diff --git a/optimum/exporters/ipex/modeling_utils.py b/optimum/exporters/ipex/modeling_utils.py
index fdc7ea86b..039d5201b 100755
--- a/optimum/exporters/ipex/modeling_utils.py
+++ b/optimum/exporters/ipex/modeling_utils.py
@@ -30,6 +30,7 @@
 logger = logging.getLogger(__name__)
 
 _IPEX_MINIMUM_VERSION_FOR_PATCHING = "2.4.0"
+_IPEX_MINIMUM_VERSION_FOR_FLASH_VARLEN_ATTN = "2.5.0"
 
 
 if is_ipex_version("<", _IPEX_MINIMUM_VERSION_FOR_PATCHING):
@@ -588,6 +589,44 @@ def postprocess_attention_output(self, attn_output):
         attn_output = attn_output.reshape(-1, attn_output.shape[-2] * attn_output.shape[-1])
         return attn_output
 
+    def varlen_attn(self, query, key, value, past_key_value, input_lens):
+        # prefill, remove padding
+        attn_output = torch.empty_like(query)
+        seq_len_tensor = torch.cat((input_lens.new_tensor([0]), input_lens.cumsum(-1).int()))
+        if past_key_value and is_ipex_version(">=", _IPEX_MINIMUM_VERSION_FOR_FLASH_VARLEN_ATTN):
+            PagedAttention.flash_attn_varlen_func(
+                attn_output,
+                query,
+                key,
+                value,
+                seq_len_tensor,
+                seq_len_tensor,
+                input_lens.max(),
+                input_lens.max(),
+                1.0 / math.sqrt(self.head_dim),
+                True,
+                past_key_value.block_tables,
+                None,
+            )
+        else:
+            varlen_attention(
+                query.contiguous() if query.device.type == "xpu" else query,
+                key.contiguous() if key.device.type == "xpu" else key,
+                value.contiguous() if value.device.type == "xpu" else value,
+                attn_output,
+                seq_len_tensor,
+                seq_len_tensor,
+                input_lens.max(),
+                input_lens.max(),
+                0.0,
+                1.0 / math.sqrt(self.head_dim),
+                False,
+                True,
+                False,
+                None,
+            )
+        return attn_output
+
     def forward(
         self,
         hidden_states: torch.Tensor,
@@ -609,27 +648,15 @@ def forward(
 
         if past_key_value is not None:
             key_cache, value_cache = past_key_value.update(key, value, self.layer_idx, attention_mask, input_lens)
+        else:
+            key_cache, value_cache = key, value
 
-        attn_output = torch.empty_like(query)
         if past_len == 0:
-            # prefill, remove padding
-            seq_len_tensor = torch.cat((input_lens.new_tensor([0]), input_lens.cumsum(-1).int()))
-            PagedAttention.flash_attn_varlen_func(
-                attn_output,
-                query,
-                key_cache,
-                value_cache,
-                seq_len_tensor,
-                seq_len_tensor,
-                input_lens.max(),
-                input_lens.max(),
-                1.0 / math.sqrt(self.head_dim),
-                True,
-                past_key_value.block_tables,
-                None,
-            )
+            # prefill
+            attn_output = self.varlen_attn(query, key_cache, value_cache, past_key_value, input_lens)
         else:
             # decode
+            attn_output = torch.empty_like(query)
             PagedAttention.single_query_cached_kv_attention(
                 attn_output,
                 query,