Estimate Complexity

To estimate the total number of floating-point operations (FLOPs) required to train GPT-3, we use the standard approximation that training a transformer model requires approximately 6 FLOPs per parameter per token. This accounts for both the forward and backward passes, with each parameter involved in roughly 6 operations per token.

Step-by-step reasoning:

1. Number of parameters (N): GPT-3 (175B model) has 175 billion parameters.
2. Number of tokens (T): The training dataset contains 300 billion tokens.
3. FLOPs per parameter per token: Approximately 6 FLOPs.
4. Total FLOPs: Multiply these three values:
   [ \text{Total FLOPs} = 6 \times N \times T = 6 \times 175 \times 10^9 \times 300 \times 10^9 = 3.15 \times 10^{23} \text{ FLOPs}. ]

This estimate aligns with commonly cited values in the literature. The quadratic attention term (arising from the sequence length) adds a negligible contribution (less than 2%) and is ignored.

Thus, the total FLOPs required to train GPT-3 is approximately (3.15 \times 10^{23}).

\boxed{3.15\times 10^{23}\ \text{FLOPs}}