Nanochat 760M Training on RTX 4070 | Generated by AI

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Question: Using nanochat’s 760M model, how long would an RTX 4070 need to train on 7.6B or 14B tokens?

Answer:

Let’s estimate from nanochat’s actual numbers. The nanochat ~560M parameter model (close to your “760M nanochat”) uses about:

Your targets:

7.6B tokens:
3.5e9 × 7.6e9
≈ 2.66e19 FLOPs

14B tokens:
3.5e9 × 14e9
≈ 4.9e19 FLOPs

Now RTX 4070:

Assume:

effective training speed:
29 TFLOPS × 0.3
≈ 8.7 TFLOPS

Time:

7.6B tokens

2.66e19 / 8.7e12
= 3.06e6 seconds

≈ 850 hours

≈ 35 days

14B tokens

4.9e19 / 8.7e12
= 5.63e6 seconds

≈ 1560 hours

≈ 65 days

So realistic RTX 4070 estimate:

Tokens RTX 4070 training time
7.6B ~1 month
14B ~2 months

(Zhiwei Li) also measured a similar nanochat d32 experiment on RTX 4070 and found that larger runs become many-week scale because of VRAM and throughput constraints.

But there is a very important detail:

The 760M nanochat model itself is around the limit of a 12GB RTX 4070. The optimizer states are expensive:

Parameters:
760M × 2 bytes (BF16 weights)
≈ 1.5 GB

Gradients:
≈ 1.5 GB

Adam states:
m + v:
760M × 8 bytes
≈ 6 GB

Total:
≈ 9 GB+

plus activations + CUDA overhead

So you need:

Otherwise OOM.

The funny thing is: your earlier intuition is correct. For a 760M model, RTX 4070 is actually a very reasonable “AI scientist machine”.

A month-long run is not crazy:

RTX 4070:
7.6B tokens
≈ GPT-2-small scale experiment

H200:
same run
≈ hours

The difference is not that the 4070 cannot do it. It is that H200 lets you iterate 100× faster.

For learning architecture, tokenizer, data mixing, SFT recipes:

4070 + nanochat = excellent

For trying 100 ablations overnight:

H200 cluster = necessary

nanochat was specifically designed around this idea: a single GPU can train meaningful GPT-2-class models, just with longer wall-clock time. (GitHub)

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