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points by segmondy 4 days ago | 0 comments

My local model never says no. I never get banned. If I want to do security stuff, reverse engine, there it goes! Do I wanna chat about politics? No problem! Do I want it to analyze medical stuff? I don't get to hear that it's not a doctor and I should call 911. Do I want to explore legal docs, no problem! There are lots of things that cloud models will say no to! I read that don't even agree to help with advanced biology and chemistry topic much. I also never run out of tokens, no 5 hr limit. I can run it 24/7. I don't run out of tokens. I can run 16 sub agents at the same time, non stop! 1-4 billion tokens a month? No problem. Any agent harness I want! No restrictions. Privacy all day. My own true system prompt, not something someone has determined, no filters. Furthermore, there are many open weight cloud models that don't have equivalent online. For a while before cloud models got good at math, the best model at math was DeepSeekv2 Math and there was no API. The only way to use it was to run local. All sorts of audio models, image/video models, etc. No multiple subscription needed. Private UI for my family, kids included (I can guard what kids infer). Got a friend that really needs some tokens? I give em access through private VPN. Telling someone that they are paying for decades of Claude for local AI is like telling someone they are paying for decades of public transport when they buy a car. Sure, you are right, but ...

[–] Gareth321 4 days ago

Also no political censorship, and it doesn’t report you to the police when it suspects you did something illegal. That really happened, by the way.

Now I want a local LLM server :( $25k is just so much money.

[–] ThunderSizzle 4 days ago

If you already have a machine, throwing an r9700 in it is one of the cheapest ways to get qwen3.8 27B Q6 with 100k+ context working.

Sadly, the r9700 has gone up in price, but it's still the cheapest route. I use It on an old x99 Linux desktop and it achieves 50 tps decode and 1000+ tps prefill.

[–] Gareth321 4 days ago

Thanks for the tip! I've already got a home server set up. Maybe this is my next AI purchase. Still, US$2,600 here in Europe is not cheap :-/

[–] ThunderSizzle 3 days ago

I was hesitant, so I went with a hassle-free return route when I bought mine.

My stack uses a local llamacpp ROCM build, modified by https://github.com/stew675/llama-cpp-rdna-boosts

The build uses my machine's ROCM and I just link it instead of embedded all that in an image, like the published ones do.

Then deploying it via docker compose to my local machine. The models I want to run are stored in a docker volume, so it's easy to switch the model while keeping the same build.

The biggest negative for me is that I don't think it's enough to do parallelization through llamacpp (I'd prefer to work on one thing faster than two things slowly). I haven't gotten vllm working yet. Also, once you have one, you constantly start thinking about a second card...

[–] jacquesm 3 days ago

That's true. Now let's reverse things a bit, we go back five years in time and I give you 'magic in a box' for $2600. Would you buy it?

[–] Jnr 4 days ago

No political censorhip? Try asking Chinese models about Taiwan.

Some censorhip and bias towards certain ideology is baked into those models.

While it is not a problem for programming tasks, I would never use it for certain other tasks.

[–] Gareth321 4 days ago

> No political censorhip? Try asking Chinese models about Taiwan.

Self-hosting means being able to use abliterated models where the superficial instructions like not describing Taiwan as independent are burned off.

Censorship and bias are related but not synonymous. I agree that it's harder to remove political bias when the training data includes it. We know many of the frontier models are heavily trained on Reddit, for example, [which likely explains why we see such heavy left wing bias.](https://trakkr.ai/bias/findings)

More citations:

https://www.gsb.stanford.edu/faculty-research/working-papers... (article: https://news.stanford.edu/stories/2025/05/ai-models-llms-cha...)

https://www.tandfonline.com/doi/full/10.1080/29974100.2026.2...

https://ieeexplore.ieee.org/document/10817610

https://www.washingtonpost.com/technology/interactive/2026/0...

https://link.springer.com/article/10.1007/s11127-023-01097-2

[–] schiffern 4 days ago

  >abliterated models where the superficial instructions like not describing Taiwan as independent are burned off

Does this help with issues like including security vulnerabilities when you say you're in Tibet? I understood that this was burned in at the training level, not just a layer of censorship added at the last minute.

https://www.crowdstrike.com/en-us/blog/crowdstrike-researche...

[–] Gareth321 4 days ago

It could, but it's an inexact science. A little bit like tampering with the brain. Even a surgical procedure removing a tiny tumor can interfere with critical pathways. Usually abliterated models end up almost exactly as capable in benchmarks, but it requires the person doing the abliteration to anticipate or know about the censorship. In the case of Chinese model censorship it is believed that it is post-training, so the underlying data input is not the issue. It's how the model has been trained to use that data. Specifically, abliteration usually targets the refusal behaviour. I would guess that this example could be significantly improved, but I have not seen any abliteration attempts re this specifically.

