Estimate Complexity

GPT-3 is one of the largest language models ever created with 175 billion parameters. The number of FLOPs required to train a neural network depends on factors such as the number of parameters, the size of the input data, the number of layers, and the batch size used during training.

To estimate the total number of FLOPs required to train GPT-3, we can consider the following:

1. Size of parameters: GPT-3 has 175 billion parameters. Each parameter needs to be updated during training, so we need to perform a certain number of FLOPs for each parameter update.

2. Number of training iterations: Training a large model like GPT-3 typically requires hundreds or thousands of training iterations. Each iteration involves multiple forward and backward passes through the network, so we need to consider the FLOPs required for each pass.

3. Batch size: The batch size used during training also affects the number of FLOPs required. Larger batch sizes require more FLOPs per iteration.

4. Complexity of the model: GPT-3 is a transformer-based model, which is known to be computationally intensive due to its self-attention mechanism. The FLOPs required for self-attention operations are typically higher compared to other types of neural network operations.

Given the large size of GPT-3 and the complex nature of transformer models, training it would require a massive amount of computational resources. Without searching online for specific numbers, we can estimate that the total number of FLOPs required to train GPT-3 would be in the order of 10^19 to 10^20 FLOPs. This is based on the fact that training large language models like GPT-2 (which has 1.5 billion parameters) required hundreds of petaflops of computation, and GPT-3 is significantly larger in size.