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The whole thing is imaginary: "In this paper, we propose Chiplet Cloud, a chiplet-based ASIC AI-supercomputer architecture that optimizes total cost of ownership (TCO) per generated token for serving large generative language models to reduce the overall cost to deploy and run these applica- tions in the real world."

So they are comparing actual implementations with a theoretical implementation. Never mind that they got the A100 figures wrong, they are still in the 'wouldn't it be nice if we had 'x'' stage. This looks like a paper whose sole purpose is to raise funds for a research project that will probably ultimately go nowhere and they needed a reason that looks good on paper to increase their chances of getting funded. A100 can already be had for $0.87/hour so even their theoretical advantage is under significant pressure and assuming they got everything else right by the time the project has run the market will have overtaken them. This is what usually happens to CPUs that are application specific.



The $0.87/hour price you gave is theoretical and also we know any price in a paper for compute is wrong by the time of publication.

Pragmatically the prices are closer to $2/hr according to this recent post here on Hacker News: https://llm-utils.org/Nvidia+H100+and+A100+GPUs+-+comparing+...

Although again prices change on a daily basis on spot providers.


> The $0.87/hour price you gave is theoretical

https://cloud.google.com/blog/products/compute/a2-vms-with-n...

That's as close as I got to verifying that price.


Another list that shows pricing both constant and spot. The best GCP spot price is $1.1, but Jarvis seems to say its spot for the 40GB A100 is $0.69:

https://fullstackdeeplearning.com/cloud-gpus/

I feel there are more fair criticisms of that paper than its inclusion of the snapshot price of variable priced compute resource.


Sure, to me it more of an extra item than the main one but it is one that you can readily verify because most of the other claims are far more vague. If they're willing to fudge on that one then I have much less confidence in the rest of their claims.


Seems like HN comments have determined that the cost number is not fudged..


Yeah, it is an architectural simulation study, this is what is usually done right at the beginning before resources are allocated to go deep on idea. So in that sense it is imaginary; but this is how new ideas get incubated.


take 3 ideas that are hot: chiplets, cloud, and LLM - remix them into the title of a paper that describes a hypothetical machine.. academia playing catch up and trying to stay relevant in my cynical eye.


Using ChatGPT


I asked gpt for giggles and the comparison is much more thorough, it has written also power per watt improvements, benefits of denser packing, and sustainability of moving toward more energy efficient solution.


I did the same exact mind exercise using ChatGPT but I haven't produced a paper out of the chat session.




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