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Local AI needs to be the norm

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Re: Local AI needs to be the norm

#311

Post by binyu »

segmondy wrote: post #300
root_axis wrote: post #290

Earlier quoted context omitted.

You are greatly underestimating the hardware requirements for productive local LLMs. Research consistently shows that parameter count sets the practical ceiling for a model's reliability. Quantized models with double digit param counts will never be reliable enough to achieve results in the realm of something like Opus 4.6.

Jokes on you. We are already running Deepseekv4Flash, Mimo2.5, MiniMax2.7, Qwen3-397B locally in very affordable hardware. These models are in the real of Opus4.6. For those of us a bit crazy, we are running KimiK2.6, GLM5.1 and more ...

They all still fall short of Opus 4.6, definitely though. They are good but fail on extremely complex tasks, in contrast with a frontier model that will keep on trying until it succeeds or exhausts the solutions space.

Re: Local AI needs to be the norm

#312

Post by yieldcrv »

reisse wrote: post #212
pronik wrote: post #80

They will be, and that moment is not that far off. We've got the progression in place already: first, large data centers could have performant LLMs, we are now firmly in "a bunch of servers with a couple of H100s each" territory, slowly going into "128 GB VRAM on a MacBook Pro or a Strix Halo". Within the next year, the pattern of "expensive remote LLM for planning, local slow-but-faster-than-human LLM for execution"…

> They will be, and that moment is not that far off. It's here, right now. I'm running quantized Qwen and Gemma on a decent, but three years old gaming rig (think RTX 3080 12GB and 32 GB RAM). Yes, it's slow, it has a small context window. But it can (given a proper harness) run through my trip photos and categorize them. It can OCR receipts and summarize spendings. It can answer simple questions, analyze code and ev…

I need to see these proper harnesses

I tried oMLX and OpenCode a few weeks ago and the 65k context window was useless, it tried to analyze a very small codebase before going full on agentic and ran out of context window immediately

I don't have time to tweak 1,000 permutations of settings just re-prove that its not as smart as Opus 4.6

I need out the box multimodal behavior as similar as typing claude in the command line and its so not there yet

but I'm open to seeing what people's workflows are

Re: Local AI needs to be the norm

#313

Post by 33MHz-i486 »

wincy wrote: post #294
root_axis wrote: post #290

Earlier quoted context omitted.

You are greatly underestimating the hardware requirements for productive local LLMs. Research consistently shows that parameter count sets the practical ceiling for a model's reliability. Quantized models with double digit param counts will never be reliable enough to achieve results in the realm of something like Opus 4.6.

Won’t these H100s drop in price in a few years? With the data center build out surely these will become 1/10th the price and you’ll be able to set up a local LLM as good as opus 4.7. Even if the frontier model become more advanced, and memory hungry, you could use the same power usage as your oven to run a current day frontier model as needed? If I could drop $10,000 to have an effectively permanent opus 4.7 subscrip…

opus 4.7 caliber models are trillions of params, and a single instance would likely run on multiple h200s. $100k of hardware. not coming to your laptop anytime soon.

Re: Local AI needs to be the norm

#314

Post by storus »

adamtaylor_13 wrote: post #307

Cool, well let me know when Opus 4.5 level performance is available locally, at speeds that serve everyday use, and 100% I'm right there with you. Until then, I'm going to keep sending my JSON to the server farm in Virginia because it's the only place that can serve me a model that actually works for my uses.

Depending on a task, there are already models matching Opus 4.5. Just not in everything. But you can always swap a local model for a particular task.

Re: Local AI needs to be the norm

#315

Post by hibikir »

ai_fry_ur_brain wrote: post #209
NitpickLawyer wrote: post #159

Earlier quoted context omitted.

API prices are most likely not subsidised. A brief look at openrouter can tell you that. There are plenty of providers that have 0 reason to subsidise that sell models at roughly the same average price. So the model works for them (or they wouldn't do it otherwise).

