diff --git a/vllm/attention/layer.py b/vllm/attention/layer.py
index 237802afccde9..fc1d61962ae17 100644
--- a/vllm/attention/layer.py
+++ b/vllm/attention/layer.py
@@ -418,35 +418,39 @@ class MultiHeadAttention(nn.Module):
 
 
 def wait_for_kv_layer_from_connector(layer_name: str):
-    if not has_kv_transfer_group() or not is_v1_kv_transfer_group():
-        return
+    print("hi --- wait_for_kv_layer_from_connector")
+    pass
+    # if not has_kv_transfer_group() or not is_v1_kv_transfer_group():
+    #     return
 
-    connector = get_kv_transfer_group()
+    # connector = get_kv_transfer_group()
 
-    forward_context: ForwardContext = get_forward_context()
-    attn_metadata = forward_context.attn_metadata
-    if attn_metadata is None:
-        return
-    assert isinstance(attn_metadata, dict)
-    connector.wait_for_layer_load(layer_name)
+    # forward_context: ForwardContext = get_forward_context()
+    # attn_metadata = forward_context.attn_metadata
+    # if attn_metadata is None:
+    #     return
+    # assert isinstance(attn_metadata, dict)
+    # connector.wait_for_layer_load(layer_name)
 
 
 def maybe_save_kv_layer_to_connector(
     layer_name: str,
     kv_cache_layer: List[torch.Tensor],
 ):
-    if not has_kv_transfer_group() or not is_v1_kv_transfer_group():
-        return
+    print("hi --- maybe_save_kv_layer_to_connector")
+    pass
+    # if not has_kv_transfer_group() or not is_v1_kv_transfer_group():
+    #     return
 
-    connector = get_kv_transfer_group()
+    # connector = get_kv_transfer_group()
 
-    forward_context: ForwardContext = get_forward_context()
-    attn_metadata = forward_context.attn_metadata
-    if attn_metadata is None:
-        return
-    assert isinstance(attn_metadata, dict)
-    connector.save_kv_layer(layer_name, kv_cache_layer,
-                            attn_metadata[layer_name])
+    # forward_context: ForwardContext = get_forward_context()
+    # attn_metadata = forward_context.attn_metadata
+    # if attn_metadata is None:
+    #     return
+    # assert isinstance(attn_metadata, dict)
+    # connector.save_kv_layer(layer_name, kv_cache_layer,
+    #                         attn_metadata[layer_name])
 
 
 def unified_attention(
@@ -497,7 +501,7 @@ def unified_attention_with_output(
     output_scale: Optional[torch.Tensor] = None,
     output_block_scale: Optional[torch.Tensor] = None,
 ) -> None:
-    wait_for_kv_layer_from_connector(layer_name)
+    # wait_for_kv_layer_from_connector(layer_name)
     forward_context: ForwardContext = get_forward_context()
     attn_metadata = forward_context.attn_metadata
     if isinstance(attn_metadata, dict):
@@ -514,7 +518,7 @@ def unified_attention_with_output(
                       output_scale=output_scale,
                       output_block_scale=output_block_scale)
 
-    maybe_save_kv_layer_to_connector(layer_name, kv_cache)
+    # maybe_save_kv_layer_to_connector(layer_name, kv_cache)
 
 
 def unified_attention_with_output_fake(