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submitted 5 months ago by to c/whitepeopletwitter@sh.itjust.works
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[–] 37 points 5 months ago (8 children)

Honestly IDK why companies especially medium-big don’t do this. They could plug in RAG with internal/confidential data and have better results and security. I guess question is what is capital plus maintenance cost of running such infra for say 10k+ employees

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  • [–] 24 points 5 months ago

    I think the issue is also that you need some serious hardware to get good inference speed when your devs are working, but then most of the time this hardware will be under utilized.

    That being said you can get good performance from indie inference farms, at a fraction of the cost of the big US labs. I think it's a great compromise and in a few months the open models will be near parity with opus 4.6 which is really all you need for most tasks.

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  • [–] 1 point 5 months ago

    Because in the feeding frenzy, every company with a product/marketing budget is trying to make the customers pay by the token and companies are doing jack to help "mere mortal" companies get going with this stuff on premise.

    You are right that the technical hurdles are not insane to get this going, but most companies don't know where to begin and there's no huge marketing blitz telling the business leaders this is realistically on the table and here's the company you can call to make it happen for you.

    Even if you overcame that and proposed really how to get going, you will still probably hit the aversion to capex that has persisted since Amazon told the industry that capex is toxic and you really want all your money to be spent on opex. Big companies like Amazon will take on that scary CapEx for you and you're expenses will be nice and just OpEx. Coincidentally, the companies that spend the most on CapEx manage to pull in more revenue and profit than you will ever dream to, but still, remember CapEx is toxic.

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  • [–] -1 points 5 months ago* (last edited 5 months ago) (3 children)

    Probably more expensive than the subsidized costs. Hmm...

    H100 GPUs cost $25k, and have 80GB of RAM. Kimi k2.6 has 1.1T parameters. Assuming 8 bit quantization, would need 14 GPUs to run a single agent at a time (I'm not sure the cloud models use quantization; it could be double). So, $350k per vibecoding dev on GPUs alone. Life expectancy is ~4 years, so ~90k/year amortized. This is ignoring the significant electrical/HVAC cost of handling 10KW of electricity and heat per vibecoding dev (and tons of other costs as well).

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  • [–] 5 points 5 months ago* (2 children)

    Probably more expensive than the subsidized costs.

    Of course, but that's exactly the problem. OpenAI and Anthropic are preparing to IPO, so they must now demonstrate profits on inference. The time to take advantage of subsidized compute is in the past, and the subscription and per-token prices that they offer for inference are skyrocketing, overwhelming the budgets of companies that somehow did not see this bait-and-switch pricing coming.

    per vibecoding dev

    No lol. These same hardware requirements would apply to the cloud hosted models as well, so if that's how it worked, you're suggesting that Anthropic, OpenAI, Meta, and Google have purchased ~14 H100 GPUs per user that they serve???

    That would be literally billions of GPUs, while it is estimated that in 2024, Google's AI division owned only 26,000 H100 GPUs and Meta owned the most H100 GPUs of any company at 350,000 units. These GPUs have very high throughput for inference and can serve many users, because that is exactly what they have been designed to do.

    I’m not sure the cloud models use quantization

    they absolutely do, yeah

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  • [–] 1 point 5 months ago (1 child)

    14 H100 GPUs per user that they serve

    Not per user, but probably decent rough estimate to that per vibecoding dev that is continually running agents 8+ hours/day. Some people's "workflows" involve running multiple parallel agents sometimes or even a significant portion of the time (using the git worktree feature), so I think that's probably a decent rough estimate. I imagine the limit would be serving 10 of these types of "devs." Of course, there's batching and stuff that can be done, but I think it still slows everybody else down near linearly. H100s aren't the only accelerators used for inference; I just chose it as an example. Google has ~5 million H100 equivalent accelerators, Microsoft has 3.5 million, and Amazon has 2.5 million (https://www.networkworld.com/article/4156949/google-owns-the-most-ai-compute-and-it-built-it-its-way.html).

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  • [–] 2 points 5 months ago* (last edited 5 months ago)

    Even so, your numbers are still a tiny fraction of GPU units compared to concurrent users, and the limit you "imagine" is just that, imagined.

    And you do need to remember that the majority of the compute at these companies is used for model training and not used for inference.

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