3 ms·
It's kind of insane how having this tech at this speed 5 years ago would have probably still been seen as insanely useful and revolutionary. If LLMs were more c
by schmorptron 24d ago
It's kind of insane how having this tech at this speed 5 years ago would have probably still been seen as insanely useful and revolutionary. If LLMs were more capable but dramatically slower, I wonder how it would impact how we use it? Dramatically more thought being put into prompts, much more preparation probably
- IgorPartola 24d agoMy parents learned to program on punch cards. They told me it was a day of preparing the program, an hour of running it, just to get a syntax error.
- deleted 24d ago[deleted]
- bluedino 24d agoWrite the program, punch the cards, send the cards to another building to be loaded, program runs, printout comes out in another building, somehow this takes 2-3 days
- lurker919 24d agoCoding is the new punch card slots now. My children will listen in awe about how typing and testing used to take hours or even (gasp!) days.
- redox99 24d agoAt 1t/s it's still faster than humans for a lot of tasks, basically doing overnight what could take humans half a week. Plus you can always parallelize.
- Izmaki 24d agoThis is what people forget when they see slow performance: at 1 t/s it's still roughly the equivalent of having another person work for you at no extra cost besides the initial purchase/sign-on-bonus. Frontier models are amazing, but what will really be useful for us is having models and hardware so efficient that you can run useful LLMs locally. One of my favourite LLMs to this day is still my jail-broken gemma4 12b because it's small enough to run on my computer, but also 100% local and free as in liberty.
- mhaberl 24d agoI would agree, but I want to add that I have real issues with combination of opencode plus slow inference (4-5tok/s). I get weird interruptions. I can only guess its related to some kind of timeouts in the harness or something. Its not a problem of the model of course, but it seems impractical atm. I wonder if anyone else had this kind of thing happening.
- redox99 24d agoI think pi handles it better
- ygjb 24d agoFor OSs harnesses this seems like a good thing to point a paid model at fixing if you don't want to dig in yourself?
- mhaberl 23d agoI might do that myself in the end checked the bug reports and PRs first though, there are a few related, nothing merged yet I ran opencode task again last night since it's slow and this morning i got a "SSE read timed out" logs show tokens still streaming in steadily (every 3-4s, 46,818 tokens in) right up to the moment opencode disconnected opencode cut a response that was actively generating (not a stalled one)
- mhaberl 22d ago
- Capricorn2481 24d ago> At 1t/s it's still faster than humans for a lot of tasks Which tasks? I think you're underestimating how token hungry current proposed workflows are.
- redox99 24d agoAnything you do right now? A typical 10 minute prompt "simply" becomes about 7 hours long. (40t/s vs 1t/s).
- Capricorn2481 24d agoI don't really do anything at my job that could be done in a single prompt, and certainly not something that would take me 7 hours. A 7 hour task would take multiple iterations with how LLMs are right now. You said it would do overnight what would take a human half a week, so I'm curious what tasks you are doing where AI is 3x faster than you even at 1t/s
- redox99 24d agoYour prompts are probably very underspecified then. Frontier models one shot the majority of my prompts. UI is kind of the exception, there I do have to ask for a lot of tweaks.
- Capricorn2481 24d agoMaybe. Do you have an example of a prompt you've done recently? I don't think it's valuable at all to try and craft a specific prompt to "one-shot" a task, I'm way faster just doing small asks and guiding it. At 1t/s, how would you even know your prompt was insufficient? It would take you 7 hours to see it.
- redox99 24d agoStuff along the lines of implement controller service and tests for the following endpoints: - list of many endpoints with the JSON they receive and return and description of what they need to achieve Stuff you could probably do in a single work day if you lock in and enter flow state, but in a typical job takes like half a week. And the vast majority of times the AI one shots it with no bugs, where I would have copy paste errors or dumb stuff I'd need to fix before it's shippable.
- batperson 24d agoThe future of inference is likely in ASICs, so we'll get the inverse, a bit less capable than frontier but super fast models. Like this 14k tok/s beast https://chatjimmy.ai/ https://chatjimmy.ai/ from Taalas (who got acquired by AMD recently). GPT-6-astra runs at like ~40 tok/s, I have a hard time imagining what could be accomplished with that type of model at 10k+ tok/s when in the hands of the public. Will certainly make cybersecurity a challenge for older systems.
- domhudson 24d agoThis is incredible! Are there other big players in this space (freezing models to silicon)?
- HeWhoLurksLate 24d agotake a look at Cerebras, who are doing wafer-scale compute
- timcobb 24d agoI imagine Astra is/will soon will be on Cerebras?
- SPascareli13 24d agoLike how crypto used ASICS but then didn't because the scaling of consumer hardware made it obsolete?
- KumaBear 24d agorunning my own locally. I just set the tasks to start when systems go idle over x. Read and copy only to external drive projects, codes, ect for review. I review the reports the changes and apply them myself or correct them. Is it slower than say throwing it into fable yes. But I don't have to be monitoring it 24/7
- mitxela 24d agoThat's because 5 years ago it was still brand new for a computer to be able to speak English. 5 years later, we have accepted that LLMs can speak English and we expect them to do useful things.