Training GPT-3 with 10 H200 GPUs | Generated by AI
Question: How long would it take to train GPT‑3 with 10× H200 GPUs?
Answer:
GPT‑3 is much larger than Qwen‑3.5 9B, so the scale difference is huge. Let’s break it down carefully.
1. GPT‑3 full model specs
| Model | Params | Dataset tokens | FLOPs (est.) |
|---|---|---|---|
| GPT‑3 (175B) | 175B | 300B–400B tokens | ~3–4×10²³ FLOPs |
Notes:
- 175B parameters → single H200 GPU cannot fit full model in memory for training without model parallelism.
- You must use FSDP or tensor model parallelism across multiple GPUs.
2. GPU throughput estimate
-
Single H200 can handle ~30M tokens/day for 9B model
-
GPT‑3 175B is ~20× larger than 9B
- Rough scaling: throughput drops by ~20× if you don’t increase batch size
- So single H200: ~1–2M tokens/day for full GPT‑3 training
-
10× H200 with ideal distributed scaling: ~10–15M tokens/day
This assumes perfect FSDP, but network overhead and memory fragmentation reduce efficiency. Realistic: 70–80% of ideal → ~8–12M tokens/day
3. Time estimate for full pretraining
- Dataset: 300B tokens
- Throughput: 10M tokens/day (realistic)
[ 300,\text{B tokens} ÷ 10,\text{M tokens/day} = 30,000 \text{ days} \approx 82 \text{ years!} ]
✅ Full GPT‑3 training on 10 H200 GPUs is completely impractical.
4. Practical approach
-
LoRA / instruction-tuning / fine-tuning
- Use pretrained GPT‑3 weights
- Fine-tuning on 1–10B tokens: 1–2 weeks on 10 GPUs
-
Partial pretraining
- Train small fraction of tokens (~1–10B tokens) for research
- 10 GPUs → 1–2 weeks
- This is useful for experimenting with hyperparameters or testing new training tricks
-
Model parallelism
- Must split 175B across hundreds of GPUs in reality (like GPT‑3 in OpenAI’s infrastructure)
5. Key takeaway
| Scenario | Tokens/day | Time for full dataset |
|---|---|---|
| 1× H200 | ~1–2M | ~410 years |
| 10× H200 | ~10M | ~82 years |
| LoRA / partial pretraining (1–10B tokens) | 10M | 1–2 weeks |
Conclusion:
- Full scratch training of GPT‑3 on 10 H200 GPUs → impossible.
- Fine-tuning / partial pretraining → feasible, practical, and realistic.
- For real GPT‑3 scale, labs use hundreds of GPUs or TPUs over 1–2 weeks.
If you want, I can make a direct comparison chart showing GPT‑2, Qwen-3.5 9B, GPT‑3 175B on 1 GPU, 10 GPUs, and 100 GPUs, so you can see exactly what’s feasible in 1 week, 1 month, or 1 year. This gives a clear operational perspective.
Do you want me to make that chart?