diff --git a/comfy/text_encoders/qwen35.py b/comfy/text_encoders/qwen35.py
index ce9b07464..2ab4990fc 100644
--- a/comfy/text_encoders/qwen35.py
+++ b/comfy/text_encoders/qwen35.py
@@ -12,6 +12,12 @@ import comfy.text_encoders.qwen_vl
 
 from .llama import BaseLlama, BaseGenerate, Llama2_, MLP, RMSNorm, apply_rope
 
+FLA_IS_AVAILABLE = False
+try:
+    from fla.ops.gated_delta_rule import chunk_gated_delta_rule as fla_chunk_gated_delta_rule
+    FLA_IS_AVAILABLE = True
+except ImportError:
+    pass
 
 def _qwen35_layer_types(n):
     return [("full_attention" if (i + 1) % 4 == 0 else "linear_attention") for i in range(n)]
@@ -253,11 +259,19 @@ class GatedDeltaNet(nn.Module):
                 query = query.repeat_interleave(rep, dim=2)
                 key = key.repeat_interleave(rep, dim=2)
 
-            core_attn_out, last_recurrent_state = torch_chunk_gated_delta_rule(
-                query, key, value, g=g, beta=beta,
-                initial_state=None,
-                output_final_state=past_key_value is not None,
-            )
+            if FLA_IS_AVAILABLE:
+                core_attn_out, last_recurrent_state = fla_chunk_gated_delta_rule(
+                    query, key, value, g=g, beta=beta,
+                    initial_state=None,
+                    output_final_state=past_key_value is not None,
+                    use_qk_l2norm_in_kernel=True,
+                )
+            else:
+                core_attn_out, last_recurrent_state = torch_chunk_gated_delta_rule(
+                    query, key, value, g=g, beta=beta,
+                    initial_state=None,
+                    output_final_state=past_key_value is not None,
+                )
 
             present_key_value = None
             if past_key_value is not None: