diff --git a/scripts/cal_mfu.py b/scripts/cal_mfu.py
new file mode 100644
index 00000000..2f408497
--- /dev/null
+++ b/scripts/cal_mfu.py
@@ -0,0 +1,126 @@
+# coding=utf-8
+# Copyright 2024 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License
+
+import torch
+from transformers import AutoConfig
+import fire
+def model_flops_counter(
+    batch_size: int,
+    seqlen: int,
+    model_config: dict,
+    is_backward: bool = True,
+    is_recompute: bool = False,
+    is_flashattn: bool = False,
+) -> float:
+    """
+    calculate the FLOPs of model per iteration
+    """
+    hidden_size = model_config.hidden_size
+    num_attention_heads = model_config.num_attention_heads
+    num_key_value_heads = model_config.num_key_value_heads
+    vocab_size = model_config.vocab_size
+    intermediate_size = model_config.intermediate_size
+    num_hidden_layers = model_config.num_hidden_layers
+    """
+    B: batch_size
+    S: seqlen
+    L: num_hidden_layers
+    H: hidden_size
+    V: vocab_size
+    I: intermediate_size
+    """
+    ### MLP calculation
+    per_mlp_calculation = 2 * hidden_size * intermediate_size
+    mlp_calculation_per_layer = per_mlp_calculation * 3
+    mlp_calculation = batch_size * seqlen * mlp_calculation_per_layer * num_hidden_layers
+
+    ### Attention calculation
+    Q_calculation = 2 * hidden_size * hidden_size
+    O_calculation = 2 * hidden_size * hidden_size
+    K_calculation = 2 * hidden_size * hidden_size * num_key_value_heads / num_attention_heads
+    V_calculation = 2 * hidden_size * hidden_size * num_key_value_heads / num_attention_heads
+    
+    QKVO_calculation = Q_calculation + O_calculation + K_calculation + V_calculation # 8H^2 / coe
+    self_attn_calculation = seqlen * hidden_size * 2 * 2  # (4 * S * H)
+    attention_calculation = batch_size * seqlen * num_hidden_layers * (QKVO_calculation + self_attn_calculation) # BSL(8H^2/coe + 4S * H)
+    
+    #Embedding and LMhead calculation
+    embedding_calculation = hidden_size * vocab_size
+    lmhead_calculation = hidden_size * vocab_size    
+    IO_calculation = 3 * batch_size * seqlen * (embedding_calculation + lmhead_calculation) # 2 *(1+2)BSHV    
+    E = attention_calculation + mlp_calculation
+    coefficient = 3
+    fix_term = 0
+    if(is_recompute):
+        coefficient = 4
+    if(is_flashattn):
+        fix_term = batch_size *seqlen * self_attn_calculation
+    
+    total_calculation = coefficient * E + IO_calculation + fix_term
+    
+    return total_calculation
+
+
+def hardware_flops_counter(
+    seconds: float, # seconds used in given iterations
+    num_gpus: int = 1,
+) -> float:
+    if "A100" in torch.cuda.get_device_name():
+        return 312 * 1e12 * seconds * num_gpus
+    elif "V100" in torch.cuda.get_device_name():
+        return 125 * 1e12 * seconds * num_gpus
+
+def compute_mfu(
+    batch_size: int,
+    seqlen: int,
+    model_config: dict,
+    num_iter: int,
+    seconds: float,
+    num_gpus: int = 1,
+) -> float:
+    """
+    compute MFU given model configuration, training config and training information
+    """
+    percentage = (num_iter * model_flops_counter(batch_size,seqlen,model_config)) / hardware_flops_counter(seconds, num_gpus)
+    
+    print(f"MFU : {percentage* 100:.2f}%")
+    return percentage
+    
+# User input
+
+### model_name
+model_name = "meta-llama/Meta-Llama-3-8B-Instruct"
+
+### training config
+batch_size = 8
+seqlen = 1*1024
+num_gpus = 1
+
+### training information
+num_iter = 225
+seconds = 605 # time used in {num_iter} iterations
+
+model_config = AutoConfig.from_pretrained(model_name)
+if __name__ == "__main__":
+    fire.Fire( 
+        compute_mfu(
+            batch_size=batch_size,
+            seqlen=seqlen,
+            model_config=model_config,
+            num_iter=num_iter,
+            seconds=seconds,
+            num_gpus=num_gpus
+        )
+    )