Pick a model size and a number of passes over the Bible; see how long each card takes and what it costs on RunPod and Vast.ai today — then the five commands that run it.
hours and dollars to train on the whole Bible
GPU
peak TFLOPS
time
RunPod $/h
cost
Vast.ai median $/h
cost
–
How the estimate is made
Training work ≈ 6 × parameters × tokens (Kaplan et al. 2020). The Bible is – tokens under this series’ tokenizer, so passes × that is the token count. Divide by the GPU’s peak dense FP16/BF16 tensor throughput times a utilisation you choose — no real run reaches peak; 25% is what nanoGPT’s published GPT-2 reproduction works out to. Multiply hours by the price.
What it tells you
The Bible is tiny by modern standards. Even GPT-2 small, 20 passes, is minutes of GPU time and cents of money; the real constraint is that a 124M-parameter model memorises a million tokens long before it finishes, so more passes buy nothing (watch the validation curve on the Bible GPT page). Rent the small card, run the script below, download the checkpoint.
Renting one and running this repository on it
# 1 RunPod: Pods → Deploy → pick a GPU (RTX 4090 is plenty) → template "RunPod PyTorch" → Deploy; or Vast.ai: Search → filter GPU → Rent (image pytorch/pytorch)
# 2 connect (both show an SSH command; RunPod also has a web terminal)
ssh root@<pod-ip> -p <port> -i ~/.ssh/id_ed25519
# 3 get the code and the corpus (this folder), install the two dependencies
git clone <your-biblicalModels-remote> && cd biblicalModels
pip install numpy torch
python -m biblegpt.tokenizer # 9 s: byte-level BPE from the text# 4 train GPT-2 small on the Bible (12 layers, 12 heads, 768 wide, 1,024 context); watch the val loss, stop when it turns up
python -m biblegpt.train --name gpt2s --layers 12 --heads 12 --embd 768 --block 1024 --batch 16 --steps 2000 --eval-every 100
# 5 copy the checkpoint and log home, then stop the pod (billing runs until you do)
scp -P <port> -r root@<pod-ip>:biblicalModels/data/biblegpt/run_gpt2s ./data/biblegpt/
The same biblegpt/train.py that produced the small model on a CPU runs unchanged on a GPU (--device cuda is picked automatically).
What this page cannot promise. Prices are a snapshot from the fetch date shown — RunPod’s list prices and the median of Vast.ai’s live offers — and both move daily; check the provider before renting. The times are peak-throughput arithmetic scaled by the utilisation slider, not measurements on these cards: treat them as the right order of magnitude, not a quote. A rented machine also bills while it boots, installs and evaluates, so a job this small costs more in set-up minutes than in training; the only figure on this page that was measured is the CPU run behind the first button.
Nine seconds elsewhere
The foundations in learning order, then the series they lead to. Each lands in about nine seconds.