Using the best estimates we have at the time, using actually-purchasable hardware.

Generative Pre-trained Transformer 3 (GPT-3) is a large language model released by OpenAI in 2020. Like its predecessor, GPT-2, it is a decoder-only[2] transformer model of deep neural network, which supersedes recurrence and convolution-based architectures with a technique known as "attention".[3]

It uses a 2048-tokens-long context[jargon], float16 (16-bit) precision, and a hitherto-unprecedented 175 billion parameters, requiring 350GB of storage space as each parameter takes 2 bytes of space, and has demonstrated strong "zero-shot" and "few-shot" learning abilities on many tasks.

"GPT-3 equivalent" in terms of floating point operations that were needed to train gpt3, as well as space requirements, energy requirements, etc. Algorithmic improvements that make a smaller model as good as gpt3 as an LLM would not count for the purpose of this question.