 9ff0c11757..b81f3fb9bf 100644
--- a/tests/jax/test_custom_call_compute.py
+++ b/tests/jax/test_custom_call_compute.py
@@ -57,8 +57,8 @@
 FP8_COMPUTE_TYPE = [jnp.float8_e4m3fn, jnp.float8_e5m2]
 LN_CASES = [(256, 128), (128, 256)]
 DTYPES = [jnp.bfloat16, jnp.float32]
-is_fp8_supported, reason = helper.is_fp8_available()
-is_mxfp8_supported, reason = helper.is_fp8_available(ScalingMode.MXFP8_1D_SCALING)
+is_fp8_supported, fp8_unsupported_reason = helper.is_fp8_available()
+is_mxfp8_supported, mxfp8_unsupported_reason = helper.is_fp8_available(ScalingMode.MXFP8_1D_SCALING)
 
 supported_scaling_modes = []
 """ Find supported scaling modes"""
@@ -209,7 +209,7 @@ def test_act_grad(self, shape, activation_type):
         assert_allclose(prim_out, ref_out, dtype=x.dtype)
         assert_allclose(prim_grad, ref_grad, dtype=x.dtype)
 
-    @pytest.mark.skipif(not is_fp8_supported, reason=reason)
+    @pytest.mark.skipif(not is_fp8_supported, reason=fp8_unsupported_reason)
     @pytest_parametrize_wrapper("shape", ALL_ACTIVATION_SHAPES)
     @pytest_parametrize_wrapper("activation_type", ACTIVATION_TYPES)
     @pytest_parametrize_wrapper("output_type", [jnp.float8_e4m3fn, jnp.float8_e5m2])
@@ -240,7 +240,7 @@ def test_act_grad_with_tensor_scaling_fp8(
         assert_allclose(prim_out, ref_out, dtype=output_type)
         assert_allclose(prim_grad, ref_grad, dtype=output_type)
 
-    @pytest.mark.skipif(not is_mxfp8_supported, reason=reason)
+    @pytest.mark.skipif(not is_mxfp8_supported, reason=mxfp8_unsupported_reason)
     @pytest_parametrize_wrapper("shape", ALL_ACTIVATION_SHAPES)
     @pytest_parametrize_wrapper("activation_type", ACTIVATION_TYPES)
     @pytest_parametrize_wrapper("output_type", [jnp.float8_e4m3fn, jnp.float8_e5m2])
@@ -270,7 +270,7 @@ def test_act_forward_with_tensor_scaling_fp8(
 
         assert_bitwise_scaled_tensors(te_output, jax_output)
 
-    @pytest.mark.skipif(not is_mxfp8_supported, reason=reason)
+    @pytest.mark.skipif(not is_mxfp8_supported, reason=mxfp8_unsupported_reason)
     @pytest_parametrize_wrapper("shape", [(2, 64, 1, 256)])
     @pytest_parametrize_wrapper("activation_type", ACTIVATION_TYPES)
     @pytest_parametrize_wrapper("output_type", [jnp.float8_e4m3fn, jnp.float8_e5m2])
@@ -391,7 +391,7 @@ def test_norm_grad(self, n, hidden, norm_type, zero_centered_gamma, epsilon, inp
             n, hidden, norm_type, zero_centered_gamma, epsilon, inp_dtype, quantizer=None
         )
 
-    @pytest.mark.skipif(not is_fp8_supported, reason=reason)
+    @pytest.mark.skipif(not is_fp8_supported, reason=fp8_unsupported_reason)
     # No Norm FWD E5M2 in TE backend
     @pytest_parametrize_wrapper("out_dtype", [jnp.float8_e4m3fn])
     @pytest_parametrize_wrapper(
@@ -506,7 +506,7 @@ def _test_norm_forward(
         if norm_type == "layernorm":
             assert_allclose(mu, ref_mu, dtype=inp_dtype)
 
