Discover the staggering amount of computing power required to train the state-of-the-art BLOOM language model, representing a major milestone in AI advancement.
Table of Contents
Question
How much computer processing time did it take to train the BLOOM model?
A. 1 million hours
B. 3 million hours
C. 5 million hours
D. 10 million hours
Answer
C. 5 million hours
The correct answer is C. It took approximately 5 million hours of computer processing time to train the BLOOM (BigScience Large Open-science Open-access Multilingual) language model.
Explanation
BLOOM is a large-scale multilingual generative language model developed through a collaborative effort called BigScience Workshop, involving over 1000 researchers from more than 70 countries and 250 institutions. The model was trained on 46 natural languages and 13 programming languages, making it one of the most linguistically diverse models to date.
To achieve this impressive feat, BLOOM required an immense amount of computational resources. The model was trained using the Jean Zay supercomputer in France, which is one of the most powerful supercomputers in Europe. It took around 5 million hours of processing time (equivalent to about 570 years on a single GPU) to complete the training process.
This massive computational requirement is due to the model’s size and complexity. BLOOM has 176 billion parameters, making it larger than GPT-3 (175B) and slightly smaller than the latest GPT-3 model, which has 760B parameters. The training data used for BLOOM consisted of 1.6 terabytes of text data, further contributing to the lengthy training process.
The 5 million hours of processing time represent a significant milestone in the field of artificial intelligence, demonstrating the immense computational power and resources required to train state-of-the-art language models. As AI continues to advance, it is likely that even more processing time will be needed to develop increasingly sophisticated and capable models in the future.
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