diff --git a/vllm/distributed/kv_transfer/kv_connector/mooncake_store_connector.py b/vllm/distributed/kv_transfer/kv_connector/mooncake_store_connector.py
index 67681dff8f6..e09ee954f2e 100644
--- a/vllm/distributed/kv_transfer/kv_connector/mooncake_store_connector.py
+++ b/vllm/distributed/kv_transfer/kv_connector/mooncake_store_connector.py
@@ -330,9 +330,9 @@ def recv_kv_caches_and_hidden_states_hpu(
             block_indices_tensor = torch.tensor(block_indices_list[start_block_idx:end_block_idx], device="hpu", dtype=torch.int32 )
             # we think this is a padding sequence, so we skip it. but we still need write kv cache
             if slen == 1:
-                for i in range(model_executable.model.model.start_layer,
-                               model_executable.model.model.end_layer):
-                    current_layer_idx = i - model_executable.model.model.start_layer
+                for i in range(model_executable.model.start_layer,
+                               model_executable.model.end_layer):
+                    current_layer_idx = i - model_executable.model.start_layer
                     kv_cache = kv_caches[current_layer_idx]
                     # key_cache, value_cache = kv_cache[0], kv_cache[1]
                     key_cache = kv_cache[0]
@@ -371,8 +371,9 @@ def recv_kv_caches_and_hidden_states_hpu(
             num_computed_tokens_list.append(num_computed_tokens)
             
             # it's padded to block size now.
-            key_values = remote_kv.to("hpu")
-            keys = key_values
+            # key_values = remote_kv.to("hpu")
+            # TEST: use CPU kv cache directly
+            keys = remote_kv
             # values = key_values[..., self.k_head_size:]
 
             htorch.core.mark_step()