Recently, the artificial intelligence research company Epoch AI released an interactive simulator designed to simulate the computational power required to train large language models. Through this simulator, researchers discovered that while an old graphics card from 2012 (such as the GTX580) can be used to train GPT-4, the cost would be ten times that of modern hardware today.
Epoch AI's research indicates that the number of floating-point operations (FLOP) required to train GPT-4 is between 1e25 and 1e26. For this study, the simulator analyzed the efficiency of different graphics cards, particularly their performance as the model size increases. The results showed that efficiency generally decreases as the model grows. For example, the recently released H100 graphics card can maintain high efficiency over a longer period, while the V100 graphics card shows a more significant drop in efficiency when faced with larger training scales.
In Epoch AI's experiments, the GTX580 graphics card has only 3GB of memory. This graphics card was a mainstream choice for training the AlexNet model back in 2012. Despite advancements in technology, researchers believe that it is possible to conduct such large-scale training using old hardware, although the required resources and costs are extremely high.
Additionally, this simulator supports complex training simulations across multiple data centers. Users can customize parameters such as the size, latency, and connection bandwidth of the data centers, allowing for simulations of training runs across various locations. This tool also enables the analysis of performance differences between modern graphics cards (such as H100 and A100), studies the effects of different batch sizes and multi-GPU training, and generates detailed log files to record the model's outputs.
Epoch AI stated that the purpose of developing this simulator is to deepen the understanding of hardware efficiency improvements and to assess the impact of chip export controls. As the demand for large training tasks is expected to increase in this century, understanding the hardware requirements for the future has become particularly important.
Key Points:
💻 The GTX580 graphics card, launched in 2021, can train GPT-4 at ten times the cost but with low efficiency.
📊 The simulator can analyze performance differences among various GPUs and supports multi-data center training simulations.
🔍 This research aims to enhance the understanding of future hardware needs to aid in the training of large AI models.
