	▲	aurareturn a day ago \| parent [-]
		Assuming 80GB H100 and you inference a model that is MoE and close to the size of the 80GB VRAM, you're going to see around 10k tokens/second fully batched and saturated. An example here might be Mixtral 8x7B. You're generating about 36 million tokens/hour. Cost of Mixtral 8x7b on Open router is $0.54/M input tokens. $0.54/M output tokens. You're looking at potentially $38.88/hour return on that H100 GPU. This is probably the best case scenario. In reality, inference providers will use multiple GPUs together to run bigger, smarter models for a higher price.

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drakythe a day ago | parent | prev [-]

3.99 at 8x instances, with a minimum 2 week commitment. Good luck getting 70% usage average during that time. Useful when you're running a training round and can properly gauge demand, not so great when you're offering an API.

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infecto a day ago | parent [-]

Is it not a good penciled number? It helps set the directional tone that at inference cost is being covered.

	▲	drakythe a day ago \| parent [-]
		It says the numbers are theoretically possible. Requiring a 66% usage to break even when 100% usage will piss off customers by invoking a queue means it’s a balancing act. “Technically correct. The best kind of correct”. So inference may technically be _capable_ of being profitable, but I have question’s about them being profitable in _practice_.