-    @pytest.mark.skipif(not is_fp8_supported, reason=reason)
+    @pytest.mark.skipif(not is_fp8_supported, reason=fp8_unsupported_reason)
     # No Norm FWD E5M2 in TE backend
     @pytest_parametrize_wrapper("out_dtype", [jnp.float8_e4m3fn])
     @pytest_parametrize_wrapper(
@@ -542,7 +542,7 @@ def test_norm_forward_with_tensor_scaling_fp8(
             q_layout=q_layout,
         )
 
-    @pytest.mark.skipif(not is_mxfp8_supported, reason=reason)
+    @pytest.mark.skipif(not is_mxfp8_supported, reason=mxfp8_unsupported_reason)
     @pytest.mark.parametrize("out_dtype", [jnp.float8_e4m3fn, jnp.float8_e5m2])
     def test_norm_forward_with_block_scaling_fp8(
         self, n, hidden, norm_type, zero_centered_gamma, epsilon, inp_dtype, out_dtype
@@ -591,7 +591,7 @@ def test_norm_forward_with_block_scaling_fp8(
 }
 
 
-@pytest.mark.skipif(not is_fp8_supported, reason=reason)
+@pytest.mark.skipif(not is_fp8_supported, reason=fp8_unsupported_reason)
 @pytest_parametrize_wrapper("in_dtype", QUANTIZATION_INPUT_DTYPE)
 @pytest_parametrize_wrapper("q_dtype", [jnp.float8_e4m3fn, jnp.float8_e5m2])
 @pytest_parametrize_wrapper("input_shape,flatten_axis", ALL_QUANTIZE_TEST_SHAPES_AND_FLATTEN_AXES)
@@ -638,7 +638,7 @@ def test_quantize_bitwise(
         assert_bitwise_scaled_tensors(te_output, jax_output)
 
 
-@pytest.mark.skipif(not is_fp8_supported, reason=reason)
+@pytest.mark.skipif(not is_fp8_supported, reason=fp8_unsupported_reason)
 @pytest_parametrize_wrapper("in_dtype", QUANTIZATION_INPUT_DTYPE)
 @pytest_parametrize_wrapper("input_shape", [(8, 16, 32)])
 @pytest_parametrize_wrapper("q_dtype", [jnp.float8_e4m3fn])
@@ -692,7 +692,7 @@ def test_grouped_qdq(
 @pytest_parametrize_wrapper("in_dtype", QUANTIZATION_INPUT_DTYPE)
 class TestFusedQuantize:
 
-    @pytest.mark.skipif(not is_fp8_supported, reason=reason)
+    @pytest.mark.skipif(not is_fp8_supported, reason=fp8_unsupported_reason)
     @pytest_parametrize_wrapper("scaling_mode", supported_scaling_modes)
     @pytest_parametrize_wrapper("input_shape,flatten_axis", QUANTIZE_TEST_SHAPES_AND_FLATTEN_AXES)
     @pytest_parametrize_wrapper("out_dtype", QUANTIZE_OUTPUT_DTYPES)
@@ -793,7 +793,7 @@ def test_quantize_dact_dbias_no_quantization(
             q_layout=QuantizeLayout.ROWWISE,
         )
 
-    @pytest.mark.skipif(not is_fp8_supported, reason=reason)
+    @pytest.mark.skipif(not is_fp8_supported, reason=fp8_unsupported_reason)
     @pytest_parametrize_wrapper("activation_type", ACTIVATION_TYPES)
     @pytest_parametrize_wrapper("input_shape", ALL_ACTIVATION_SHAPES)
     @pytest_parametrize_wrapper("out_dtype", QUANTIZE_OUTPUT_DTYPES)
@@ -817,7 +817,7 @@ def test_quantize_dact_dbias_tensor_scaling(
             q_layout=q_layout,
         )
 
-    @pytest.mark.skipif(not is_mxfp8_supported, reason=reason)
+    @pytest.mark.skipif(not is_mxfp8_supported, reason=mxfp8_unsupported_reason)
     @pytest_parametrize_wrapper("activation_type", ACTIVATION_TYPES)
     @pytest_parametrize_wrapper(
         "input_shape", [s for s in ALL_ACTIVATION_SHAPES if is_shape_supported_by_mxfp8(s)]
@@ -886,7 +886,7 @@ def test_gemm_bf16(self, m, n, k, data_layout):
 
         assert_allclose(primitive_out, ref_out, dtype=jnp.bfloat16)
 
