Chinchilla Training Token Guidelines | Generated by AI
Question: For a ~200M parameter custom GPT-2 model, using the Chinchilla 20× tokens-per-parameter rule, do we need 4 billion tokens to train it optimally?
Answer:
Yes, your math is exactly right.
The Chinchilla Scaling Law
The Chinchilla paper (Hoffmann et al., 2022) found the optimal compute ratio is roughly:
tokens = 20 × parameters
So for a 200M model:
20 × 200,000,000 = 4,000,000,000 = 4 billion tokens
That is correct.
Putting It in Context
| Model | Params | Chinchilla-optimal tokens |
|---|---|---|
| GPT-2 small | 124M | ~2.5B tokens |
| Custom middle | 200M | ~4B tokens |
| GPT-2 medium | 350M | ~7B tokens |
Note: OpenAI’s original GPT-2 was trained on ~40B tokens for all sizes — that was actually over-trained by Chinchilla standards, which is why modern reproductions (like Karpathy’s llm.c) use 10B tokens for 124M and 30B tokens for 350M — those are more than Chinchilla-optimal, intentionally, because a slightly over-trained smaller model is often more useful at inference time (better loss per parameter at serving cost).
What 4B Tokens Means Practically
Dataset: FineWeb or FineWeb-Edu (100B tokens total) — you only need 4% of it. This is very manageable.
Training steps at 0.5M tokens/step:
4,000,000,000 / 500,000 = 8,000 steps
Compare to 124M nanoGPT which runs ~19,073 steps to see 10B tokens. At Chinchilla-optimal (2.5B tokens), that’s only ~5,000 steps for 124M.
Wall-clock on RTX 4070:
If your 200M model does roughly ~0.5–0.8 sec/step at batch_size=2, grad_accum=240:
8,000 steps × ~0.7 sec = ~5,600 sec ≈ 1.5 hours
Wait — that seems shockingly fast. The catch: Chinchilla-optimal is compute-optimal, not loss-optimal. It tells you the best model to train for a fixed compute budget, not the lowest possible loss. To reach a good val loss (~2.9x), you will want to train longer — closer to 10–30B tokens — just like Karpathy does.
Summary
| Question | Answer |
|---|---|
| Is 4B tokens the Chinchilla-optimal amount? | Yes, exactly |
| Is 4B tokens enough to get a good model? | Barely — you’d want 10B+ for quality |
| Is 4B tokens feasible on a 4070? | Yes, very manageable |
| Does more tokens beyond 4B hurt? | No — over-training improves inference-time efficiency |
So 4B tokens is your minimum target. Aim for 10B tokens (what Karpathy uses for 124M) as a practical baseline — at 200M that is 2× over-trained by Chinchilla, which is perfectly fine and gives a noticeably better model.