Understanding Slow Training Iterations | Generated by AI
Those big jumps are completely normal. They aren’t “slow iterations” — they are evaluation + checkpoint iterations, which always take much longer than the normal training steps.
Here’s what’s happening in your log:
1. Normal iterations
These are the lines like:
iter 100: time 168ms
iter 200: time 170ms
...
iter 900: time 168ms
Those are pure forward+backward passes on a single training batch. They stay in a stable range (~160–180 ms), which matches your GPU capability.
2. The “slow” ones
Examples:
iter 0: time 12543ms
iter 500: time 5985ms
iter 1000: time 5726ms
These lines follow exactly after the “step X” blocks where NanoGPT does:
- full validation loss evaluation
- full training loss evaluation
- saving a checkpoint to disk (which is often the slowest part, depending on SSD speed)
- sometimes GPU syncs / CPU-GPU sync latency before logging
That entire sequence happens every eval_interval or log_interval depending on config. In your output, you can see the pattern:
step 500: train loss..., val loss...
saving checkpoint
iter 500: loss..., time 5985ms
and
step 1000: train loss..., val loss...
saving checkpoint
iter 1000: loss..., time 5726ms
So iter 1000 time isn’t the training step’s compute time — it’s the combined cost of evaluation + checkpoint writing + the actual training step.
Why iter 0 is even larger (~12 seconds)?
The very first step includes:
- full graph compilation/warmup (PyTorch/CUDA kernels)
- loading initial data into GPU
- initial evaluation
- first checkpoint write (if configured)
That’s why it’s extra huge.
Summary
Your “slow iterations” are not real training steps. They only happen at evaluation+checkpoint boundaries.
Normal iterations (those that matter for throughput) are ~170ms and stable.
If you want fewer slow spikes, you can reduce:
eval_intervalalways_save_checkpoint- or checkpoint frequency
Let me know if you want recommended settings for faster overall training on 4070/4080/4090.