A.I. wants to control the future. It’s sucking up the resources of the present.
, or RLHF. The former refers to the technical practice of adding more real-word-example data for the LLM’s benefit, so that it establishes a wider berth of knowledge without starting training all over again; RLHF is the means by which a human content trainer assists training, whether by grading certain bits of output or feeding refined data that will help to produce a desired result.
GPTs may have their pre-training, but they still require compute and energy to spit out answers and paragraphs upon prompting. As the, “inference costs far exceed training costs when deploying a model at any reasonable scale. In fact, the costs to inference ChatGPT exceed the training costs on a weekly basis.” Per SemiAnalysis’ own calculations, “ChatGPT costs $694,444 per day to operate in compute hardware costs,” equating to about 36 cents per interaction.A.I.
, the compute cost for the initial training run alone probably summed up to $12 million—200 times the cost of training GPT-2, which only had 1.5 billion parameters. In early 2021, researchers from Google and the University of California–Berkeley estimated that merelyGPT-3 consumed up to 1,287 megawatt-hours of electricity, enough to power about 360 homes for a year—all before you get into the inference.
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