diff --git a/src/mcore_bridge/model/gpts/qwen4_exp.py b/src/mcore_bridge/model/gpts/qwen4_exp.py
index 4414371..f13c676 100644
--- a/src/mcore_bridge/model/gpts/qwen4_exp.py
+++ b/src/mcore_bridge/model/gpts/qwen4_exp.py
@@ -180,8 +180,9 @@ def _qsa_select(self, hidden_states, attn_kwargs, position_ids=None):
                                     f'falling back to full attention ({"packing/thd" if is_thd else f"CP={cp_size}"}).')
                 return None, False
             raise RuntimeError(f'QSA needs the sparse kernel here ({"packing/thd" if is_thd else f"CP={cp_size}"}), '
-                               'but QSASparseCoreAttention was not installed -- triton is missing or '
-                               f'kv_channels={getattr(self.config, "kv_channels", None)} is not a power of two. '
+                               'but QSASparseCoreAttention was not installed -- triton is missing, '
+                               f'kv_channels={getattr(self.config, "kv_channels", None)} is not a power of two, '
+                               'or this device is not CUDA (the kernel is not compiled for NPU). '
                                'Use --padding_free false with context_parallel_size 1 to take the bool-mask path, '
                                f'or set {QSA_SPARSE_KERNEL_ENV}=0 to fall back to full attention.')
         if cp_size > 1 and getattr(self.config, 'cp_comm_type', None) != 'all_gather':
diff --git a/src/mcore_bridge/model/modules/kernels/qsa_kernels.py b/src/mcore_bridge/model/modules/kernels/qsa_kernels.py
index fa57d75..38fdc71 100644
--- a/src/mcore_bridge/model/modules/kernels/qsa_kernels.py
+++ b/src/mcore_bridge/model/modules/kernels/qsa_kernels.py
@@ -57,10 +57,15 @@ def qsa_sparse_supported(head_dim: int) -> bool:
     if not HAVE_TRITON or head_dim <= 0 or (head_dim & (head_dim - 1)):
         return False
     if not torch.cuda.is_available():
-        logger.warning_once('The QSA sparse kernel is only tested on CUDA GPUs and may fail to compile on '
-                            'this device. If you hit triton compile errors, set '
-                            f'{QSA_SPARSE_KERNEL_ENV}=0 to disable it (QSA then falls back to full '
-                            'attention under packing/CP, and to the bool-mask path otherwise).')
+        # Triton-Ascend compiles this kernel with the CUDA tile sizes and fails
+        # (UB/Cc overflow). Do not install it there. Packing and CP then take the
+        # explicit QSA_SPARSE_KERNEL=0 full-attention fallback instead of dying
+        # inside the compiler. The bool-mask path (CP == 1, not packed) is unchanged.
+        logger.warning_once('The QSA sparse kernel is CUDA-only and is disabled on this device. '
+                            f'Set {QSA_SPARSE_KERNEL_ENV}=0 to fall back to full attention under '
+                            'packing or context parallelism. With CP == 1 and packing off, QSA '
+                            'keeps the bool-mask path.')
+        return False
     return True
 
 
diff --git a/tests/test_qsa_sparse_supported.py b/tests/test_qsa_sparse_supported.py
new file mode 100644
index 0000000..1b92da8
--- /dev/null
+++ b/tests/test_qsa_sparse_supported.py
@@ -0,0 +1,52 @@
+# Copyright (c) ModelScope Contributors. All rights reserved.
+"""The QSA sparse kernel stays off unless CUDA can compile it."""
+import ast
+from pathlib import Path
+
+
+def _load(have_triton, cuda):
+    path = Path(__file__).resolve().parents[1] / 'src/mcore_bridge/model/modules/kernels/qsa_kernels.py'
+    tree = ast.parse(path.read_text())
+    fn = next(node for node in tree.body if isinstance(node, ast.FunctionDef) and node.name == 'qsa_sparse_supported')
+    module = ast.Module(body=[fn], type_ignores=[])
+    ast.fix_missing_locations(module)
+    warnings = []
+
+    class _Cuda:
+
+        @staticmethod
+        def is_available():
+            return cuda
+
+    class _Logger:
+
+        @staticmethod
+        def warning_once(message):
+            warnings.append(message)
+
+    namespace = {
+        'HAVE_TRITON': have_triton,
+        'QSA_SPARSE_KERNEL_ENV': 'QSA_SPARSE_KERNEL',
+        'logger': _Logger(),
+        'torch': type('Torch', (), {'cuda': _Cuda})(),
+        'use_qsa_sparse_kernel': lambda: True,
+    }
+    exec(compile(module, str(path), 'exec'), namespace)
+    return namespace['qsa_sparse_supported'], warnings
+
+
+def test_non_cuda_does_not_enable_the_kernel():
+    supported, warnings = _load(have_triton=True, cuda=False)
+    assert supported(64) is False
+    assert warnings and 'CUDA-only' in warnings[0]
+
+
+def test_cuda_power_of_two_head_stays_enabled():
+    supported, warnings = _load(have_triton=True, cuda=True)
+    assert supported(128) is True
+    assert warnings == []
+
+
+def test_non_power_of_two_head_stays_disabled():
+    supported, _ = _load(have_triton=True, cuda=True)
+    assert supported(96) is False