diff --git a/vllm/attention/backends/habana_attn.py b/vllm/attention/backends/habana_attn.py
index fc7f74c1f96fb..f60370146b7f2 100644
--- a/vllm/attention/backends/habana_attn.py
+++ b/vllm/attention/backends/habana_attn.py
@@ -142,6 +142,13 @@ def forward(
         query = query.view(-1, self.num_heads, self.head_size)
         key = key.view(-1, self.num_kv_heads, self.head_size)
         value = value.view(-1, self.num_kv_heads, self.head_size)
+
+        if prefill_meta := attn_metadata.prefill_metadata:
+            block_indices = prefill_meta.block_indices
+            block_offsets = prefill_meta.block_offsets
+        if decode_meta := attn_metadata.decode_metadata:
+            block_indices = decode_meta.block_indices
+            block_offsets = decode_meta.block_offsets
         if kv_cache is not None:
             key_cache, value_cache = HabanaPagedAttention.split_kv_cache(
                 kv_cache, self.num_kv_heads, self.head_size)
@@ -149,9 +156,8 @@ def forward(
             # Reshape the input keys and values and store them in the cache.
             # If kv_cache is not provided, the new key and value tensors are
             # not cached. This happens during the initial memory profiling run.
-            block_indices, block_offset = cache_ops.prepare_to_cache(key_cache, attn_metadata.slot_mapping)
-            key_cache = self.key_cache(key, key_cache, block_indices, block_offset)
-            value_cache = self.value_cache(value, value_cache, block_indices, block_offset)
+            key_cache = self.key_cache(key, key_cache, block_indices, block_offsets)
+            value_cache = self.value_cache(value, value_cache, block_indices, block_offsets)
 
         if prefill_meta := attn_metadata.prefill_metadata:
             # Prompt run.
diff --git a/vllm/attention/ops/habana_paged_attn.py b/vllm/attention/ops/habana_paged_attn.py
index fa4dba165f3b3..051fcd8cca272 100644
--- a/vllm/attention/ops/habana_paged_attn.py
+++ b/vllm/attention/ops/habana_paged_attn.py
@@ -19,6 +19,8 @@ class HabanaPagedAttentionMetadata:
     block_list: Optional[torch.Tensor]
     block_mapping: Optional[torch.Tensor]
     block_usage: Optional[torch.Tensor]
+    block_indices: Optional[torch.Tensor]
+    block_offsets: Optional[torch.Tensor]
 
 
 class HabanaPagedAttention:
diff --git a/vllm/hpu/cache_ops.py b/vllm/hpu/cache_ops.py
index 4734333c99e49..40dcd8eae17bd 100644
--- a/vllm/hpu/cache_ops.py
+++ b/vllm/hpu/cache_ops.py
@@ -10,24 +10,6 @@
 import habana_frameworks.torch as htorch
 
 
-def reshape_and_cache(key, value, key_cache, value_cache, slot_mapping, dtype, is_prompt=False):
-    block_size = key_cache.size(1)
-    slot_mapping = slot_mapping.flatten()
-    indices = torch.div(slot_mapping, block_size, rounding_mode="floor")
-    offsets = torch.fmod(slot_mapping, block_size)
-    key_cache.index_put_((indices, offsets), key)
-    value_cache.index_put_((indices, offsets), value)
-
-
-def prepare_to_cache(cache, slot_mapping):
-    block_size = cache.size(1)
-    slot_mapping = slot_mapping.flatten()
-    indices = torch.div(slot_mapping, block_size, rounding_mode="floor")
-    offsets = torch.fmod(slot_mapping, block_size)
-
-    return indices, offsets
-
-
 def insert_or_update_cache(input, cache, block_indices, block_offsets):
     cache.index_put_((block_indices, block_offsets), input)
 
diff --git a/vllm/worker/habana_model_runner.py b/vllm/worker/habana_model_runner.py
index 696736e96511b..8316fed55e7d3 100644
--- a/vllm/worker/habana_model_runner.py
+++ b/vllm/worker/habana_model_runner.py
@@ -200,6 +200,13 @@ def pad_list(l, k, v):
     return l + [v] * padding
 
 
+def precompute_indices_and_offsets(block_size, slot_mapping):
+    slot_mapping = slot_mapping.flatten()
+    indices = torch.div(slot_mapping, block_size, rounding_mode="floor")
+    offsets = torch.fmod(slot_mapping, block_size)
+    return indices, offsets
+
+
 class HpuModelAdapter():
     def __init__(self, model, block_size):
         self.model = model
@@ -595,10 +602,14 @@ def _prepare_prompt(
                                        dtype=torch.long,
                                        device=self.device)
 
+        block_indices, block_offsets = precompute_indices_and_offsets(self.block_size, slot_mapping)
+
         attn_metadata = self.attn_backend.make_metadata(
             block_list=None,
             block_mapping=None,
             block_usage=None,
+            block_indices=block_indices,
+            block_offsets=block_offsets,
             attn_bias=None,
             seq_lens_tensor=seq_lens_tensor,
         )
@@ -698,10 +709,14 @@ def _prepare_decode(
                                     dtype=torch.long,
                                     device=self.device)
 
+        block_indices, block_offsets = precompute_indices_and_offsets(self.block_size, slot_mapping)
+
         attn_metadata = self.attn_backend.make_metadata(
             block_list=block_list,
             block_mapping=block_mapping,
             block_usage=block_usage,
+            block_indices=block_indices,
+            block_offsets=block_offsets,
             attn_bias=None,
             seq_lens_tensor=None,
         )
@@ -886,10 +901,10 @@ def _seq_len(self, attn_metadata):
     def trim_attn_metadata(self, metadata: AttentionMetadata) -> object:
         prefill_metadata = subtuple(metadata.prefill_metadata,
                                     "TrimmedPrefillMetadata",
-                                    ['attn_bias', 'seq_lens_tensor'])
+                                    ['attn_bias', 'seq_lens_tensor', 'block_indices', 'block_offsets'])
         decode_metadata = subtuple(metadata.decode_metadata,
                                     "TrimmedDecodeMetadata",
-                                    ['attn_bias', 'block_list', 'block_mapping', 'block_usage'])
+                                    ['attn_bias', 'block_list', 'block_mapping', 'block_usage', 'block_indices', 'block_offsets'])
         return subtuple(metadata,
                         'TrimmedMetadata',
                         ['slot_mapping',