-    @pytest.mark.skipif(not is_fp8_supported, reason=reason)
+    @pytest.mark.skipif(not is_fp8_supported, reason=fp8_unsupported_reason)
     @pytest_parametrize_wrapper("m,n,k", [(64, 32, 64)])
     @pytest_parametrize_wrapper("q_dtype", [jnp.float8_e4m3fn, jnp.float8_e5m2])
     @pytest_parametrize_wrapper("scaling_mode", supported_scaling_modes)
@@ -928,7 +928,7 @@ def ref_func(x, w, data_layout):
         assert_allclose(primitive_x_grad, ref_x_grad, dtype=jnp.bfloat16)
         assert_allclose(primitive_w_grad, ref_w_grad, dtype=jnp.bfloat16)
 
-    @pytest.mark.skipif(not is_fp8_supported, reason=reason)
+    @pytest.mark.skipif(not is_fp8_supported, reason=fp8_unsupported_reason)
     @pytest_parametrize_wrapper("m,n,k", [(64, 32, 64)])
     @pytest_parametrize_wrapper("q_dtype", [jnp.float8_e4m3fn, jnp.float8_e5m2])
     @pytest_parametrize_wrapper("scaling_mode", supported_scaling_modes)
@@ -992,7 +992,7 @@ def _ref_jax_norm_impl(x, gamma, beta, norm_type, zero_centered_gamma, eps, quan
 
 
 class TestFusedDense:
-    @pytest.mark.skipif(not is_fp8_supported, reason=reason)
+    @pytest.mark.skipif(not is_fp8_supported, reason=fp8_unsupported_reason)
     @pytest.mark.parametrize("m,n,k", [(64, 32, 64)])
     @pytest.mark.parametrize("q_dtype", [jnp.float8_e4m3fn, jnp.float8_e5m2])
     @pytest.mark.parametrize("scaling_mode", supported_scaling_modes)
@@ -1077,7 +1077,7 @@ def ref_func(x, w, gamma, beta):
         if beta is not None:
             assert_allclose(prim_beta_grad, ref_beta_grad, dtype=q_dtype)
 
-    @pytest.mark.skipif(not is_fp8_supported, reason=reason)
+    @pytest.mark.skipif(not is_fp8_supported, reason=fp8_unsupported_reason)
     @pytest.mark.parametrize("m,n,k", [(64, 32, 64)])
     @pytest.mark.parametrize("activation_type", [("gelu",), ("gelu", "linear")])
     @pytest.mark.parametrize("q_dtype", [jnp.float8_e4m3fn, jnp.float8_e5m2])
@@ -1284,7 +1284,7 @@ def test_grouped_gemm_fp16(self, dtype, input_shape, layout):
         prim_out = tex.grouped_gemm(lhs, rhs, group_sizes, contracting_dims)
         self._assert_grouped_gemm_output(prim_out, group_sizes, ref_out, dtype)
 
-    @pytest.mark.skipif(not is_fp8_supported, reason=reason)
+    @pytest.mark.skipif(not is_fp8_supported, reason=fp8_unsupported_reason)
     @pytest.mark.parametrize("fwd_bwd_dtype", fwd_bwd_dtypes)
     @pytest_parametrize_wrapper("scaling_mode", supported_scaling_modes)
     @pytest_parametrize_wrapper("layout", ["NN"])
@@ -1360,7 +1360,7 @@ def test_grouped_dense_grad_fp16(self, dtype, input_shape):
         assert_allclose(prim_wgrad, ref_wgrad, dtype=dtype)
         assert_allclose(prim_dbias, ref_dbias, dtype=dtype)
 
-    @pytest.mark.skipif(not is_fp8_supported, reason=reason)
+    @pytest.mark.skipif(not is_fp8_supported, reason=fp8_unsupported_reason)
     @pytest.mark.parametrize(
         "fwd_bwd_dtype",
         [(jnp.float8_e4m3fn, jnp.float8_e4m3fn), (jnp.float8_e4m3fn, jnp.float8_e5m2)],