Estimate Complexity
Sonar Pro Search's response to the 'Estimate Complexity' challenge.
?Prompt
Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.
Response
Estimating the FLOPs Required to Train GPT-3
Background Information
GPT-3 is a large transformer-based language model developed by OpenAI. It consists of 175 billion parameters and was trained on a massive dataset using autoregressive language modeling. "FLOPs" stands for "Floating Point Operations," a standard measure of computational cost in machine learning.
Step-by-Step Estimation
1. Model Size and Training Data
- Number of parameters: 175 billion (1.75×10¹¹).
 - Training dataset size: GPT-3 was reportedly trained on 300 billion tokens.
 
2. FLOPs per Parameter per Token
For transformer models, the number of FLOPs per token per parameter is not exactly 1, but it’s a useful approximation for estimation. In practice, additional computation is required for attention mechanisms, feedforward layers, and other architectural components. A reasonable ballpark for transformer models is between 2 and 6 FLOPs per parameter per token (some sources estimate closer to 6).
3. Overall Calculation
Using the approximation of 6 FLOPs per parameter per token:
[\text{Total FLOPs} = \text{Parameters} \times \text{Tokens} \times \text{FLOPs per parameter per token} ]
Plugging in the numbers:
[\text{Total FLOPs} = 175,000,000,000 \times 300,000,000,000 \times 6 ] [= 3.15 \times 10^{22} \text{ FLOPs} ]
4. Summary Table
| Parameter | Value | 
|---|---|
| Parameters | 175,000,000,000 | 
| Training Tokens | 300,000,000,000 | 
| FLOPs/param/token | 6 | 
| Total FLOPs | 3.15 × 10²² | 
Interpretation
- Order of magnitude: The estimated FLOPs required to train GPT-3 are approximately ( 3 \times 10^{22} ) FLOPs.
 - Assumptions: This estimate assumes 6 FLOPs per parameter per token, which accounts for the complexity of transformer computations but is still a rough approximation.
 - Caveats: The actual number may vary depending on implementation details, optimizations, and the precise definition of "FLOPs" for this context.
 
In summary: Training GPT-3 likely required on the order of ( 3 \times 10^{22} ) FLOPs, based on reasonable approximations of model architecture and training regime.
About Sonar Pro Search
Capabilities
Categories
Specifications
- Provider
 - Perplexity
 - Released
 - 2025-10-30
 - Size
 - LARGE
 - Context
 - 200,000 tokens