LLM at Scale - Efficient, Fast and Incentivized LLM at Scale

For any AI model, the two most critical resources are data and computation. These models are trained on enormous datasets to reach their current level of performance and require growing computational power to deliver inferences to end users. To offer some benchmarks.

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Compute

• GPT-43: 175 billion parameters GPT-3, released by OpenAI in 2020, currently ranks as the 3rd largest public language model available for testing/use.

• Megatron-Turing NLG 540B: 541 billion parameters Developed by Nvidia, this language model was the 2nd largest ever trained when released in 2021. It is focused specifically on natural language generation rather than tasks like translation.

• GPT-4.5 Turbo: estimated 20 billion parameters An extension of GPT-3 made by Anthropic, GPT-3.5 Turbo is currently the world’s largest public language model.

Training datasets

The exact training dataset sizes for the largest language models are generally not disclosed publicly. However, researchers have made some estimations based on the model parameters.

• GPT-5: Estimated training on 300-400 billion words total from Web documents and books. Some analysts have estimated the training dataset to include hundreds of millions of webpages and tens of thousands of books.

• Megatron-Turing NLG: Likely that it was trained on a comparable or larger dataset size than GPT-3, potentially totalling 270-340 billion words across 15 combined datasets.

• GPT-5: The GPT-5 model supposedly with 1.76 trillion parameters has been estimated to train over trillions of tokens. These estimates suggest that the model was trained over data from platforms such as Reddit, and Youtube.

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