diff --git a/python/llm/src/ipex_llm/transformers/models/llama.py b/python/llm/src/ipex_llm/transformers/models/llama.py
index e542c6c5822..077faf50c5b 100644
--- a/python/llm/src/ipex_llm/transformers/models/llama.py
+++ b/python/llm/src/ipex_llm/transformers/models/llama.py
@@ -1346,10 +1346,31 @@ def llama_attention_forward_4_36_original(
         key_states = repeat_kv(key_states, self.num_key_value_groups)
         value_states = repeat_kv(value_states, self.num_key_value_groups)
         # otherwise, use native attention
-        attn_output, attn_weights = native_sdp(query_states, key_states, value_states,
-                                               attention_mask,
-                                               bsz, q_len, kv_seq_len,
-                                               self.head_dim, self.num_heads, output_attentions)
+        if query_states.device.type == "xpu":
+            attn_output, attn_weights = native_sdp(query_states, key_states, value_states,
+                                                   attention_mask,
+                                                   bsz, q_len, kv_seq_len,
+                                                   self.head_dim, self.num_heads, output_attentions)
+        else:
+            # CPU path
+            if not output_attentions:
+                attn_output = torch.nn.functional.scaled_dot_product_attention(
+                    query_states,
+                    key_states,
+                    value_states,
+                    attn_mask=attention_mask,
+                    dropout_p=self.attention_dropout if self.training else 0.0,
+                    # The q_len > 1 is necessary to match with
+                    # AttentionMaskConverter.to_causal_4d that
+                    # does not create a causal mask in case q_len == 1.
+                    is_causal=self.is_causal and attention_mask is None and q_len > 1,
+                )
+            else:
+                attn_output, attn_weights = native_sdp(query_states, key_states, value_states,
+                                                       attention_mask,
+                                                       bsz, q_len, kv_seq_len,
+                                                       self.head_dim,
+                                                       self.num_heads, output_attentions)
 
     attn_output_size = (bsz, self.num_heads, q_len, self.head_dim)
     if attn_output.size() != attn_output_size:
@@ -1773,9 +1794,6 @@ def llama_model_forward_4_36_internal(
     if inputs_embeds is None:
         inputs_embeds = self.embed_tokens(input_ids)
 
-    # IPEX-LLM modifications:
-    # Disable sdpa for CPU
-    self._use_sdpa = False
     if self._use_flash_attention_2:
         # 2d mask is passed through the layers
         attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) \