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DO YOU KNOW HOW BIG IS GPT-4?
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1.8 TRILLION parameters across 120 layers, making it 10 times larger than GPT-3!
16 EXPERTS within the model, each with 111 BILLION parameters for MLP!
13 TRILLION tokens of training data, including text-based and code-based data, with some fine-tuning from ScaleAI and internally!
$63 MILLION in training costs, taking into account computational power and training time!
3 TIMES MORE expensive to run than the 175B parameter Davinci, due to larger clusters and lower utilization rates!
128 GPUs for inference, using …
ai big billion code computational costs data discuss experts fine-tuning gpt gpt-3 gpt-4 llm machinelearning making mlp parameters power text tokens training training costs training data