diff --git a/python/llm/src/ipex_llm/transformers/models/llama.py b/python/llm/src/ipex_llm/transformers/models/llama.py
index ae9b7812..2d5597c2 100644
--- a/python/llm/src/ipex_llm/transformers/models/llama.py
+++ b/python/llm/src/ipex_llm/transformers/models/llama.py
@@ -46,7 +46,8 @@ from ipex_llm.transformers.models.utils import init_fp8_kv_cache, append_fp8_kv_
 from ipex_llm.transformers.models.utils import is_enough_kv_cache_room_4_31, \
     apply_rotary_pos_emb, is_enough_kv_cache_room_4_36
 from ipex_llm.transformers.models.utils import apply_rotary_pos_emb_no_cache_xpu
-from ipex_llm.transformers.models.utils import use_flash_attention, use_sdp, use_sdp_fp8
+from ipex_llm.transformers.models.utils import use_flash_attention, use_sdp, use_sdp_fp8, \
+    use_sdp_causal
 from ipex_llm.transformers.models.utils import mlp_fusion_check, fp16_fusion_check
 from ipex_llm.transformers.models.utils import use_decoding_fast_path
 from transformers.modeling_outputs import BaseModelOutputWithPast
@@ -1678,8 +1679,17 @@ def llama_attention_forward_4_38_quantized(
     if len(past_key_value.key_cache) <= self.layer_idx:
         repeated_key_states = repeat_kv(key_states, self.num_key_value_groups)
         repeated_value_states = repeat_kv(value_states, self.num_key_value_groups)
-        if should_split_qkv_tensor(query_states, bsz, self.num_heads,
-                                   q_len, kv_seq_len, output_attentions):
+        if use_cache:
+            cache_kwargs = None
+            key_states, value_states = past_key_value.update(key_states, value_states,
+                                                             self.layer_idx, cache_kwargs)
+        if use_cache and use_sdp_causal(q_len, kv_seq_len, self.head_dim,
+                                        query_states, self.training):
+            import xe_addons
+            attn_output = xe_addons.sdp_fp8_causal(query_states, key_states,
+                                                   value_states, attention_mask)
+        elif should_split_qkv_tensor(query_states, bsz, self.num_heads,
+                                     q_len, kv_seq_len, output_attentions):
             attn_output, _ = native_sdp_split_qkv_tensor(query_states, repeated_key_states,
                                                          repeated_value_states,
                                                          attention_mask, cache_position,
@@ -1719,10 +1729,6 @@ def llama_attention_forward_4_38_quantized(
                 attn_weights = nn.functional.softmax(attn_weights, dim=-1,
                                                      dtype=torch.float32).to(query_states.dtype)
             attn_output = torch.matmul(attn_weights, repeated_value_states)
-        if use_cache:
-            cache_kwargs = None
-            key_states, value_states = past_key_value.update(key_states, value_states,
-                                                             self.layer_idx, cache_kwargs)
     else:
         cache_kwargs = None  # Specific to RoPE models
         key_states, value_states = past_key_value.update(key_states, value_states,