Understanding Model FLOPs Utilization (MFU) | Generated by AI
In the context of nanoGPT (a minimal implementation for training GPT models), mfu stands for Model FLOPs Utilization. It’s a key efficiency metric during training that measures how effectively the hardware (e.g., GPU) is being utilized, expressed as a percentage of the theoretical peak floating-point operations per second (FLOPs) achievable on that device.
Quick Breakdown:
- What it tracks: It compares the actual FLOPs your model performs per training iteration (based on factors like model size, batch size, and sequence length) against the hardware’s maximum possible FLOPs (e.g., for an NVIDIA A100 GPU in bfloat16 precision, this peak is around 312 TFLOPs).
- Why it’s useful: Higher MFU (e.g., 50-70% is good for large models) indicates better resource usage and faster training. Low values (like the ~10% in your logs) might suggest bottlenecks such as I/O, small batch sizes, or suboptimal hardware configs.
- How it’s calculated in nanoGPT: The code estimates it using the forward/backward pass FLOPs per iteration, divided by the hardware’s peak FLOPs and wall-clock time. You can find the implementation in
model.pyunderestimate_mfu().
For example, in your logs:
iter 3820: loss 0.8915, time 33.27ms, mfu 10.63%means the model used only ~10.63% of the GPU’s peak FLOPs capacity for that step, which is typical for smaller setups or early training.
If you’re tweaking nanoGPT for better MFU, try increasing batch size or using mixed precision.
References
- MFU calculation · Issue #322 · karpathy/nanoGPT
- Code Explanation: nanoGPT
- Using Model Flops Utilization (MFU)