diff --git a/optimum/habana/transformers/models/mistral/modeling_mistral.py b/optimum/habana/transformers/models/mistral/modeling_mistral.py
index 6075524b5f..f7d28b2fe0 100644
--- a/optimum/habana/transformers/models/mistral/modeling_mistral.py
+++ b/optimum/habana/transformers/models/mistral/modeling_mistral.py
@@ -19,6 +19,7 @@
 # limitations under the License.
 """PyTorch Mistral model."""
 
+import os
 import math
 from typing import List, Optional, Tuple, Union
 
@@ -47,7 +48,6 @@
     _gaudi_prepare_4d_causal_attention_mask,
 )
 
-
 try:
     from habana_frameworks.torch.hpex.kernels import RotaryPosEmbeddingHelperV2 as FusedRoPE
 
@@ -68,6 +68,15 @@
     print("Not using HPU fused scaled dot-product attention kernel.")
     FusedSDPA = None
 
+#  FusedScaledDotProductAttention
+class ModuleFusedSDPA(torch.nn.Module):
+    def __init__(self, fusedSDPA):
+        super().__init__()
+        self._hpu_kernel_fsdpa = fusedSDPA
+
+    def forward(self, query, key, value, attn_mask, dropout_p, is_casual, scale):
+        return  self._hpu_kernel_fsdpa.apply(query, key, value, attn_mask, dropout_p, is_casual, scale)
+
 class Matmul(torch.nn.Module):
     def __init__(self):
         super().__init__()
@@ -113,16 +122,6 @@ def gaudi_mistral_repeat_kv(
 
     return query_states, key_states, value_states, attention_mask
 
-
-def update_sincos_cache(self, seq_len):
-    # Call rotary emb forward() to update cos/sin cache when infering more than self.max_position_embeddings
-    # This helps in avoiding creation of these caches during actual model forward pass and
-    # reduce memory consumption and improve performance.
-    if seq_len > self.max_position_embeddings:
-        self.max_position_embeddings = seq_len
-        _, _ = self.rotary_emb(self.k_proj.weight, seq_len=seq_len)
-
-
 def gaudi_mistral_rmsnorm_forward(self, hidden_states):
     """
     Copied from MistralRMSNorm.forward: https://github.com/huggingface/transformers/blob/main/src/transformers/models/mistral/modeling_mistral.py
@@ -153,6 +152,7 @@ def __init__(self, config: MistralConfig, layer_idx: Optional[int] = None):
         self.v_cache = KVCache()
         self.matmul_qk = Matmul()
         self.matmul_av = Matmul()
+        self.fused_scaled_dot_product_attention = ModuleFusedSDPA(FusedSDPA) if FusedSDPA else None
         self.inp_seq_len = -1
         self.norm_factor = 1.0 / math.sqrt(self.head_dim)
 
@@ -177,6 +177,14 @@ def reorder_kv_cache(self, beam_idx: torch.LongTensor):
         self.reorder(self.v_cache.cache, beam_idx, seq_length, head_dim)
         return (self.k_cache.cache.shape, self.v_cache.cache.shape)
 
+    def update_sincos_cache(self, seq_len):
+        # Call rotary emb forward() to update cos/sin cache when infering more than self.max_position_embeddings
+        # This helps in avoiding creation of these caches during actual model forward pass and
+        # reduce memory consumption and improve performance.
+        if seq_len > self.max_position_embeddings:
+            self.max_position_embeddings = seq_len
+            _, _ = self.rotary_emb(self.k_proj.weight, seq_len=seq_len)
+
     def forward(
         self,
         hidden_states: torch.Tensor,
@@ -263,8 +271,9 @@ def forward(
             import habana_frameworks.torch.hpu as ht
             if q_len == 1:
                 # next token
-                with ht.sdp_kernel(enable_recompute=False):
-                    attn_output = FusedSDPA.apply(
+                use_recompute = True if os.getenv("QUANT_CONFIG", "") else False
+                with ht.sdp_kernel(enable_recompute=use_recompute):#False):
+                    attn_output = self.fused_scaled_dot_product_attention(
                         query_states, key_states, value_states, attention_mask, 0.0, False, None
                     )
             else:
@@ -272,10 +281,10 @@ def forward(
                 if flash_attention_causal_mask:
                     # causal masking on first token requires inputs to be of the same length
                     with ht.sdp_kernel(enable_recompute=flash_attention_recompute):
-                        attn_output = FusedSDPA.apply(query_states, key_states, value_states, None, 0.0, True, None)
+                        attn_output = self.fused_scaled_dot_product_attention(query_states, key_states, value_states, None, 0.0, True, None)
                 else:
                     with ht.sdp_kernel(enable_recompute=flash_attention_recompute):
-                        attn_output = FusedSDPA.apply(
+                        attn_output = self.fused_scaled_dot_product_attention(
                             query_states, key_states, value_states, attention_mask, 0.0, False, None
                         )
         else:
@@ -421,6 +430,10 @@ def allocate_kv_cache(self, batch_size, max_seq_len, inp_seq_len):
     def reorder_kv_cache(self, beam_idx: torch.LongTensor):
         return tuple(layer.reorder_kv_cache(beam_idx) for layer in self.layers)
 
+    def update_sincos_cache(self, seq_len):
+        for layer in self.layers:
+            layer.update_sincos_cache(seq_len)
+
     def forward(
         self,
         input_ids: torch.LongTensor = None,
@@ -535,6 +548,7 @@ def forward(
                 and (torch.distributed.is_initialized() is False or torch.distributed.get_world_size() == 1)
             ):
                 htcore.mark_step()
+
             if output_hidden_states:
                 all_hidden_states += (hidden_states,)