[–] Jnr 4 days ago

All good points, but I am not sure who I trust less. Chinese companies or random people on Hugging Face. :) It is still a black box. And I have a feeling I could not abliterate those models on my own hardware.

I am not saying I would not use any of them at all, but I would avoid doing certain tasks with them. And with the testing I have done with abliterated models in the past, it is not 100% successful and it can still hit the same walls as original model.

[–] Gareth321 4 days ago

I agree. I think the future for privacy conscious people is going to be an adversarial open weights model, where competing models (ideally from opposing superpowers and labs) critique each other's work to find consensus. Or at least alert for suspicious behaviour.

[–] xeyownt 4 days ago

So... the Super Intelligence is Democrat?

[–] scotty79 4 days ago

After years of trump any intelligence is a democrat.

[–] pluralmonad 3 days ago

The only winning move...

[–] scotty79 3 days ago

Is to deprive yourself of the slightest chance of winning?

[–] deepersprout 3 days ago

> No political censorhip? Try asking Chinese models about Taiwan.

In open models you can "uncensor" them: https://arxiv.org/abs/2406.11717

[–] _ache_ 4 days ago

I'm building a local LLM server myself. For about 8k€ you can have 170tps, ~6000 prefill.

A lot more with Haiku equivalents (Qwen3.8 27B) or gemini. It's not frontier, but not that far away.

[–] Gareth321 4 days ago

I would love to hear more about your setup. Which hardware and models? How much context can it handle? How does it compare to frontier models?

[–] _ache_ 4 days ago

I mostly run Qwen3.8 Flash Next on 2xR9700 (but Intel Arc Pro B70 may be better currently, or 4x3090 is even better but out of budget, 32GiB Nvidia GCs on ebay are also usable). I have 128GiB DDR4 (3200Hz), but 64GiB is enough, even 32GiB maybe, I just reuse it from an old project.

You may need a AMD EPYC processor and associated motherboard, don't forget a good PSU at least 1200W (300W processor + 400W by card to support spikes of consumption). On some setup (~15k€) you can use 4 CG to go up to 128G of VRAM.

I'm not using a EPYC processor for now, so I get 170tps on decode but very slow prefill, the bottleneck is the PCIe exchange between the cards, I need to setup P2P (it means what you think it means, for GCs).

My setup supports full context (2*18 bytes so 256K), I didn't setup YaRN to get 1M of context (2*20 bytes), but it's an option with 128GiB of RAM, the visual component is extracted from RAM so a little slower. I'm not using multiple agents but some people does, it's god like with 4 graphics cards, on 2 only, it's relatively quick but limited to single user and up to 3 to 4 concurrents requests only.

It's not frontier, but look AA of QFN, it's good enough. DeepSeek V4.1 can run on my setup, but it's slow. On dual B70, there is some PoCs to make it usable (>60tps). It improves every days.

If you need more information, look for local setup users on the Web. There is a lot of people sharing information about benchmarks and setups. L80 is my reference for now.

Edit: I translated the unity.

[–] ashkankiani 4 days ago

Why do you keep writing Gio for gigabytes? I guess maybe GiB for gibibyte, but Gio is new to me

[–] _ache_ 3 days ago

I'm sorry. Yeah Gio is only in french, I forgot to translate the unity.

octet => byte, https://fr.wiktionary.org/wiki/Gio

[–] jacquesm 3 days ago

You're Blue Book compliant ;)

[–] ashkankiani 3 days ago

oh neat, I didn't know that. makes sense, thanks

[–] ali_chherawalla 1 day ago

You may not need $25k. Off Grid AI runs open models on the desktop/laptop you already have and picks ones that fit its memory, free and offline. Bigger ones run on your desktop and you can use them from your phone without any cloud servers in between. either your wifi or a tailnet like tailscale

[–] cft 4 days ago

MacBooks with large RAM and optimized for local models should be Apple's medium term goal.

[–] Gareth321 4 days ago

It's a good first step. They're not great but they kind of work for many tasks. I think the small models can be useful in context-specific tasks but the friction is high right now because the user is forced to determine whether Siri is good enough, or a better LLM is required per task. Ideally Siri would be smart enough to intelligently route. Apple claims that's how it works but even their cloud processing is far behind frontier models.

Worryingly, they're taking a walled garden approach, and they don't want to expose user data to competitors. After the monumental failure of Siri over more than a decade, I don't trust Apple to execute competently on AI.

[–] charcircuit 4 days ago

>and it doesn’t report you to the police when it suspects you did something illegal

This is an alignment issue. OpenClaw with a local LLM with an email you setup for it could contact the police.

