Hacker News Reader

Comment by locknitpicker | Hacker News Reader

> (...) it hums at120t/s. It’s really a surprisingly good model for general tasks, and I’m soon to see how it does with more agentic kit.

This is exactly why I interpret the aggressive price cutting by OpenAI on their Luna model line as a clear sign they are scared shitless of local models, and how they can trigger a "the king has no clothes" moment for AI companies like OpenAI.

View on HN

> as a clear sign they are scared shitless of local models,

The number of people with hardware to run a model like that is very small.

It's going to be many years before hardware to run medium size local models is widely available. Running mid-size local models doesn't even register as a factor in pricing decisions right now.

It's such a small part of the market and will be for many years, until RAM and other hardware prices come down.

> The number of people with hardware to run a model like that is very small.

That's not the problem anyone is facing, and analysis anyone is doing. At all.

The problem is the huge AI-related budget that companies who dove head first onto AI are facing.

Therefore the problem everyone is faced with boils down to whether they keep paying the likes of OpenAI a de-facto business tax, or they replace that tax with a one-off CapEx invoice followed by a utilities bill.

And you don't need a frontier model to do most things. In fact, the biggest challenge in using local models in agentic coding is the models supporting these agent-based tools.

> It's going to be many years before hardware to run medium size local models is widely available.

Nonsense. Speaking for myself, my employer already introduced a hard cap on AI credits while rolling out hardware support for local models, and even went to the extents of rolling out workstations with 64GB of RAM as a baseline. Consequently, we are using the likes of gemma4 for a fair share of everyday tasks. This has a transformative effect on your workflows because suddenly you don't ration tokens, and agent orchestration doesn't become a cash guzzler. So far the only constraints are tool support and token throughput, but that's not a concern.

I wonder how this compares to a hard(-ish) cap on Sonnet-and-better models, and unlimited access to Haiku. That’s one of the policies we’re considering.

> The number of people with hardware to run a model like that is very small.

True, but the number of people who pay the non-subsidized API price is also very small, and it's all in big companies who would have no issue buying or renting hardware for that purpose.

Many years is only long in relation to the AI hype cycle. In terms of a human lifespan it is not that much, maybe 10 years at most and that's pessimistic. We technically live in that future already it is just very unevenly distributed.

A human lifespan doesn't have that many decades in it either...

Ram prices suggest there is going to be a mighty glut of the stuff in a couple of years. We may easily see 32->64->128 GB system RAM off the back of that and CPUs seem to be moving to unifying VRAM and main memory.

I don't think running a ~200B parameter model is that out of reach, it might be usual by the mid 2030s for people in the tech world. Factor in the crazy improvements in quality given a number of parameters too.

But why? HBM is not reusable by consumers, and we would probably be at ddr6 by the time used ddr5 comes crashing down.

It won't. There's a high chance these could end up getting scrapped due to "security".

> The number of people with hardware to run a model like that is very small.

But there are a lot of companies who do and rent it out as a service.

Idk, I’m running 180b on $2500 of hardware at 120 T/S (single user of course) but I bought it before the clown show. Now it’s probably more like $10k lol

I expect I’ll be able to run 1/2 terabyte models in 5 years for a couple $K in hardware. Inference is going to be a lot like Bitcoin mining. Only the peak of the power/performance curve is going to be profitable. Which means a glut of surplus hardware that can’t make money but makes tons of tokens.

I hope you are right. I've been waiting for GPU prices to ease off ever since ASIC mining took off.

but the number of small to medium sized companies which really want to run (some of) the AI usage under their control and are willing to drop some 100ks on it aren't that few (especial for non US companies given the state the US is in)

> but the number of small to medium sized companies which really want to run (some of) the AI usage under their control and are willing to drop some 100ks on it aren't that few

To this I'll add companies who don't mind paying for a non-US service provider to deliver the compute required to run local models.

Just like some users are happy to run GPT6 Luna, some users are happy to run gemma4 et al.

At cost of power at $.45kwh here and dual 3090 setup easily approaching 1kw per hour at 120 tok/sec, latest Luna is cheaper per million tokens.

Your electrical costs are likely less than mine though

Yeah electric costs are nil on the microgrid but capacity is limited.

I'm working off a 128gb Strix system on mostly hydro and it's worth it for me. Qwen 3.8 27B and Flash Next, Gemma 4 and MOE models. It Keeps my thinking and methods local while moving at a speed I can actually read and think about.

It can be annoying when you're running something long and realize there is an issue. But hey, I'm not wasting paid tokens. Reset and try again. I've probably thought of a better way by then too.

A dual 3090 system shouldn't go above 600W wall power while running inference, even less with some weights in RAM as you need to do with "only" 48GB VRAM and this model. Unless you are running water cooling, the cards are likely sitting at 200-250W power limit each anyway so they don't run too hot. This is considering a consumer tier CPU and mainboard with about 90W TDP, not a dual CPU or something like a threadripper that can easily push the power draw to 1kw if it runs the calculations on the RAM weights. My dual 3090 setup usually drew about 200W-300W average with LLM workloads according to my HA historical data.

> as a clear sign they are scared shitless of local models

Partially agree and partially disagree. Qwen 3.8 next (120b + 50b ngrams) is at the upper end of a local model, and serving it at good speeds (esp for pp) is not cheap. So no, oAI are not particularly scared that everyone will suddenly start using local models of this size / quality.

I do agree that having these models in the open is great pressure on the price point of "small" API models (like luna, haiku, etc). And that's good news for us consumers and for the field in general - more competition is always better.

And on the cost estimate, the true power of local models is the guarantee of running them on prem w/o any data going out. Price wise it is still cheaper to go API, for small teams.

The "upper end" of local models as of today would be something like MiMo 2.6 Pro or Kimi K3, either of which is significantly smarter than Qwen Flash-Next. Roughly comparable in smarts to the old GPT Terra series, not quite as capable as Sonnet. But yes, running these models locally at realtime-usable speeds and without heavy quantization would be even harder. They can be quite tolerable for plain Q&A chat and great for running multiple unattended workloads on a 24/7 basis, but that may be a bit of a niche.

Yeah, I should have worded that better. I'd say it's at the upper level of what can be ran on plain consumer hw (like a laptop, mac, etc). For ~k3 you'd realistically need dedicated hw anyways. Or a cluster of consumer hw, and that comes with its own problems and drawbacks (esp in pp which matters a lot for agentic stuff).

Kimi K3 has been demonstrated on a top-of-the-line laptop Mac running at several seconds per token (it has to fetch essentially all experts from SSD, since IIRC the dense part of the model takes around ~105GB on its own!). That's impressive enough if you only care about pure Q&A/chat or pure unattended tasks, but it's not what one would expect for agentic work. You'd need quite a bit of additional RAM to have good caching for the most common experts, and even then that would only help so much. MiMo Pro would be a roughly similar story.

> Or a cluster of consumer hw, and that comes with its own problems and drawbacks (esp in pp which matters a lot for agentic stuff).

Prefill actually ought to work well on a cluster AIUI, since it can leverage both pipeline parallelism and batching quite effectively.

Viewed differently, they make it much cheaper to distill their models.

Reply on news.ycombinator.com