GPT-2 XL Training Storage Requirements | Generated by AI
Question: For training GPT-2 XL from scratch, how large a volume do I need to store everything — raw text data, tokenized data, and all checkpoints?
Answer:
GPT-2 XL Model Size First
GPT-2 XL: 1.5B parameters
float32: 1.5B × 4 bytes = 6GB per checkpoint
float16: 1.5B × 2 bytes = 3GB per checkpoint
bfloat16: 1.5B × 2 bytes = 3GB per checkpoint
With optimizer states (AdamW stores 2 momentum terms):
float32 model + AdamW:
params: 6GB
grad: 6GB
momentum (m): 6GB
variance (v): 6GB
total: ~24GB per training checkpoint
Full Breakdown
| Item | Size | Notes |
|---|---|---|
| Raw text (WebText-scale) | 40GB | OpenWebText ~40GB compressed |
Tokenized .bin files |
40GB | roughly same size after GPT-2 BPE |
| Model checkpoint (fp32+AdamW) | 24GB | full training state |
| Keep last 3 checkpoints | 72GB | safe rollback |
| Best checkpoint (inference only) | 3GB | fp16 weights only |
| Logs, tensorboard, misc | 2GB | |
| Total | ~157GB |
Recommendation
200GB volume — gives you ~40GB breathing room.
$0.00015/GB/hour × 200GB = $0.03/hour at rest
$0.03 × 24 × 30 = ~$21.60/month
Cheap insurance vs running out of space mid-training at $2/hr GPU time.
Directory Layout on Volume
/mnt/data/
├── raw/
│ └── openwebtext/ # ~40GB raw text
├── tokens/
│ └── train.bin # ~40GB tokenized
│ └── val.bin
├── checkpoints/
│ ├── ckpt_step_10000.pt # ~24GB each
│ ├── ckpt_step_20000.pt
│ └── ckpt_best.pt # ~3GB fp16
└── logs/
└── tensorboard/
Practical nanoGPT Config for This
# In your train.py / config
out_dir = '/mnt/data/checkpoints'
dataset = '/mnt/data/tokens'
# Save every N steps, keep last 3
save_interval = 1000
keep_last_n = 3
Bottom Line
Pick 200GB → costs $0.03/hr at rest, ~$21/month idle. Versus losing a training run because you hit disk full at step 50,000 — not worth it.