Training GPT-3.5 Requires Far More Compute | Generated by AI
No, the total compute from a single NVIDIA RTX 4070 Ti GPU running non-stop for one year at 90% utilization (1.14 × 10²¹ FLOPs) is far short of what’s needed to train a model like GPT-3.5 from scratch.
Key Comparison
- Required for GPT-3.5 training: Estimates place the pre-training compute for GPT-3.5 (similar to GPT-3 in scale, with around 175 billion parameters) at approximately 3.14 × 10²³ FLOPs. This is based on public analyses of GPT-3’s training, as OpenAI hasn’t released exact figures for GPT-3.5, but it’s widely considered comparable.
- Your scenario: 1.14 × 10²¹ FLOPs is only about 0.36% of that amount—meaning you’d need roughly 275 times more compute (or equivalently, 275 such GPU-years) to match the training requirements.
- Time on one GPU: Even if you could somehow run the training on a single GPU (which you can’t, due to memory limits—GPT-3.5 would require hundreds of GB of VRAM), it would take around 35 years based on similar calculations for high-end GPUs like the A100.
Practical Limitations
Beyond raw FLOPs, training a large language model like GPT-3.5 requires:
- Massive parallelism: It was trained on thousands of GPUs simultaneously (e.g., clusters of A100s) to handle the model’s size and complete training in months, not years.
- Data and infrastructure: Access to enormous datasets (trillions of tokens), specialized software, and cooling/power setups that a single consumer GPU can’t provide.
- Efficiency tweaks: Modern training uses mixed precision (e.g., FP16), optimizers, and distributed systems, but even optimized, one GPU-year won’t cut it.
If you’re interested in smaller-scale experiments, consider fine-tuning pre-trained models or training tiny LLMs (e.g., with 1-10 billion parameters) on consumer hardware—these can be done with far less compute, often in days or weeks.
Demystifying GPT-3
Why Training ChatGPT (GPT-3.5) Takes 35 YEARS on a Single GPU!
The FLOPs Calculus of Language Model Training