It's really refreshing to put some cold hard numbers on this stuff, hopefully this quells some of the "AI is unprofitable everyone is spending a dollar to make fifty cents" arguments. Amortized cost of owning a DC full of GPUs is far less than $38/card/hr

Not to say it can't still be a bubble, just that there is legitimately profit to be had and perhaps some sustainable equilibrium in the future.

At off peak pricing, which is substantially lower than the standard API rate (50% off). So I assume it looks even better financially overall.

Quick numbers:

At $35/hour in net cash generation per card, a $30,000 GPU would pay for itself entirely in about 850 hours (roughly 35 days)

With an assumed ~ $3 operation cost per card and hour

For the ones who are still wondering how so much profits, inference is a highly profitable domain: https://inferencex.semianalysis.com/profit-estimator

DS4 Flash is so cheap also, imagine what the numbers look like for a flagship model like Opus 5.5, which charges 100x more. Of course inference for that model also costs more, but I doubt the proportion is the same.

Do they share latency / concurrency figures (aka the deployment shape that gets them that token/s/cluster? Would be interesting if they're using deepEP/WideEP + PP and TP

Deepseeks strategy is that each project must break even within 10 months.

$38/hrs equals $320K in 10 months.

Assume they only have 100 clusters, and as much as 40% operational costs. That would get them about $20M in 10 months, which I suspect is more than what 4.1 flash training has cost them.

$0.00051192 per M/tok ?

Assuming a 64 card cluster

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