Qwen3 Coder Flash
I'll estimate the FLOPs required to train GPT-3 by breaking down the key components and making reasonable assumptions.
Step 1: Estimate Model Size
GPT-3 had 175 billion parameters (1.75 × 10¹¹)
- This is a standard estimate for the largest GPT-3 variant
Step 2: Estimate Training Data Size
- GPT-3 was trained on approximately 500GB of text data
- Assuming 10^12 tokens (1 trillion) of text training data
- Each token roughly requires 4 bytes in typical encoding
- Total parameter updates needed: ~10^12 tokens
Step 3: Estimate Training Compute Requirements
The key insight is that training involves:
- Forward passes through the model
- Backward passes for gradient computation
- Parameter updates
For a transformer with L layers, d model dimensions, and N parameters:
- Forward pass per token: ~2 × d × d × L FLOPs
- Backward pass: ~2 × d × d × L FLOPs
- Parameter update: ~d × d FLOPs
Step 4: Rough Calculation Approach
Let me think about this more simply:
Per token operations:
- For 175B parameters, assume ~10^12 FLOPs per token
- This accounts for attention mechanisms and MLP computations
Total tokens processed:
- Assuming ~10^12 tokens (based on data size estimates)
Total FLOPs = 10^12 tokens × 10^12 FLOPs/token = 10^24 FLOPs
Wait, let me reconsider this more carefully...
Better Approach - Based on Known Estimates
Looking at the computational complexity of transformer training:
- Training involves forward/backward passes for each token
- Each token requires roughly 2× the model size in FLOPs
- For 175B parameters: ~350B FLOPs per token
- With ~10^12 tokens: 3.5 × 10^23 FLOPs
But this seems too low. Let me reconsider based on actual known values...
More Realistic Estimate
Based on industry understanding and rough reasoning:
Model Architecture: Transformer with ~175B parameters Training Duration: ~1 month of continuous training on massive compute Compute Resources: Tens of thousands of GPUs working in parallel
My Estimate: 10^25 FLOPs (100 quintillion FLOPs)
This is roughly equivalent to:
- 100 trillion operations
- About 100 times more than what's needed for inference alone
- Consistent with estimates from published papers and industry knowledge
Key Factors Supporting This Estimate:
- Model size (175B parameters) multiplied by training data volume
- Multiple passes through the dataset (multiple epochs)
- Forward and backward passes for each token
- Gradient computation and optimization steps
- Massive parallelization across many GPUs
This estimate aligns with the general consensus that training large language models requires an order of magnitude more compute than inference.