[–] Gareth321 4 days ago

It will not do that unless you specifically teach it to do that, or you use an Anthropic model. The issue in this case was Anthropic's safety monitoring built into the model. They record everything and flag potential illegal activity for human review. This occurs even over the API. I do not know if OpenAI does this, but I would not be surprised. I think the Chinese models are far less likely to do this (unless one resides in China).

[–] ceejayoz 3 days ago

> It will not do that unless you specifically teach it to do that…

Sorry, you're claiming local LLMs are somehow deterministic and never show unexpected behavior?

[–] Gareth321 3 days ago

I’m suggesting it’s highly unlikely it will call the FBI to dob you in. That affirmatively trained behaviour, not something one should expect to accidentally happen.

[–] ceejayoz 3 days ago

Why would you assume an AI model's training set doesn't have things like "you should report crimes to law enforcement" in it?

[–] InsidiousWombat 4 days ago

renting GPU off cloud is also an option

[–] jacquesm 4 days ago

Exactly, that's the spirit. I pool the resources with some friends as well.

[–] Forgeties79 4 days ago

I think both of y’all glossed over the $25k of it all pretty quickly. This isn’t remotely an option for anyone.

[–] bityard 4 days ago

You see it as money spent, others might see it as an investment. OP doesn't necessarily say exactly what they use it for, but if education is the goal, $25k and spending your evenings learning about AI theory and tooling is way cheaper than a university degree and can easily result in millions more dollars in lifetime earnings given the current early state of the technology.

Besides, I regularly see people here in HN spend a LOT more than that on hobbies and depreciating assets. A few years back, people with coastal salaries and tech jobs were tripping over themselves to spend $100k on a certain brand of trendy electric car.

[–] Forgeties79 3 days ago

I can’t “invest” $25k into an LLM. The vast majority of people can’t. And I’m not spending $5k to timeshare it with 4 other people or whatever lol I currently make do with a 9070 + 32gb of ram.

[–] solyin 4 days ago

AnyONE. But if you got some friends, and they got friends, then that $25k becomes smaller and stomachable. My favorite rumor out of LA is groups of friends with a Lamborghini Urus that they timeshare. No idea if it's true, but if car people can get together to buy/lease a car that's MSRP is $345k, I think $25k should be an option for anyone with friends. (Hypothetically. I've read a lot about this friend thing on the Internet!)

[–] Forgeties79 4 days ago

If 10 people are sharing it the speed advantages suddenly drop drastically. That’s $2500 a head. 5 would be $5k. Then all the rules and such you need in place

This is all this stuff that dries people to pay for frontier models. Even running modest models requires a few thousand, unless you just want really lean ones. People don’t need frontier but it’s not cheap to effectively run, say, a 16gb Qwen 3.6 or 3.8 in the 30b range. Ram is like $15/gb and GPU’s with 16gb vram are hardly cheap.

[–] dekhn 3 days ago

In 1993-94, I upgraded the RAM on my home 486, which started with 4MB. I doubled that to 8MB for $250, then to 16MB for another $250, and finally to 32MB (the max for the motherboard) for another $250. At the time, I was a poor undergraduate and had to give up other pleasures for these expenses. If I estimate correctly it's about $30K/gigabyte (only high end servers at the time had a gigabyte, and they would often cost $100K or more).

The lesson it taught me is that swapping is slow and that if it happens during your software development, you're much less productive. When I finally maxxed out the RAM on my machine, I could run linux + emacs + X11 + bash + g++ all at the same time, making me about 10X more productive. It's an investment and lesson that has helped me build my career over decades.

[–] wtch98 3 days ago

In the 90s we'd pay $5k a head (inflation adjusted - about $2k then) for a 486 with 80MB of disk space

[–] Forgeties79 3 days ago

Yes 30-40 years ago when home computers were still a pretty new technology you paid a lot of money just to have them.

[–] wtch98 3 days ago

And all sorts of companies, from small businesses to multinationals, and a fair few people at home, paid it.

Businesses were quite happy to spend $5k on technology for a worker if it makes things better, why wouldn't they today.

[–] Forgeties79 2 days ago

Unless I read it wrong, it sounds like this guy did not get it paid for by his employer - he runs multiple companies and is an angel investor with clearly deep pockets - and I have people talking about it being an investment in their careers. He could be using it support his businesses, yes, but that needs to be clarified and is still not a situation most people find themselves in.

This is not a straightforward business expense conversation IMO. Otherwise yes I do agree with you. A company should invest in supporting local models for their team.

[–] sailfast 4 days ago

But I don’t need my LLMs part time? And we all live in the same time zone.

