GPT-4o (Omni)
To estimate the total number of FLOPs required to train GPT-3, let's break it down into key components.
1. Model Size and Parameters
GPT-3 has 175 billion parameters.
2. FLOPs per Forward and Backward Pass
In deep learning, the FLOPs required for a forward pass and backward pass are estimated as:
- Forward pass: ( 2 imes ) the number of parameters
- Backward pass: ( 2 imes ) the FLOPs of the forward pass
Thus, total FLOPs per training step:
[ ext{Total FLOPs per step} = 6 imes ext{number of parameters} imes ext{tokens per batch} ]
For GPT-3, let's assume 175B parameters and a batch size of 2048 tokens (common for large-scale training).
[ ext{FLOPs per training step} = 6 imes 175 imes 10^9 imes 2048 ]
[ = 2.15 imes 10^{15} ext{ FLOPs per step} ]
3. Total Training Steps
GPT-3 was trained with 300 billion tokens (as per OpenAI's paper).
If the batch size is 2048 tokens, the total number of steps is:
[ ext{Total steps} = rac{300 imes 10^9}{2048} approx 1.46 imes 10^8 ]
4. Total FLOPs Required
Multiplying the FLOPs per step by the total number of steps:
[ ext{Total FLOPs} = (2.15 imes 10^{15}) imes (1.46 imes 10^8) ]
[ approx 3.14 imes 10^{23} ext{ FLOPs} ]
Final Answer:
The estimated total number of FLOPs required to train GPT-3 is about ( 3.14 imes 10^{23} ) floating point operations.