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Comment by riknos314 | Hacker News Reader

Deepseek prices their models based on a 10 month hardware payback period. They report ~$38 revenue/GPU-hour on V4.1-Flash clusters.

Inference is clearly profitable. The question is whether it's profitable enough to justify the training research costs.

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Are those statements audited or fact checked? Inference profitability is great but how much of that profit gets eaten up paying back training costs, R&D, and operating expenses.

No, and it’s very rare for Chinese companies to allow independent audits. I would not trust DeepSeek financial disclosures. However from industry data we can see that inference is often profitable. It’s the training which is very expensive. Which is not a big problem for Chinese companies since to date much of their training is just distilling frontier models.

Their models and inference frameworks are open, you can run them yourselves to check how many tokens per day you can generate, and then validate that against their API prices.

Not sure how they can get more transparent than that.

That's conflating different things. Unless you believe you can instantly saturate any amount of GPUs you bring online, 24/7, with paying customers from day one, then theoretical max throughputs on a stack isn't the same thing as revenue generated. Customer acquisition costs are real!

Even if you could acquire infinity custom on day one for zero cost, revenue isn't profit.

My point was really about profitability in general. The inference portion might be profitable but if training and operating costs are very large, then that can make the whole thing unprofitable. E.g. if it costs you $100B to train a good model but you make $0.00001 per inference token, you may not be able to hit breakeven for a very long time.

At best, this tells you v4.1 flash is profitable, but can they really get $38 per gpu hour? Sounds like a lot.

If they keep it running at near constant usage, you'd only need 80-120 million tokens an hour to hit 38$ of revenue (at off peak pricing, half as much for peak pricing).

From their own inference framework docs: "A single cluster currently serves 155 trillion tokens per day, with overall revenue of US$38 per GPU-hour (at valley pricing)."

https://github.com/deepseek-ai/DeepInfer/blob/main/doc/infer...

So this implies they actually hit about double that during peak pricing.

V4.1-Flash is probably quite smaller and much more resource effective than the frontier models. Add to that much cheaper energy in China.

Clearly potentially profitable. Not clearly profitable.

How much did it cost to train Deepseek v4.1 flash?

Great question. Nobody knows.

The cool thing about models designed for cheaper inference is that post-training (which often requires inference as a part of the feedback loop) becomes cheaper as well, so almost certainly cheaper than models that are less optimized.

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