Training GPT-2 on One GPU | Generated by AI
Yes, the total compute of 1.14 × 10²¹ FLOPs from a single NVIDIA RTX 4070 Ti running non-stop for one year at 90% utilization is sufficient to train a model like GPT-2 from scratch, potentially even an optimized version comparable to the original 1.5 billion parameter model.
Key Comparison
- Required for GPT-2 training: The original GPT-2 (1.5B parameters) was trained on approximately 10 billion tokens, requiring an estimated 9 × 10¹⁹ FLOPs based on standard transformer training formulas (roughly 6 × parameters × tokens). However, for a compute-optimal version (e.g., similar to DeepMind’s Gopher 1.4B model trained on 300B tokens), estimates rise to about 2.5 × 10²¹ FLOPs. Your scenario provides 1.14 × 10²¹ FLOPs, which is more than enough for the original setup (about 12 times the compute) and roughly half of the optimal estimate—close enough that with efficient training techniques, it could work for a high-quality 1.5B model.
- Smaller variants: If referring to GPT-2 Small (124M parameters), compute-optimal training requires only about 2.37 × 10¹⁸ FLOPs (on ~3.3B tokens). Your setup delivers over 480 times that amount, meaning you could train it many times over or on vastly larger datasets for better performance.
- Time on one GPU: Training GPT-2 (1.5B) on a single GPU isn’t feasible due to memory constraints (it requires ~50GB+ during training, while the 4070 Ti has 12GB). You’d need model parallelism or a multi-GPU setup. For GPT-2 Small, it’s more practical on consumer hardware and could complete in weeks rather than months, depending on optimizations.
Practical Limitations
Similar to GPT-3.5, training involves more than raw FLOPs:
- Hardware needs: A single consumer GPU can’t handle large models without techniques like quantization or distributed training.
- Data and setup: You’d need access to a large dataset (e.g., 10B+ tokens from sources like Common Crawl), plus software like Hugging Face Transformers.
- Efficiency: Using mixed precision (FP16) and modern optimizers can reduce effective compute needs by 2-3x.
For hands-on experiments, start with fine-tuning pre-trained GPT-2 or training even smaller models—these are doable on a 4070 Ti in hours to days.
Why didn’t we get GPT-2 in 2005?
The FLOPs Calculus of Language Model Training
Training a compute-optimal gpt2-small
An empirical analysis of compute-optimal large language model training
GPT-2 - Wikipedia