They are subsidized, heavily. This is simple math, there are lots of reasons to subsidize. Please go look up the hardware requirements to run your favorite model and a given tok/ps then multiple that by 86400 (seconds in a day) then divide that by 1mm and multiple by the $ per mm tokens, then ask yourself if there's any possibility they could be profitable or even close to break even. You are going off vibes alone, t…

The amounts of API tokens many large companies are using through, say AWS bedrock are quite high. We've seen leaks on the bills for real world use cases. It's not unreasonable to see normal individual subscriptions as possibly subsidized.... but do we think someone like Anthropic is going to be subsidizing 7, 8, or even 9 figures monthly bills from megacorps? Because said megacorps will swap out to a competitor immediately, so your subsidy is unlikely to lead to loyalty or anything.

If Anthropic and OpenAI are subsidizing the metered API usage, their model is going to end up just as successful as MoviePass. They are burning enough money on the training costs already.

Re: Local AI needs to be the norm

#316

Post by bugglebeetle »

adamtaylor_13 wrote: post #307

Cool, well let me know when Opus 4.5 level performance is available locally, at speeds that serve everyday use, and 100% I'm right there with you. Until then, I'm going to keep sending my JSON to the server farm in Virginia because it's the only place that can serve me a model that actually works for my uses.

The frontier Chinese open source models are already at this level, GLM-5.1 and Kimi K2.6 specifically.

Re: Local AI needs to be the norm

#317

Post by wincy »

root_axis wrote: post #308
wincy wrote: post #294

Earlier quoted context omitted.

Won’t these H100s drop in price in a few years? With the data center build out surely these will become 1/10th the price and you’ll be able to set up a local LLM as good as opus 4.7. Even if the frontier model become more advanced, and memory hungry, you could use the same power usage as your oven to run a current day frontier model as needed? If I could drop $10,000 to have an effectively permanent opus 4.7 subscrip…

> Won’t these H100s drop in price in a few years Doubtful. The increase in demand is greatly outpacing supply, and all signs point to a continued acceleration in demand > If I could drop $10,000 to have an effectively permanent opus 4.7 subscription today, I would. lol well obviously, but realistically that price point is going to be closer to $100k, with a perpetual $1k a month in power costs.

Cool, thanks for the information. I guess they drive prices down by massively parallelizing requests on say an H100 X8 array? So this is spread across. So if I say, wanted to use it for 8 hours a day in my theoretical world it’d be too expensive. My work definitely wouldn’t pay $100,000 for a server farm even if it’d give an AI to all our employees, you’d have to have engineers, a colocation space, basically all the problems that companies didn’t like and went to AWS for.

Re: Local AI needs to be the norm

#319

Post by tuananh »

binyu wrote: post #309
adamtaylor_13 wrote: post #307

Cool, well let me know when Opus 4.5 level performance is available locally, at speeds that serve everyday use, and 100% I'm right there with you. Until then, I'm going to keep sending my JSON to the server farm in Virginia because it's the only place that can serve me a model that actually works for my uses.

DeepSeek V4 with 1 million token context window is pretty powerful, although still not there. There's hope that Opus 4.5 level performance locally is not that far away.

From what I read, ds v4 is very close with opus 4.6 performance.

Re: Local AI needs to be the norm

#320

Post by MarsIronPI »

codebje wrote: post #266
irishcoffee wrote: post #222

Earlier quoted context omitted.

What’s the rush?

It depends on the purpose for the model. AFAIK LLMs aren't particularly capable at researching answers, relying more on having 'truth' baked in to their weights, so if it takes 12 months to train up a crowd-trained LLM it'll be 12 months behind the times. How serious a risk is poisoned weights? Can we leverage the cryptobros into using LLM training as a proof of work?

What? I use Qwen 3.5 35B-A3B and it definitely knows how and when to do web searches to fill in gaps in its knowledge.
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