At the Data Center World 2023 event in Austin, Texas, the high expenses of training huge language models were discussed. There, theMind’s Constantine Goltsev, an AI\/ML solutions business, stressed the expenses associated with training models like ChatGPT.<\/p>\n
Goltsev stressed that training ChatGPT’s 175 billion parameters is extremely expensive, requiring 175 billion computations for each input and using a large amount of electricity. OpenAI’s future GPT-4 model conducted compute operations for months using 12,000 to 15,000 Nvidia A100 processors, which cost $10,000 apiece. These discoveries highlight the need for a more realistic approach to artificial intelligence deployment.<\/p>\n
Goltsev advocated using smaller, open source models that can match or even outperform ChatGPT’s performance. Organizations may accomplish spectacular results by adopting the same fine-tuning approaches used to produce ChatGPT by using academic models or open source equivalents with parameters numbering in the billions (e.g., 6 billion or 3 billion). This method makes it possible to use AI technology at a reasonable cost.<\/p>\n
Goltsev used Amazon Web Services (AWS) as an example to show how a major legal firm may use AWS to develop a semantic search engine. The company might achieve its goals for roughly $30 to $40 per hour by hiring a few big instances with around eight A100 cards, plenty of memory, and storage.<\/p>\n