| ▲ | simonw 4 hours ago |
| > The kernel work helped reduce the end-to-end cost of serving the model by 20%, while its experiments increased token-generation efficiency by more than 15%. If the cost of serving GPT-5.6 just dropped by 20%, does that add up to literally billions of dollars in savings per month? We know Anthropic spend $1.25 billion renting inference capacity from SpaceX (in two Colossus datacenters) from the SpaceX IPO, but we don't know how much of Anthropic's inference capacity that is (presumably a small fraction, since they were operating on top of AWS and other providers before the SpaceX deal.) I've not seen any numbers that hint at OpenAI's per-month inference bill, but surely that has to be in the multiple billions of dollars as well. So 20% is a really, really big deal. |
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| ▲ | NitpickLawyer 4 hours ago | parent | next [-] |
| ~2 years ago gemini2.5 helped write better kernes for itself and (only) reached 1% efficiency gains. Today we're at 20%. |
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| ▲ | magicalist an hour ago | parent | next [-] | | If you optimize program A and manage to wring out a 1% improvement, and I optimize program B and improve performance by 20%, you can see the problem with trying to infer anything from those two numbers. Edit: searching for the story now, further bolstering the point is that was 1% in training time [1], and the openAI claim is 20% in end to end inference cost. This is a bad comparison. [1] https://deepmind.google/blog/alphaevolve-a-gemini-powered-co... | | |
| ▲ | NitpickLawyer an hour ago | parent [-] | | > This is a bad comparison. How so? First, kernel writing (or ML engineering more broadly) is a highly specialised task. Not everyone can do it. It shows that models are getting better and better at (easily verifiable) hard tasks. And you can "hire" that expertise much easier than you can hire the equivalent meatbags. And more importantly you can "fire" them as soon as the task is done. And then hire them 3 months later, when the new model drops. And so on. Second, 20% gains in inference today gives better end results (i.e. lower overall cost) than 1% in training 2 years ago. Today's models are improving mostly via RL. And RL is highly dependant on fast inference (you want many rollouts for each training scenario). Same for dataset filtering, environment generation, distillation, etc. |
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| ▲ | dust42 3 hours ago | parent | prev [-] | | In 2 years from now we will be at 400%. https://xkcd.com/605/
Also, it is called kernels (you have nitpick in your username) |
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| ▲ | 2 hours ago | parent | prev | next [-] |
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| ▲ | dominotw 4 hours ago | parent | prev [-] |
| imagine writing that on your resume > reduced inference cost by 20 percent saving company x billion dollars per month |
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| ▲ | paxys 3 hours ago | parent | next [-] | | Where are you going to apply to with that resume that’s a step up from your current job though? | | |
| ▲ | petesergeant 3 hours ago | parent | next [-] | | The other place, but for more money | |
| ▲ | bpavuk 3 hours ago | parent | prev [-] | | lots of places, actually. not everyone wants to be attached to the Silicon Valley culture, and that line alone will guarantee practically any workplace. that person is going to find out what work-life balance is :) | | |
| ▲ | paxys 3 hours ago | parent [-] | | Sure, but those places don’t need such lofty resumes to begin with. | | |
| ▲ | speed_spread 2 hours ago | parent [-] | | With the right kind of credentials, it's not about need, it's about want. Flip the roles and let yourself become an object of desire, an aspirational hire. |
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| ▲ | tekacs 4 hours ago | parent | prev | next [-] | | In this case, and I don't mean this critically, I guess it would technically be, "Instructed model to find efficiencies... reducing inference cost by 20% saving company x billion dollars per month." I have no doubt that further work was required to enable this, but it's still very cool to be possible to say that. | | | |
| ▲ | hirako2000 4 hours ago | parent | prev [-] | | Contributed to. Can't be some IC who made a few nice PRs | | |
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