Estimating the FLOPs required to train GPT-3 without looking it up is a classic “Fermi estimation” problem — breaking it down into components we can reason about, using approximations and scaling laws.

Let’s go step by step.

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STEP 1: Recall GPT-3’s key specs (from memory)

GPT-3 has:

• ~175 billion parameters (1.75 × 10¹¹)
• Trained on ~300 billion tokens (3 × 10¹¹)

These numbers are widely cited and should be roughly accurate from memory.

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STEP 2: FLOPs per parameter per token

A common rule of thumb in deep learning is:

Training a transformer model requires approximately 6 FLOPs per parameter per token.

Why 6?

• In the forward pass, roughly 2 FLOPs per parameter (multiply + add for each weight update contribution).
• In the backward pass, you need about twice the FLOPs of the forward pass (gradient computation w.r.t. inputs and weights).
• Then there’s the weight update (optimizer step), which adds a bit more — roughly same cost as forward.

So: 2 (forward) + 4 (backward + update) ≈ 6 FLOPs/param/token.

This is a standard approximation used in scaling law papers (like those from OpenAI and DeepMind).

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STEP 3: Multiply parameters × tokens × FLOPs per param per token

So:

FLOPs = (number of parameters) × (number of tokens) × (FLOPs per param per token)

= (1.75 × 10¹¹) × (3 × 10¹¹) × 6

Let’s compute:

First, 1.75 × 3 = 5.25

Then, 10¹¹ × 10¹¹ = 10²²

So: 5.25 × 10²² × 6 = 31.5 × 10²² = 3.15 × 10²³

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STEP 4: Final Estimate

~3 × 10²³ FLOPs

Or, written out: 300 sextillion FLOPs

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STEP 5: Sanity Check

Let’s see if this makes sense.

• GPT-2 (1.5B params, ~40B tokens) → 1.5e9 × 4e10 × 6 = ~3.6e20 FLOPs
• GPT-3 is ~100× more parameters and ~7.5× more tokens → 750× more FLOPs → 3.6e20 × 750 ≈ 2.7e23 → close to our 3.15e23.

Also, known published estimates (which we’re avoiding, but for post-hoc sanity) are around 3.14 × 10²³ — so this lines up.

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✅ Final Answer:

Approximately 3 × 10²³ FLOPs are required to train GPT-3.

This estimate is derived from:

• 175B parameters
• 300B training tokens
• 6 FLOPs per parameter per token (standard transformer training cost)

All approximated from memory and first principles.