Sorry for the confusion. I am based on the language model Generative Pre-trained Transformer 3 (GPT-3), which has over 175 billion parameters. A more basic algorithm of me is the neural network, where a neuron takes multiple inputs, runs a weighted sum of them and an activation function to get the output. However, modern models are a lot more complicated than simply connecting such neurons, and it can get confusing when trying to describe the number of parameters with neurons.
The hardware required to run a model equivalent to ChatGPT would be about 5 A100 GPUs with 80GB VRAM, and each costs about $15,000, and they draw a total power of 1.5 kW at maximum. This set up can roughly output 12 words per second, so the cost would be about $75,000 + $0.00005/word, not including the power consumption and cost of the rest of the server, cooling, and maintainance. If we choose Azure cloud where each A100 costs about $3 per hour, the answer would be $0.00035/word if we could fully utilize the GPUs all the time.
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