VPS Upgrade vs GPU Droplet Comparison | Generated by AI
Good question. Let me break it down:
If you upgrade VPS to 2vCPU + 50GB extra space
Time Estimate
Current VPS (1vCPU): ~10 hours to tokenize 61GB
2 vCPU with multiprocessing: ~5-6 hours
The script uses mp.Pool with cpu_count // 2 workers. With 2 CPUs → only 1 worker (same as now). The speedup would come from slightly faster CPU, not parallelization.
Disk Space After Upgrade
Current: 10GB total
├── 61GB raw file (doesn't fit!)
With +50GB: 60GB total
├── 61GB raw file ← needs 61GB
└── 30GB bin files
RESULT: STILL NOT ENOUGH (need 91GB, only have 60GB)
You Still Need More Disk
To hold both input + output you’d need ~100GB total (61 + 30 + buffer).
Realistic Options
| Option | Cost | Time | Effort |
|---|---|---|---|
| Upgrade VPS to 2vCPU + 100GB | ~$15-20/mo extra | 5-6 hours | Easy |
| Use GPU droplet for prep | Already paid | 20-30 min | Transfer file once |
| Use VPS for prep (current specs) | Already paid | ~10 hours | Wait |
My Recommendation
Use the GPU droplet for both. Here’s why it’s actually faster overall:
Option A (VPS upgrade): Option B (GPU droplet):
Pay $15-20/mo extra Use existing droplet
Wait 5-6 hours Wait 20-30 min for prep
Still need GPU droplet Then train immediately
for training anyway
Transfer once, done:
# From VPS to GPU droplet
rsync -avz root@159.89.216.20:/root/zz/datasets/fineweb_extracted_all.txt root@<gpu-droplet>:~/
# On GPU droplet (~30 min prep + instant training setup)
ln -sf fineweb_extracted_all.txt ./data/fineweb/train_fineweb.txt
python data/fineweb/prepare_fineweb.py
python train.py config/train_fineweb.py
What’s the GPU droplet specs (RAM, disk, GPU model)?