GPT-3 Training Costs and Hardware Specifications | Generated by AI
Training Details for GPT-3
To train GPT-3 (specifically the 175B parameter version), OpenAI used a massive supercomputer infrastructure provided by Microsoft. Below are the estimated costs, time, and hardware used for its development.
1. Training Cost
The cost of a single training run for GPT-3 is estimated to be between $4.6 million and $5 million.
- Compute Expenses: This figure primarily covers the rental or operational cost of the high-end GPU instances in the cloud (Microsoft Azure).
- Total Budget: While the raw compute was ~$5M, the total cost for OpenAI—including research, personnel, and multiple failed or experimental runs—was likely significantly higher.
2. Training Time
The actual training time for the final model is estimated to have taken 34 days using a highly parallelized cluster.
- Parallelism: To achieve this, OpenAI used 1,024 NVIDIA A100 GPUs working in sync.
- Hypothetical Comparison: If you tried to train GPT-3 on a single NVIDIA Tesla V100 GPU, it would take approximately 355 years to complete.
3. Hardware (The “Cards”)
OpenAI used data-center grade GPUs rather than consumer graphics cards.
- Primary Card: The model was originally developed and trained on NVIDIA Tesla V100 GPUs as part of a custom Microsoft supercomputer.
- Cluster Scale: This supercomputer featured over 10,000 GPUs and 285,000 CPU cores to handle the massive workload.
- Current Standard: Later optimizations and reproductions (like those estimated in 2021) often cite the NVIDIA A100 or H100 as the standard hardware for models of this scale.
Summary Table
| Category | Details |
|---|---|
| Compute Cost | ~$4.6M – $5M per run |
| Duration | ~34 days (on a 1,024 GPU cluster) |
| Main Hardware | NVIDIA Tesla V100 (later A100/H100) |
| Total Parameters | 175 Billion |
References:
- OpenAI Presents GPT-3 Technical Overview
- Demystifying GPT-3 Training Costs
- GPT-3 Wikipedia Technical Summary