This seems like a pretty bad paper. Their headline claim that they are 300x faster than an A100 at serving GPT-3 uses obviously wrong numbers for how fast A100s can run GPT-3. They seem to have misread the DeepSpeed Inference paper and claim that the best throughput on GPT-3 sized models was 18 tok/s, but if you look at figure 8 on page 11 of the paper [1], it shows that they are able to achieve ~74 teraflops on serving LM-175B (GPT-3), which is about 211 tok/s. To calculate TCO, we can say 211 tok/s = 760,000 tok/hour and A100s are about $1/hr, so TCO per 1k tokens using that paper's method is about $0.0013, much lower than the $0.02 that they claim, reducing the claimed TCO advantage from 94x to 6.2x. Combine this with the fact that the paper they used is from a year ago and there are more efficient inference methods than there were then and the speedup probably goes even lower, maybe to 3x. This is without even looking at the chip design itself, whose costs are probably far underestimated.
[1]: https://arxiv.org/pdf/2207.00032.pdf