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Of course. Here is an estimate of the total number of FLOPs required to train GPT-3, based on my internal kn owledge and first principles, without searching online.
The Logic and Formula
To estimate the computational cost of training a large language model like GPT-3, we can use a well-establis hed formula. The total number of Floating Point Operations (FLOPs) is approximately:
Total FLOPs ≈ 6 × P × D
Let's break down what each part of this formula means:
- P: The number of parameters in the model. This is the total count of all the weights and biases that the model learns during training.
- D: The total number of tokens in the training dataset. This represents the volume of data the mo del processes.
- 6: This is a constant factor that provides a good approximation for transformer-based models. It com
es from:
- Forward Pass (1 × P × D): For each token processed, the number of operations is roughly proporti
onal to the number of parameters. A common rule of thumb is
2 * PFLOPs per token for a forward pass (one multiplication and one addition for each parameter). So the total for one pass over the data is2 * P * D. - Backward Pass (2 × Forward Pass): The backward pass (backpropagation), where the model calculate
s gradients to update its parameters, is computationally more intensive. It's typically estimated to require
about twice the operations of the forward pass. So,
2 * (2 * P * D) = 4 * P * D. - Total: Adding the forward and backward pass gives us
2PD + 4PD = 6PD.
- Forward Pass (1 × P × D): For each token processed, the number of operations is roughly proporti
onal to the number of parameters. A common rule of thumb is
Now, let's estimate the values for P and D for GPT-3.
Step-by-Step Estimation
Step 1: Estimate the Number of Parameters (P)
My internal knowledge base indicates that the largest version of GPT-3, which is the one most people refer t o, has a specific number of parameters.
- P ≈ 175 billion parameters
- In scientific notation, this is 175 x 10⁹
Step 2: Estimate the Training Dataset Size (D)
GPT-3 was trained on a massive corpus of text data scraped from the internet (like the Common Crawl dataset) , books, and Wikipedia. The key is not the raw size of the dataset, but the number of tokens actually used f or training. My recollection is that the model was trained for a single epoch on a curated dataset.
- D ≈ 300 billion tokens
- In scientific notation, this is 300 x 10⁹
Step 3: Perform the Calculation
Now we can plug these estimated values into our formula:
Total FLOPs ≈ 6 × P × D
-
Substitute the values for P and D:
Total FLOPs ≈ 6 × (175 × 10⁹) × (300 × 10⁹) -
Multiply the constants first:
6 × 175 = 10501050 × 300 = 315,000 -
Multiply the powers of 10:
10⁹ × 10⁹ = 10¹⁸ -
Combine the results:
Total FLOPs ≈ 315,000 × 10¹⁸ -
Express this in standard scientific notation:
Total FLOPs ≈ 3.15 × 10⁵ × 10¹⁸ = 3.15 × 10²³
Final Estimate and Conclusion
Based on my reasoning and the standard formula for calculating training costs, the estimated number of FLOPs required to train the 175B parameter GPT-3 model is approximately:
3.15 x 10²³ FLOPs
To put this astronomical number into perspective:
- A petaFLOP is 10¹⁵ FLOPs. Our estimate is 315,000,000 petaFLOPs.
- If you had a supercomputer that could perform one petaFLOP per second (10¹⁵ FLOPs/sec), it would take ** 3.15 x 10⁸ seconds** to complete the training.
- There are about 31.5 million seconds in a year, so this is equivalent to roughly 10 years of continu ous computation on that machine.
This calculation demonstrates why training foundational models like GPT-3 requires massive-scale data center s with thousands of GPUs running in parallel for months and is an undertaking that costs millions of dollars . The actual paper from OpenAI stated the training took approximately 3.6 x 10²³ FLOPs, so my estimate i s very close to the reported figure.