diff --git a/python/llm/src/ipex_llm/transformers/models/mistral.py b/python/llm/src/ipex_llm/transformers/models/mistral.py
index 983f2508..4534f735 100644
--- a/python/llm/src/ipex_llm/transformers/models/mistral.py
+++ b/python/llm/src/ipex_llm/transformers/models/mistral.py
@@ -37,6 +37,7 @@
 
 from typing import Optional, Tuple, Union, List
 
+import os
 import torch
 from transformers.cache_utils import Cache
 from transformers.modeling_outputs import BaseModelOutputWithPast
@@ -45,8 +46,11 @@ from transformers.models.mistral.modeling_mistral import MistralModel, MistralAt
 from ipex_llm.transformers.models.common import merge_qkv_base
 from ipex_llm.transformers.models.common import scaled_dot_product_attention
 from ipex_llm.transformers.models.utils import should_use_fuse_rope, apply_rotary_pos_emb
+from ipex_llm.transformers.models.utils import should_use_compresskv, is_enough_kv_cache_room_4_36
 from ipex_llm.transformers.models.utils import use_quantize_kv_cache
 from ipex_llm.transformers.kv import DynamicFp8Cache, DynamicNormalCache
+from ipex_llm.transformers.kv import DynamicCompressCache, DynamicCompressFp8Cache
+KV_CACHE_ALLOC_BLOCK_LENGTH = int(os.environ.get("KV_CACHE_ALLOC_BLOCK_LENGTH", 256))
 
 
 def mistral_model_forward(
@@ -69,11 +73,22 @@ def mistral_model_forward(
     use_quantize_kv = use_quantize_kv_cache(self.layers[0].mlp.down_proj, inputs,
                                             self.config.num_attention_heads //
                                             self.config.num_key_value_heads)
+    use_compress_kv = should_use_compresskv(inputs, inputs.size(1)) or \
+        isinstance(past_key_values, DynamicCompressCache)
 
     if use_cache:
-        if use_quantize_kv and not isinstance(past_key_values, DynamicFp8Cache):
+        if use_compress_kv and not isinstance(past_key_values, DynamicCompressCache):
+            if use_quantize_kv:
+                past_key_values = DynamicCompressFp8Cache.from_legacy_cache(past_key_values)
+            else:
+                past_key_values = DynamicCompressCache.from_legacy_cache(past_key_values)
+        elif use_quantize_kv and not isinstance(past_key_values, DynamicFp8Cache):
             past_key_values = DynamicFp8Cache.from_legacy_cache(past_key_values)
-        elif not use_quantize_kv and not isinstance(past_key_values, DynamicNormalCache):
+        elif (
+            not use_quantize_kv
+            and not use_compress_kv
+            and not isinstance(past_key_values, DynamicNormalCache)
+        ):
             past_key_values = DynamicNormalCache.from_legacy_cache(past_key_values)
     # ipex-llm changes end
 
@@ -127,8 +142,16 @@ def mistral_attention_forward(
         query_states, key_states = apply_rotary_pos_emb(query_states, key_states,
                                                         cos, sin, position_ids, "mistral")
 
-    key_states, value_states = past_key_value.update(key_states, value_states,
-                                                     self.layer_idx, None)
+    if isinstance(past_key_value, DynamicCompressCache):
+        enough_kv_room = is_enough_kv_cache_room_4_36(past_key_value, self.layer_idx, q_len)
+        key_states, value_states = past_key_value.update(
+            key_states, value_states, self.layer_idx,
+            query_states, attention_mask, self.num_key_value_groups,
+            self.config, enough_kv_room, KV_CACHE_ALLOC_BLOCK_LENGTH
+        )
+    else:
+        key_states, value_states = past_key_value.update(key_states, value_states,
+                                                         self.layer_idx, None)
 
     # IPEX-LLM OPT: sdpa
     attn_weights = None
diff --git a/python/llm/src/ipex_llm/transformers/models/mixtral.py b/python/llm/src/ipex_llm/transformers/models/mixtral.py
index b63772a8..25083827 100644
--- a/python/llm/src/ipex_llm/transformers/models/mixtral.py
+++ b/python/llm/src/ipex_llm/transformers/models/mixtral.py
@@ -52,7 +52,7 @@ from ipex_llm.ggml.quantize import ggml_tensor_qtype
 from ipex_llm.utils.common import invalidInputError
 from ipex_llm.transformers.models.utils import init_kv_cache, extend_kv_cache, append_kv_cache
 from ipex_llm.transformers.models.utils import apply_rotary_pos_emb, is_enough_kv_cache_room_4_36
-from ipex_llm.transformers.models.mistral import should_use_fuse_rope
+from ipex_llm.transformers.models.utils import should_use_fuse_rope
 from ipex_llm.transformers.models.utils import use_decoding_fast_path
 from ipex_llm.transformers.models.utils import use_flash_attention, use_sdp
 from ipex_llm.transformers.models.utils import mlp_fusion_check, SILU
@@ -171,7 +171,7 @@ def mixtral_attention_forward(
     # for flash attention
     original_dtype = hidden_states.dtype
 
-    use_fuse_rope = should_use_fuse_rope(self, hidden_states, position_ids)
+    use_fuse_rope = should_use_fuse_rope(hidden_states, position_ids, self.training)
     enough_kv_room = is_enough_kv_cache_room_4_36(past_key_value, self.layer_idx)
     decoding_fast_path = use_decoding_fast_path(self.q_proj,
                                                 use_fuse_rope,