diff --git a/flashinfer/utils.py b/flashinfer/utils.py
index 18167c1ad2..e6c2bd836d 100644
--- a/flashinfer/utils.py
+++ b/flashinfer/utils.py
@@ -586,6 +586,19 @@ def is_sm12x_supported(device: torch.device) -> bool:
     return version_at_least(torch.version.cuda, min_cuda)
 
 
+def is_cvt_rs_supported(device: torch.device = None) -> bool:
+    """Check if the GPU supports the PTX cvt.rs.f16x2.f32 instruction.
+
+    This is a non-forward-compatible SM100a feature — not all SM >= 100 have it.
+    In particular, SM120 (Blackwell lite) does NOT support it.
+    """
+    if device is None:
+        device = torch.device("cuda")
+    major, _ = get_compute_capability(device)
+    # SM100a and SM110a support cvt.rs; SM120 does not.
+    return major in (10, 11)
+
+
 def determine_mla_backend(device: torch.device) -> str:
     return "fa3" if is_sm90a_supported(device) else "fa2"
 
diff --git a/tests/mamba/test_philox_rounding.py b/tests/mamba/test_philox_rounding.py
index 66c344c87f..9e55edf71f 100644
--- a/tests/mamba/test_philox_rounding.py
+++ b/tests/mamba/test_philox_rounding.py
@@ -12,7 +12,7 @@
 import triton.language as tl
 from torch.utils.cpp_extension import load_inline
 
-from flashinfer.utils import get_compute_capability
+from flashinfer.utils import get_compute_capability, is_cvt_rs_supported
 
 
 # ---------------------------------------------------------------------------
@@ -222,8 +222,8 @@ def philox_module():
 def stochastic_round_module():
     """Compile cvt_rs_f16x2_f32 test kernel with sm_100a (hardware PTX path)."""
     major, minor = get_compute_capability(torch.device("cuda"))
-    if major < 10:
-        pytest.skip("cvt.rs.f16x2.f32 requires sm_100a (Blackwell or later)")
+    if not is_cvt_rs_supported(torch.device("cuda")):
+        pytest.skip("cvt.rs.f16x2.f32 requires sm_100a; not supported on this GPU")
     # Append 'a' suffix for SM >= 9, matching flashinfer/compilation_context.py:44-45
     minor_str = f"{minor}a" if major >= 9 else str(minor)
     gencode = f"-gencode=arch=compute_{major}{minor_str},code=sm_{major}{minor_str}"
diff --git a/tests/mamba/test_selective_state_update_mtp.py b/tests/mamba/test_selective_state_update_mtp.py
index 0dc91e444c..3ec0c9c0bf 100644
--- a/tests/mamba/test_selective_state_update_mtp.py
+++ b/tests/mamba/test_selective_state_update_mtp.py
@@ -10,7 +10,7 @@
 import torch
 
 import flashinfer
-from flashinfer.utils import get_compute_capability
+from flashinfer.utils import is_cvt_rs_supported
 
 from .triton_reference.selective_state_update import selective_state_update_triton
 from .utils import create_test_inputs, clone_preserving_strides
@@ -1114,11 +1114,11 @@ def make_inputs(
     def make_reference_output(self, inputs):
         """Compute reference output using Triton with stochastic rounding."""
         state_ref = clone_preserving_strides(inputs["state_cache"])
-        major, _ = get_compute_capability(torch.device("cuda"))
-        # Triton cvt.rs.f16x2.f32 requires SM100a+; on older GPUs the Triton
-        # reference falls back to regular rounding while the CUDA kernel still
-        # exercises its software stochastic rounding path.
-        rand_seed = self.RAND_SEED if major >= 10 else None
+        # Triton cvt.rs.f16x2.f32 requires SM100a (non-forward-compatible);
+        # on unsupported GPUs the Triton reference falls back to regular
+        # rounding while the CUDA kernel still exercises its software
+        # stochastic rounding path.
+        rand_seed = self.RAND_SEED if is_cvt_rs_supported() else None
         y_ref = selective_state_update_triton(
             state_ref,
             inputs["x"],
@@ -1249,11 +1249,11 @@ def make_reference_output(self, inputs):
         """Compute reference output using Triton with SR and intermediate states."""
         state_ref = clone_preserving_strides(inputs["state_cache"])
         intermediate_states_ref = inputs["intermediate_states_buffer"].clone()
-        major, _ = get_compute_capability(torch.device("cuda"))
-        # Triton cvt.rs.f16x2.f32 requires SM100a+; on older GPUs the Triton
-        # reference falls back to regular rounding while the CUDA kernel still
-        # exercises its software stochastic rounding path.
-        rand_seed = self.RAND_SEED if major >= 10 else None
+        # Triton cvt.rs.f16x2.f32 requires SM100a (non-forward-compatible);
+        # on unsupported GPUs the Triton reference falls back to regular
+        # rounding while the CUDA kernel still exercises its software
+        # stochastic rounding path.
+        rand_seed = self.RAND_SEED if is_cvt_rs_supported() else None
 
         y_ref = selective_state_update_triton(
             state_ref,
diff --git a/tests/mamba/test_selective_state_update_stp.py b/tests/mamba/test_selective_state_update_stp.py
index 7da054d18f..dfb1794520 100644
--- a/tests/mamba/test_selective_state_update_stp.py
+++ b/tests/mamba/test_selective_state_update_stp.py
@@ -3,7 +3,7 @@
 import torch
 
 import flashinfer
-from flashinfer.utils import get_compute_capability
+from flashinfer.utils import get_compute_capability, is_cvt_rs_supported
 
 from .triton_reference.selective_state_update import selective_state_update_triton
 from .utils import create_test_inputs, clone_preserving_strides
@@ -666,11 +666,11 @@ def make_inputs(self, batch, nheads, dim, dstate, _state_dtype, weight_dtype):
     def make_reference_output(self, inputs):
         """Compute reference output using Triton with stochastic rounding."""
         state_ref = inputs["state_cache"].clone()
-        major, _ = get_compute_capability(torch.device("cuda"))
-        # Triton cvt.rs.f16x2.f32 requires SM100a+; on older GPUs the Triton
-        # reference falls back to regular rounding while the CUDA kernel still
-        # exercises its software stochastic rounding path.
-        rand_seed = self.RAND_SEED if major >= 10 else None
+        # Triton cvt.rs.f16x2.f32 requires SM100a (non-forward-compatible);
+        # on unsupported GPUs the Triton reference falls back to regular
+        # rounding while the CUDA kernel still exercises its software
+        # stochastic rounding path.
+        rand_seed = self.RAND_SEED if is_cvt_rs_supported() else None
         y_ref = selective_state_update_triton(
             state_ref,
             inputs["x"],