diff --git a/tensorrt_llm/_torch/attention_backend/sparse/dsa.py b/tensorrt_llm/_torch/attention_backend/sparse/dsa.py
index 0bcad59624..361473b4d1 100644
--- a/tensorrt_llm/_torch/attention_backend/sparse/dsa.py
+++ b/tensorrt_llm/_torch/attention_backend/sparse/dsa.py
@@ -1535,7 +1535,9 @@ class Indexer(nn.Module):
                                dim=-1)
         q_pe, k_pe = self.rotary_emb(position_ids, [q_pe, k_pe.unsqueeze(1)])
         k_pe = k_pe[:, 0, :]
-        return q_pe, q_nope, k_pe, k_nope
+        q = self._prep_q_or_k(q_pe, q_nope)
+        k = self._prep_q_or_k(k_pe, k_nope)
+        return q, k
 
     def _weight_proj(self, hidden_states: torch.Tensor):
         return self.weights_proj(_to_float(hidden_states))
@@ -1556,14 +1558,7 @@ class Indexer(nn.Module):
         quant_block_size = metadata.kv_cache_manager.quant_block_size
         assert quant_block_size == 128, "Only support quant_block_size = 128 for now"
 
-        q_pe, q_nope, k_pe, k_nope = q_and_k
-        q, k = maybe_execute_in_parallel(
-            lambda: self._prep_q_or_k(q_pe, q_nope),
-            lambda: self._prep_q_or_k(k_pe, k_nope),
-            self.ln_events[0],
-            self.ln_events[1],
-            self.aux_stream,
-        )
+        q, k = q_and_k
         q_fp8, q_scale = q
         k_fp8, k_scale = k
         q_fp8 = q_fp8.view(-1, self.n_heads, self.head_dim)