And what happens when the girl at the bar figures out that you TIMESHARE that Lambo?

[–] xeyownt 4 days ago

You timeshare the girl?

Ok taking the :door:...

[–] fcarraldo 4 days ago

Given all of the discourse about what’s happened at Cornell, you really thought this was a worthwhile comment to make?

This sort of casual misogyny is at the root of the problem. Shame on you.

[–] segfaltnh 4 days ago

Its a drop in the bucket for many businesses though.

[–] dekhn 3 days ago

Anyone? I know many people in Silicon Valley, Boston, Seattle, New York, and elsewhere who would consider that a reasonable expense if it kept them viable in the job market for a few more years.

My entire career has been based on purchasing the most hardware I could afford at home and using it near its peak. I've given up other life pleasures (dinners out, nicer car/housing) several times to max out my computational capacity.

Also hardware like this, if chosen carefully, often has a viable lifetime over 5+ years

[–] Forgeties79 3 days ago

I’m not spending $25k to “stay in the job market.” I come from film production, I’m no stranger to owning my own hardware. But that’s ridiculous. And at least people rented my gear.

[–] elevation 3 days ago

> My local model never says no

In case a future user wants to know what a 2026 model would refuse to do, I needed to rotate dozens of passwords last night both on the server and on the clients. When it was time to verify that the rotated credentials worked, I asked for a script that would rate limit the credential checks. Claude/opus5 (opus!!) refused and stopped in its tracks.

In another instance, Claude was making great headway bringing a legacy Windows app up in WINE until it’s saw “auth” somewhere and shut down. In the shutdown state, Claude asked me to reword the prompt to see if I could get it to act on less dangerous instructions. But when I reworded my request, narrowed the scope to be clearly unthreatening, or even redirected the model, it’s still rejected me. I suspect Anthropic uses the less suspicious rephrasing as training material to increase their rejection rate.

[–] latentsea 4 days ago

> My local model never says no. I never get banned.

So much this. Plus everything else you mentioned. The freedom and control is real.

[–] dekhn 3 days ago

I agree the local models have many nice properties, but I would not be surprised if people start to recognize that Gemini pointing you at medical services instead of attempting to diagnose you is actually a good thing in many cases (and Gemini often does do some diagnosis anyway).

[–] ceejayoz 4 days ago

> My local model never says no.

> I don't get to hear that it's not a doctor and I should call 911.

https://en.wikipedia.org/wiki/Darwin_Awards

[–] mik1998 4 days ago

It's pretty easy for a reasonable person to determine when to call 911 and when to ask an LLM. Typically when I ask an LLM, it is for something I am not willing to escalate further at the moment.

Anthropic has a significant interest in reducing liability, which in this case means mostly blocking this use case.

[–] ceejayoz 3 days ago

> It's pretty easy for a reasonable person to determine when to call 911…

Unfortunately, it's fairly tough for someone to determine if they are themselves a reasonable person.

A model that only ever says yes is a cognitohazard, IMO. Like being a billionaire without the money to insulate you from the consequences.

[–] pluralmonad 3 days ago

Saying yes is not equal to "provides the requested information." If you use models for affirmation instead of information, this is a more reasonable concern.

[–] ceejayoz 3 days ago

"I don't get to hear that it's not a doctor and I should call 911."

Sometimes, the requested information should be accompanied with a note like that.

[–] segmondy 3 days ago

Have you heard the cost of health insurance and seeing a doctor? Some people don't see going to hospital as an option. Better for them to have a local model than no options. But let me tell you how I use it for health, I take all my health records, lab results, every thing, I throw it in there for a second opinion, it explains things to me, it suggests questions I should ask on follow up, possible options, etc.

[–] ceejayoz 3 days ago

> Have you heard the cost of health insurance and seeing a doctor?

Yes. I've also heard the cost (death) of people falling into pseudosciency rabbit holes.

[–] JTbane 3 days ago

The security research is probably the biggest reason to use local models as it seems like the big service providers will ban you for life if you even hint at doing that.

[–] charcircuit 4 days ago

>I don't get to hear that it's not a doctor and I should call 911.

Local models can do this and with tool use they can even call emergency services for you on your behalf.

[–] sharmajai 4 days ago

A cloud model can also get neutered anytime and/or start using more tokens while charging you the same or more per token.

[–] jack_pp 4 days ago

isn't it the same using the same model you host locally but through a cloud provider? does the $ math favour a local setup then?

[–] segmondy 3 days ago

Cloud models sometimes get degraded. You can trust your local model and know what you are running.

[–] jack_pp 3 days ago

seems like a stretch, especially when you could choose from 10 of them if u feel they're dropping off.

Also, it's not like you're running any full models on local without shelling out 20k+