Comment by schleck8
16 hours ago
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
DeepSeek CEO has previously said that their finance team considers the optimal payback period for hardware purchase to be 10 months. That is, 10 months worth of revenue should be equal to the purchase price of the hardware. This doesn’t cover operational costs such as electricity. This is their method for API pricing.
Source: https://x.com/FuckAnthropic/article/2080268590033621295 (it’s a leaked meeting transcript so take it with a grain of salt)
Its not 10 months worth of revenue should be equal to purchase price. He is saying 10 months margin will cover the hardware cost, so revenue is ofcourse going to be higher, quote below.
"This is our API pricing. For our API pricing, we consider a reasonable profit to be roughly: we buy a batch of equipment from the market, and recoup the cost in ten months. I think that's a reasonable profit."
Isn't electricity a pretty major factor even over such a short fraction of the hardware's lifetime?
No, the absolute max power draw of these cards is like 1.5 kW, say 3 kW per card with mediocre cooling attached to a server. DCs get power in the 10c/kWh range at most. So 30c/card/hour, peanuts. And this is the most power hungry card at full tilt with mediocre cooling efficiency.
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Your estimation feels too high. It implies >1000% ROI on a GPU card (= $300k gross annual profit on a $30k card).
As a reference point, Z.ai is operating at 26% gross margin as of 1H26. So assuming ~90% gross margin is likely a bit off.
No, those $3 are on the high end. If you do not factor in hardware depreciation, at China's low electricity prices (largest variable cost and China has a surplus larger than many country's entire consumption) and with GB200 which is one of DeepSeek's main platforms, the operation cost is actually less than 50 cents per hour most likely. If you factor in hardware depreciation this rises significantly but almost certainly stays under 3 usd per hour, probably closer to 2.
Orinally it was an educated guess based on rental prices I've seen but Opus 5.5 and Gemini 3.8 Flash both arive at the same numbers based on DeepSeek's Zhihu post and GB200 specs. Around 30 cents before depreciation and $2 to 3.50 including it.
I'm curious why such a large gap exists between our numbers. My hypotheses are:
A. The average utilization rate of inference GPU cards is not that high (i.e. it peaks during the daytime, but is idle at night). So the time window for earning $38/h is fairly limited.
B. They are renting costly GPU capacity from 3rdparty hyperscalers because they can't meet the peak demand with their own hardware.
C. Free-tier users are eating the gross margin.
D. Subscription users are eating the gross margin.
Another observable point is that DeepSeek's company-wide gross margin was reportedly 44.6%. It's intriguing what the full picture looks like here.
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How is the math so good for data centres but so bad for home gear? A Mac mini capable of running these models ends up at around 10 years of subscription costs.
Serving LLM inference is more efficient with more concurrent requests. For a single inference request, the bottleneck is GPU memory bandwidth, as the inference engine spends most of its time waiting to move values in memory. With concurrent requests, it's possible to service multiple requests in a single pass through the model weights, meaning you can utilize more of the GPU/APU's compute.
So you have to price your local hardware against however many subscriptions you would need to run a dozen concurrent agents around the clock.
But even then, I would guess subscriptions are priced on the basis that they don't expect every user to utilize their entire usage limit. I.e. power users are subsidized by casual users.
All of it is really about scale, same reason as a single person you can't sell french fries as cheap as McDonalds.
Couldnt they though? A medium fry at mcdonalds costs about $4 and you can buy 5lbs if potatoes for $4.
I think its more like 40 cents to make at home. So I think its quite unlike McDonalds.
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Because the datacenter setups serve multiple users concurrently while your mac mini only serves one. You would also lose money hand over fist renting/buying 4xH200s just to serve deepseek flash to yourself.
These datacenters run the GPU's close to 100% usage 24/7. The Mac mini at home probably has <25% utilization. And then there's economies of scale, wholesale discounts, etc.
The Mac mini GPU also isn't as optimized as the datacenter GPUs
You can batch calls if you're serving a lot of people at once.
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I want to understand this more - are you saying "would pay for itself" if you are offering chatbot commercially? Or using it yourself in lieu of ChatGPT etc..?
I still struggle to wrap my head around a $30,000 GPU card. But impressive payback numbers.
I just installed a $16k GPU at home - super pricy to be sure, but when you price it against hiring 4 junior developers working around the clock it's a steal
What if you compare it to an OpenRouter account and $0.50 per million tokens?
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Could you explain what your infra set up looks like and what is your typical use case, if its more than a hobby?