GPU Droplet Confirmed and Transfer Summary | Generated by AI
✅ GPU Test Training Complete!
GPU droplet confirmed working — 100 iterations in seconds, loss dropped 10.84 → 8.72 on the H200.
📋 Full Situation Summary
🔄 61GB Transfer (in progress)
VPS (159.89.216.20) ───scp───► My Server (/tmp) ───scp───► GPU Droplet
61GB 823MB / 61GB waiting
ETA: ~5 hours total
Note: VPS disk I/O is the bottleneck (1 vCPU, slow disk). Could take 3-5 hours for full 61GB transfer.
✅ What’s Already Done on GPU Droplet
| Item | Status | Notes |
|---|---|---|
| GPU | ✅ Working | NVIDIA H200, 143GB VRAM |
| PyTorch + CUDA | ✅ Installed | torch 2.11.0, CUDA 13 |
| nanoGPT code | ✅ Transferred | 157MB |
| Sample data | ✅ Transferred | 27MB train.bin + 191MB val.bin |
| Test training | ✅ Passed | Loss 10.84 → 8.72, confirmed GPU works |
⏳ What’s Pending
| Item | Status | Notes |
|---|---|---|
| Full 61GB file transfer | 🔄 1.3% done | 823MB / 61GB, ~5 hours |
| Full data preparation | ⏳ Waiting | Run prepare_fineweb.py after transfer |
| Real training | ⏳ Waiting | Run with train_fineweb.py config |
🐌 Transfer Speed Issue
The VPS is only pushing ~200MB/min because it’s:
- 1 vCPU reading from possibly slow disk
- Network bandwidth to my server
Alternatives to speed up:
- Direct VPS → GPU droplet transfer (once SSH keys sorted)
- Resume the transfer on GPU droplet directly from VPS
- Wait it out — 5 hours is not terrible
Want me to set up the direct VPS → GPU droplet SSH connection so the transfer can go straight there?