Training nanochat d32 on RTX 4070

Training nanochat d32 on RTX 4070 | Generated by AI

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The nanochat d32 model (1.9B parameters, same as the one on nanochat.karpathy.ai) was trained on 38 billion tokens on an 8×H100 node in ~33 hours.

A single RTX 4070 (desktop version, 12 GB VRAM) is much slower than one H100, and you’ll also be limited by VRAM, so you can’t run the original batch size / sequence length without heavy quantization or gradient checkpointing.

Here are realistic estimates for training the same d32 model (1.9B, 38B tokens) on one RTX 4070:

Setup on 4070 (12 GB)	Approx. TFLOPs (effective)	Estimated total training time	Notes
FP16 / BF16 (native, no quant)	~25–30 TFLOPs	Impossible	Needs ~24–28 GB VRAM → OOM
4-bit quant (QLoRA-style or GPTQ) + gradient checkpointing	~18–22 TFLOPs	~35–45 days (~850–1,100 hours)	Most realistic for 12 GB card
3-bit or NF4 + aggressive optimizer (AdamW-8bit)	~20–24 TFLOPs	~30–38 days (~750–900 hours)	Slightly faster, still ~1 month
2.5–2.7-bit experimental quant (very recent bitsandbytes)	~22–26 TFLOPs	~28–35 days	Cutting-edge, may be unstable

Summary for one RTX 4070

Realistically expect ≈ 4–6 weeks of continuous 24/7 training if you use modern 3–4 bit quantization and all the memory-saving tricks Karpathy uses in llm.c / nanoGPT (gradient checkpointing, flash-attention, torch.compile, etc.).
Power consumption will be ~200–250 W the whole time → ~150–200 kWh total → electricity cost roughly $15–40 depending on your local rates (Taiwan electricity is ~NT$3–4/kWh → ~NT$500–800).

If you only have a 4070 Laptop (usually 8 GB or capped lower TDP), add another 50–100% time (so 2–3 months).

Faster alternatives on one 4070

Train the smaller d21 model (1.3B params) instead → ~60–65% of the time → 18–25 days
Train d32 but on fewer tokens (e.g., 15B–20B tokens) → you’ll finish in 12–20 days and still get something quite usable (still beats GPT-2 on many tasks).

So bottom line: yes, you absolutely can train nanochat d32 on a single RTX 4070, but expect roughly one month of nonstop running with 3–4 bit quantization.

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