diff --git a/colossalai/inference/modeling/models/nopadding_llama.py b/colossalai/inference/modeling/models/nopadding_llama.py
index 108b79174..29760f564 100644
--- a/colossalai/inference/modeling/models/nopadding_llama.py
+++ b/colossalai/inference/modeling/models/nopadding_llama.py
@@ -84,6 +84,7 @@ def llama_model_forward(
     sequence_lengths = inputmetadata.sequence_lengths
     batch_size = inputmetadata.batch_size
     kv_seq_len = inputmetadata.kv_seq_len
+    # use_cuda_kernel = False
     use_cuda_kernel = True
     # NOTE: After testing, the performance of this configuration is relatively good. With updates
     # and optimizations to the CUDA kernel implementation, a more detailed analysis of this configuration's
@@ -97,7 +98,7 @@ def llama_model_forward(
 
     sm_scale = 1.0 / (inputmetadata.head_dim**0.5)
 
-    norm_output = None
+    norm_output = torch.empty_like(hidden_states)
     residual = None
 
     for layer_id, decoder_layer in enumerate(self.layers):
@@ -122,10 +123,9 @@ def llama_model_forward(
         last_token_indexs = sequence_lengths.cumsum(dim=-1)
         hidden_states = hidden_states[last_token_indexs - 1].contiguous()
         residual = residual[last_token_indexs - 1].contiguous()
-        norm_output = torch.empty_like(hidden_states)  # NOTE non-functional, but cuda graph only capture decoding only
+        norm_output = torch.empty_like(hidden_states)
     hidden_states, _ = self.norm(hidden_states, norm_output, residual, use_cuda_kernel)
 
-
     return hidden_states
 
 
@@ -198,7 +198,8 @@ def llama_rmsnorm_forward(
     residual: torch.Tensor = None,
     use_cuda_kernel: bool = True,
 ):
-    if use_cuda_kernel:
+    # if use_cuda_kernel:
+    if False:
         if residual is not None:
             inference_ops.fused_add_rms_layernorm(hidden_states, residual, self.weight.data, self.variance_epsilon)
             return hidden_states, residual
@@ -338,7 +339,8 @@ class NopadLlamaAttention(LlamaAttention):
                 sm_scale=sm_scale,
             )
         else:
-            if use_cuda_kernel:
+            # if use_cuda_kernel:
+            if False:
                 inference_ops.rotary_embedding_and_cache_copy(
                     query_states,
                     key_states,