NanoGPT Training Run Requirements | Generated by AI
Here’s a breakdown of what this nanoGPT training run will require based on your exact config and hardware (RTX 4070 12GB assumed from the run name) and log.
Tokens Processed
- Tokens per iteration → 524,288 (16 sequences × 1024 context × 32 grad_acc)
- max_iters → 20,000
- Total tokens seen → 524,288 × 20,000 = 10.486 billion tokens (~10.5B)
This is roughly 2.5–3× more than the original GPT-2 124M was trained on and very close to the 10B-token FineWeb-Edu subset many people use today to beat original GPT-2 performance.
Compute (FLOPs)
Your model has 40.56M parameters (a bit smaller than the usual 124M/125M GPT-2 because n_embd=384 instead of 768).
Rough transformer FLOPs estimate (6 × params × batch × seqlen per iteration, forward+backward):
- ≈ 2,550 PFLOPs total (2.55 × 10¹⁵ FLOPs)
That is normal for a decent run on a ~40–125M model to ~10–11B tokens.
Expected Wall-Clock Time on Your RTX 4070
The first iteration took ~32 seconds because PyTorch was compiling the model (normal, happens once).
After compilation, iteration times for a ~40–85M model on an RTX 4070 with torch.compile, flash-attention, and this batch size typically settle to 2.5 – 4.5 seconds per iteration (often ~3–3.5 s/iter once warmed up).
So for 20,000 iterations:
| Avg iter time (realistic) | Total training time | Approximate finish |
|---|---|---|
| 2.5 s/iter | ≈ 13.9 hours | ~14 hours |
| 3.0 s/iter | ≈ 16–17 hours | ~16–17 hours |
| 3.5 s/iter | ≈ 19–20 hours | ~20 hours |
| 4.0 s/iter | ≈ 22–23 hours | almost 1 full day |
Most people running similar 85M–125M models on a 4070/4070 Ti report 15–22 hours for ~10–11B tokens, so expect your run (slightly smaller model) to finish in 15–20 hours of continuous training.
GPU Memory (VRAM)
Your config fits very comfortably in 12 GB:
- Model ~40–45M params in fp16/bf16 ≈ 80–90 MB
- Activations + optimizer states + grad acc 32 → peak ≈ 9–11 GB
- You will see ~10–11.5 GB VRAM used → perfectly safe on a 4070/4070 Ti/4070 Super, no OOM.
Electricity / Cost (if you care)
RTX 4070 draws ≈ 180–220 W under full load here.
20 hours × 200 W average = ~4 kWh → roughly $0.50–$1.00 USD depending on your electricity price.
Summary – How much does this train “need”?
- Data — Your ~3.68B-token FineWeb subset will be seen ≈ 2.85× (10.5B / 3.68B)
- Compute — ~2.5 PFLOPs, 10.5 billion tokens
- Time on RTX 4070 — 15–20 hours (will finish sometime tomorrow morning/afternoon if you let it run overnight)
- VRAM — 10–11 GB peak → safe
- Power cost — negligible
Just let it run — after the first ~30–60 seconds the iterations will speed up dramatically and you’ll see the MFU climb to 20–35 % (typical for Ada GPUs on this model this size). You should end up with a very respectable small GPT-2-class model that already outperforms the original 124M GPT-2 on many benchmarks because you’re training on cleaned FineWeb-Edu data. Enjoy!