Training 760M Model on 12GB GPU | Generated by AI
Here’s the analysis of this machine:
Machine Specs
| Component | Details |
|---|---|
| CPU | Intel i9-13900KF, 24 cores / 32 threads, 5.8GHz boost |
| RAM | 64GB (56GB available) |
| GPU | NVIDIA RTX 4070, 12GB VRAM, CUDA 13.0, driver 580.126.20 |
| Disk | /mnt/data = 1.8TB total, 972GB free (45% used) |
| PyTorch | 2.10.0+cu128, CUDA working |
What’s Already Here
You already have nanoGPT set up at /mnt/data/nanoGPT/ with:
- 285GB of FineWeb-Edu data in
data/fineweb/edu_fineweb100B/(263GB of shards + 6.9GB train.bin) - A 760M config at
config/train_fineweb_760m.py— n_layer=24, n_head=24, n_embd=1536 - Previous training runs: out-fineweb, out-gpt2-200m, out-wikipedia, etc.
The Problem: RTX 4070 has only 12GB VRAM
The 760M config was written for MI300X (192GB). On your RTX 4070 (12GB):
- Model weights (fp16): ~1.5GB
- Optimizer states (Adam): ~4.5GB
- Activations for bs=32, block=1024: way over 12GB
You’ll hit OOM immediately with batch_size=32.
Options
Option A: Train on this GPU with reduced batch
batch_size=2, gradient_accumulation_steps=16
This keeps effective batch at 32K tokens but processes 2 samples at a time. Will be slow (~weeks for 445K steps) but fits in 12GB.
Option B: CPU-only training — 64GB RAM is plenty for the model, but training would take months. Not practical.
Option C: Use the MI300X remotely — if you have SSH access to the MI300X machine, scp the data there and train. The 760M config is already tuned for it.
Option D: Smaller model on this GPU — train a 200M or 350M model that comfortably fits with bs=12+ on the 4070.
The data is already prepared — no downloads needed. Want me to adjust the config for Option A (760M on RTX 4070 with gradient accumulation) and do a smoke test?