YHK - yet another hacker news clone

Accounting for initial acquisition cost and typical depreciation/failure rate? I assume they already consider colo price, electricity, etc

Fascinating site. Thanks for linking. However note if you change to "open router" pricing in some cases even B200 which is ~2y old has negative margin (and AFAIK ppl are using longer amortization than 2y - I do not know how cost are calculated here though).

Why isn't ordered by profit % per GPU? It' surely the most important indicator

Today. As new suppy comes online and demand levels off, that 38$ will shrink. Absent legal monopolies, the margin will shrink to a sliver more similar to any other service. That is the danger of open source models. They destroy monopolies and shrink margins.

With this new data at hand, I have to wonder how profitable would it be for the investors.

The cost of amortising a datcenter, the gpus, the engineering costs attached all to produce inference.

At how much percentage of its inference would it be profitable (if it does), how much returns would it make over the long term and what would be the profit of those inferences/ profit-revenue and how that compares to other industries.

Are there any statistics which show this. Also how efficient token inference has gotten over time?

Until the Chinese stuff came online I was completely convinced this is just a bubble waiting to pop and nothing will come of this tech due to its absurd costs. The Chinese stuff changed this picture massively. I'm daily driving DeepSeek 4.1 flash (at API prices) and every now and then I give Opus 5.5 a try and don't notice anything missing for me.

My workflow is one where I am heavily involved in the process and I don't like giving the agent too much autonomy + I need to follow along. Opus 5.5 speak is still cause aneurysms for my brain. It also has this vibe to its prose... like it's looking down on me. This is Prolly my bias speaking though.

I can't read the original Chinese text, but if it's revenue, then you still can't say anything about profitability. Housing and powering one GPU should be way less than $38 per hour, so there should be a profit on inference. If that profit can cover training new models we don't know, but it would appear to not be the case.

For the past year or two I think it has generally been agreed that inference is profitable, but the LLM business as a whole is not.

> hopefully this quells some of the "AI is unprofitable everyone is spending a dollar to make fifty cents" arguments.

I think everyone has known that inference is profitable overall for the major providers for a while (I'm sure not all user accounts, but in aggregate).

The major providers are unprofitable after you add in the training and research costs and other overheads.

This is why the open source models are such a threat if they become good enough - it will destroy the business model of just providing raw chat/intelligence.

Arguably that's also why a few of these providers are trying to diversify into whole-platform approaches rather than just relying on having their whole thing be a great model.

We still need to know if that includes purchase and maintenance costs, which are substantial. And then of course all other costs associated with it.

I don't think i'm paying more than Deepseek price, ever

No, a cluster consists of 4x SM100, and $38 is per card, so after 10 months you already have $1 million.

Of course, you won't have 100% utilization.

Thats like sticking your head in the sand :)

Yeah, but they can't ban the rest of the world from using it :)

Isn’t this a ban on it being used by the state, rather than people/civilians?

No, that isn't going to happen. The US can't afford to lose the AI race and US companies that can't compete will get thrown under the bus and replaced with those that can.

The DHS order only bans buying DeepSeek products directly (e.g. doing inference on their China hosted servers). It does not apply to open weights at all. And DeepSeek just gave their response to the DHS order.

What moat?

Today the closed-source giants maintain a substantial cost-per-token advantage due to proprietary inference tricks like speculative decoding kernels, ultra-optimized FlashAttention derivatives, multi-GPU pipeline orchestration and dynamic prefix-caching algorithms.

I'm not sure whether the DeepSeek's open-sourced inference engine does all of these, but it appears that plenty of inference tricks have just been revealed for the open source community to learn from and start improving upon.

In theory, this can allow a small US lab to compete with O/A/G and take marketshare from them with small open models deployed centrally or (and this is think is the real moat) on-prem for enterprises.

> they do not believe that big corporations in the US or Europe would actually use chinese models

This is exactly right. The only AI that matters from an investor perspective is the AI that is built into your phone, your car, ships with Windows and Mac, and built into legal and business tools like Lexus Nexus and Salesforce.

Everything else is hobby.

There were pretty specific rumors about IPOs happening early this year, up to apparent insider bets on prediction platforms. Don't you think the apparent delays in IPOs could have something to do with the open weights/open source competition? The monetary conditions (interest rates,...) were better early in the year, arguably predictably so, so what would be your alternative explanation why they chose to postpone?

