Prepare Data on GPU Droplet Instead | Generated by AI
Exactly right — your current VPS has 10GB disk + 512MB RAM, which can’t handle 61GB input + ~30GB output = ~91GB total.
But here’s the better approach: Don’t prepare on VPS at all.
Recommended: Prepare on GPU Droplet
┌─────────────────┐ just transfer raw ┌─────────────────┐
│ VPS (current) │ ─── 61GB text file ─── │ GPU Droplet │
│ 1 vCPU, 512MB │ (rsync/scp) │ (has GPU + big │
│ 10GB disk │ │ disk + RAM) │
└─────────────────┘ └────────┬────────┘
│
run prepare.py here
│
~30GB .bin files
ready for training
On GPU Droplet (big disk, fast CPU):
# Transfer raw text file
rsync -avz --progress root@159.89.216.20:/root/zz/datasets/fineweb_extracted_all.txt ./
# Link and prepare (uses GPU droplet's resources)
ln -sf fineweb_extracted_all.txt ./data/fineweb/train_fineweb.txt
python data/fineweb/prepare_fineweb.py
# Then train immediately on the same machine
python train.py config/train_fineweb.py
Why This Makes Sense
| Task | VPS | GPU Droplet |
|---|---|---|
| Prepare data | ❌ Too weak | ✅ Powerful CPU + big disk |
| Train model | ❌ No GPU | ✅ Has GPU |
| Host code | ✅ Fine | ✅ Fine |
VPS just stores the raw 61GB file. GPU droplet does all the heavy work.
What specs does your GPU droplet have?