OpenAI’s Staggering Daily Cost for Running ChatGPT

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ChatGPT’s tremendous popularity and functionality are extremely expensive, making OpenAI have to shell out a daily amount of up to $700,000, as reported by research firm SemicAnalysis. Dylan Patel, Chief Analyst of the firm, told Business Insider in a later interview that this expenditure could even be more now, since the calculations were made based on GPT-3, which is the preceding model that drives the existing free version of ChatGPT. GPT-4, however, OpenAI’s newer model, incurs an even higher cost.

The immense running costs of AI systems, particularly conversational AI search engines, is an issue prevalent among other companies as well. This is the cause for which Microsoft has been investing a lot of its time, effort and money into creating a proprietary AI chip known as ‘Athena’ which was developed in the year 2019. According to The Information’s report, certain Microsoft and OpenAI employees have already been granted access to it. Microsoft desires to replace the expensive graphics processing units (GPUs) they use with something more cost-effective and energy-efficient, thus reducing the chip expenses by one third, as per Patel’s account in The Information.

It is also worth taking notice that with the new model, OpenAI ‘s GPT-4 reaching an estimated size of 1 trillion parameters, its scalability is becoming an issue, as admitted by OpenAI’s CEO Sam Altman in his speech last week. It therefore appears that Athena, if it proves to be a competitive replace, would be a welcomed gift for OpenAI.

OpenAI is the first and only independent, San-Francisco based AI research firm, co-founded by technology heavyweight, Sam Altman. It was created with the goal to empower the world, maximize research and learnings, and monitor all uses of their AI systems to ensure a secure and non-threatening environment.

See also  Will ChatGPT Remain Free Forever? OpenAI's Subscription Plans Revealed

Dylan Patel, Chief Analyst at SemicAnalysis, is responsible for researching and gathering pertinent data regarding the costs and efficiency of AI systems, as well as providing the research firm’s clients with data-backed advice and practical solutions.

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