> I think they do not believe that big corporations in the US or Europe would actually use chinese models in their business operations due to security, regulatory and/or business continuity concerns.

European business users (+ consumers) can access GLM-5.3 via Mistral.ai hosting. (with GDPR)

https://docs.mistral.ai/models/zai-glm-5-3

https://legal.mistral.ai/terms/get-started/

If they are open sourced, it means any random data center could host it domestically and AI becomes a utility.

Europe is going to be just as worried about using US models for large parts of government related functions and they're going to feel alot better about open source self-hosted chinese models than US models which can literally get turned off on a whim by Trump. They'd prefere home grown models if they can, but if they can't they'll go self-hosted and that'll be Chinese at this rate.

There's very little understanding of business operations on HN, especially outside the tech echo chamber. The "software will eat the world" mindset.

Based on HN comments, every company would be running Gentoo and developing line of business applications in Haskell or OCaml.

> I think they do not believe that big corporations in the US or Europe would actually use chinese models in their business operations due to security, regulatory and/or business continuity concerns.

They'd be wrong. Especially about Europe. Many companies are in a bind about this: they can't upload their confidential files to a service provider but they do want AI. So right now they end up with a choice: do nothing, breach their contracts, use a service provider that pinky promises not to share their data (which we have already seen they will violate) and run local models.

The latter is the reason you can't buy powerful GPUs for reasonable prices because a local model (GLM 5.3, DS 4, etc) + some hardware is all you need to solve the problem in a way that avoids the legal issues.

> I think they do not believe that big corporations in the US or Europe would actually use chinese models in their business operations due to security, regulatory and/or business continuity concerns.

Well they are wrong, I'm at a large EU startup and they are currently considering Deepseek as their default model, Opus/Fable would still be available for complex tasks.

This isn't true, especially not with the inference engine becoming public, now you can build your on models on the same or similar architecture and ship massive gains on top of these stacks.

I think people don't realize the Salesforce SAP Palantir would love to ship these models, and they have/are shipping them.

The threat of exfilteration of data via these models is high but it's nowhere near as high as the margins.

For instance if you could cut your ai input bill by over 90% would you do it? Can you put some of that extra margin say 10-20% to make it more secure.

I know of atleast 10 fortune 500 companies that are entirely planning to or have moved to Chinese models for a large portion of their work.

It's insane people in finance are delusional enough to think that won't happen.

Maybe Govt will try to de-risk with bans on using open models but I don't think that's going to work at scale, people will literally spawn labs to launder DeepSeek/GLM models into a US model by some novel training etc approach or something.

I still don't think that it's as much a kami-kaze rather it's an attempt to normalize the world around the Chinese stack.

If DeepInfer is supported by everyone, it will be much easier to export stuff aka hardware that only supports it for now or is optimized for it.

Assume china starts selling hyper optimized Asics for inference at some point.

This feels like a race to the bottom as soon as possible because they have much lower costs, if there is a Wipeout in AI Labs in US chinese labs are largely insulated, and they can hopefully buy Nvidia chips for cheap if that does happen.

Yes, the theoretical approach is listed there. But the actual code is what's missing.

Sure, but that size/token is inherent to the model. Part of DeepSeek's hosting advantage, and thus a remaining part of their moat, is migrating KV cache from VRAM to host RAM to nonvolatile storage, and presumably routing requests to where KV cache has migrated. People here on HN say KV cache lifetimes can be 8+ hours with DeepSeek, and I don't know of any other provider that can match that. That infrastructure isn't part of this open-source release.

> The real loser in all this is obviously Europe, which has nothing competitive once the Chinese models are gone.

Those models are open source / open weights. Europeans can continue with Chinese models as a base same like Cursor used Kimi K2.5 a base for their composer 2.0 model or SWE-2 from cognition is post-trained Kimi K3

I would assume DeepSeek is considered one of China's strategic assets and is therefore heavily policed.

The other benefit is that as more of the stack is open sourced we can finally see just how cheaply these models can be served. Which in turn gives us insight into what the frontier labs might be doing.

That, and drive down prices everywhere.

From what I’ve heard from owners (anecdotal) those batteries are still utter garbage.

that doesnt say what cards the reference number used.