NanoGPT Training Iteration Time Explained | Generated by AI
Yes — in NanoGPT’s training logs, that “time” value is the wall-clock duration for a single training iteration.
Here’s what that timing represents:
What is measured in that number?
It’s the actual elapsed time needed to finish one optimizer step, which includes:
- forward pass
- backward pass
- gradient update
- any fused kernels
- dataloader overhead (if any)
- logging overhead (minimal)
So in your log:
iter 200: loss 6.6062, time 3838.34ms, mfu 12.67%
This means:
- Each training step at this point takes 3.838 seconds per iteration.
- MFU shows how fully your GPU’s flops are being used.
Why can one step be several seconds?
Common reasons:
- Large context length
- Small GPU batch size leading to inefficient launch patterns
- Model too large for the GPU, triggering more memory operations
- Slow CPU-side dataloader
- Using a single parquet file or non-sharded dataset
- Gradient checkpointing (adds overhead)
If you want, tell me your:
- model size
- block size
- batch size
- GPU model
- dataset setup
Then I can estimate whether ~3.8 s/iter is expected or if something’s misconfigured.