diff --git a/tests/operators/test_pre_cache_len_concat.py b/tests/operators/test_pre_cache_len_concat.py
new file mode 100644
index 00000000000..dd2fc8dcc49
--- /dev/null
+++ b/tests/operators/test_pre_cache_len_concat.py
@@ -0,0 +1,95 @@
+import unittest
+
+import numpy as np
+import paddle
+
+from fastdeploy.model_executor.ops.gpu import pre_cache_len_concat
+
+
+def ref_pre_cache_len_concat(seq_lens_decoder, seq_lens_this_time, block_size):
+    """
+    Reference implementation.
+    """
+    bsz = len(seq_lens_this_time)
+    cu_seqlens_k = np.zeros(bsz + 1, dtype=np.int32)
+    batch_ids = []
+    tile_ids_per_batch = []
+    total_tokens = 0
+    gridx = 0
+
+    for bid in range(bsz):
+        cache_len = int(seq_lens_decoder[bid])
+        q_len = int(seq_lens_this_time[bid])
+        if q_len <= 0:
+            cache_len = 0
+        loop_times = (cache_len + block_size - 1) // block_size  # div_up
+        for tile_id in range(loop_times):
+            batch_ids.append(bid)
+            tile_ids_per_batch.append(tile_id)
+        gridx += loop_times
+        total_tokens += cache_len + q_len
+        cu_seqlens_k[bid + 1] = total_tokens
+
+    return (
+        cu_seqlens_k,
+        np.array(batch_ids, dtype=np.int32),
+        np.array(tile_ids_per_batch, dtype=np.int32),
+        np.array([gridx], dtype=np.int32),
+        np.array([total_tokens], dtype=np.int32),
+    )
+
+
+class TestPreCacheLenConcat(unittest.TestCase):
+    def setUp(self):
+        paddle.set_device("gpu")
+
+    def test_smoke_shapes(self):
+        bsz = 3
+        max_dec_len, block_size = 16, 4
+
+        seq_lens_decoder = np.array([8, 4, 2], dtype=np.int32)
+        seq_lens_this_time = np.array([2, 3, 1], dtype=np.int32)
+
+        seq_lens_decoder_t = paddle.to_tensor(seq_lens_decoder, dtype="int32")
+        seq_lens_this_time_t = paddle.to_tensor(seq_lens_this_time, dtype="int32")
+
+        outputs = pre_cache_len_concat(seq_lens_decoder_t, seq_lens_this_time_t, max_dec_len, block_size)
+        cu_seqlens_k, batch_ids, tile_ids, num_blocks, kv_token_num = [out.numpy() for out in outputs]
+
+        # Shape checks
+        self.assertEqual(cu_seqlens_k.shape[0], bsz + 1)
+        self.assertEqual(batch_ids.shape, tile_ids.shape)
+        self.assertEqual(num_blocks.shape, (1,))
+        self.assertEqual(kv_token_num.shape, (1,))
+
+        # Basic value sanity checks
+        self.assertTrue(np.all(np.diff(cu_seqlens_k) >= 0))  # monotonic
+        self.assertGreaterEqual(num_blocks[0], 0)
+        self.assertGreaterEqual(kv_token_num[0], 0)
+
+    def test_strict_values_with_ref(self):
+        max_dec_len, block_size = 16, 4
+
+        seq_lens_decoder = np.array([8, 4, 2], dtype=np.int32)
+        seq_lens_this_time = np.array([2, 3, 1], dtype=np.int32)
+
+        seq_lens_decoder_t = paddle.to_tensor(seq_lens_decoder, dtype="int32")
+        seq_lens_this_time_t = paddle.to_tensor(seq_lens_this_time, dtype="int32")
+
+        outputs = pre_cache_len_concat(seq_lens_decoder_t, seq_lens_this_time_t, max_dec_len, block_size)
+        cu_seqlens_k, batch_ids, tile_ids, num_blocks, kv_token_num = [out.numpy() for out in outputs]
+
+        # Reference implementation
+        ref_outputs = ref_pre_cache_len_concat(seq_lens_decoder, seq_lens_this_time, block_size)
+        ref_cu, ref_batch_ids, ref_tile_ids, ref_num_blocks, ref_kv_token_num = ref_outputs
+
+        # Compare all outputs against reference
+        np.testing.assert_array_equal(cu_seqlens_k, ref_cu)
+        np.testing.assert_array_equal(batch_ids[: len(ref_batch_ids)], ref_batch_ids)
+        np.testing.assert_array_equal(tile_ids[: len(ref_tile_ids)], ref_tile_ids)
+        self.assertEqual(num_blocks[0], ref_num_blocks[0])
+        self.assertEqual(kv_token_num[0], ref_kv_token_num[0])
+
+
+if __name__ == "__main__":
+    unittest.main()