nanoGPT Training Time Estimate

nanoGPT Training Time Estimate | Generated by AI

Home 2026.04

Question: Based on the nanoGPT training log shown (RTX 4070, 124M parameters, FineWeb dataset), how much total training time is estimated and when will it finish?

Answer:

📊 Training Log Analysis

Key Metrics Extracted from Your Log

Metric	Value
Model parameters	123.59M
Tokens per iteration	524,288
MFU (Model FLOP Utilization)	~15.41%
Time per iter (normal)	~9,330 ms
Time per eval step	~29,164 ms
Current progress	iter 1800 / ~5000–6000 est.
Current val loss (step 1500)	4.0096

⏱️ Time Per Iteration Breakdown

Normal training iter: ~9.33 seconds each
Eval/checkpoint iters (every 500 steps): ~29 seconds each
Eval frequency: every 500 steps → adds ~20 extra seconds every 500 iters

🔢 Estimating Total Steps Needed

The standard nanoGPT FineWeb training run typically targets around ~5,000 steps (sometimes up to 6,000) to reach a stable validation loss in the 3.8–4.0 range with a stock nanoGPT config. However, your current val loss at step 1500 is still 4.0096, which is relatively high — meaning you likely need at minimum 5,000–7,000 steps total depending on your config.

Your MFU of 15.41% is reasonable for an RTX 4070 — the 4070 has significantly less memory bandwidth and compute compared to a 4090 (~40% less tensor core throughput), which explains the slower speed vs. reference runs.

📐 Time Estimate Calculation

Assuming ~5,000 total steps (conservative target):

Remaining steps from iter 1800 = 5000 - 1800 = 3,200 steps
Normal iters: 3,200 × 9.33s = ~29,856s ≈ 8.3 hours
Eval overhead (every 500): ~6–7 evals × 29s = ~3.5 min (negligible)

Total remaining ≈ ~8.3 hours

Assuming ~6,000 total steps (safer target for better loss):

Remaining = 6000 - 1800 = 4,200 steps
4,200 × 9.33s = ~39,186s ≈ ~10.9 hours

🕐 Summary Estimate

Scenario	Remaining Time	Total Run Time
5,000 steps target	~8.3 hours	~12–13 hours total
6,000 steps target	~10.9 hours	~15–16 hours total
7,000 steps target	~13.5 hours	~18–19 hours total

The run has already consumed roughly ~4–5 hours (1800 iters × 9.33s ≈ ~4.7h), consistent with the log timestamps.

💡 RTX 4070 Context

For reference, a single RTX 4090 achieves ~160k tokens/sec throughput and can reach 3.25 validation loss in about 90 minutes on an optimized speedrun setup. Your RTX 4070 at ~15.4% MFU is running the stock nanoGPT (not the optimized speedrun version), which is significantly slower — but perfectly normal for a standard training run.

✅ Recommendation

If your config sets max_iters = 5000, expect ~8 more hours.
If max_iters = 6000, expect ~11 more hours.
Check your train.py for the max_iters variable to get the exact target — that’s the single most important number for your ETA.
The loss curve looks healthy and normal — it’s converging well.

References:

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