Posted by guregodevo 10 hours ago
Of course the proprietary models will continue to improve and we have no guarantee that there will be new open models. But for coding, the local models are already good enough for a vast amount of use cases, if you are an experienced developer yourself and can guide it.
These 3bit quants of qwen are supposed to be 95% accurate as the full model and runs moothly on laptops:
https://huggingface.co/ISTA-DASLab/Qwen3.8-27B-GSQ-RCO-GGUF
There are multiple independent tests of this on YouTube.
I like these quants as much as the next guy when they're all I can run but I have both R9700 with 32GB VRAM and a 5060 Ti with only 16GB VRAM and so I run both Q6 Unlsoth quants and ISTA IQ3_XXS quants and yeah there is an absolute world of difference in realworld performance.
IMHO I think it's pretty scummy behavior from the quant providers to frame these quants this way. Just be honest about where the degradation is really occurring on the non-saturated benchmarks.
Given pace of advance I'm not convinced this is a winning strategy. The models of today (open or not) are way smarter than those of a year ago.
...so we really do need open models to keep pace, not just be statically competitive at a given point in time
So when I really want to solve a problem, I just think on it. And when I need something to iterate through the things I thought of quickly, I use deepseek.
cecli.dev
Your quantum engine aside, current open-source LLMs will not age well. They have a knowledge cutoff. If you ask them five years from now about something that didn't exist when they were trained, chances are they won't be able to provide a useful answer. They may still be useful for many applications, but their value will eventually deteriorate. Having continuous access to up-to-date open-weight LLMs is therefore about much more than simply having access to "better" models.
I mainly just want to point out the value of human ingenuity coupled with a model like DS4.1 is technically enough to get most people to where they want to go, and that includes smarter open models.
and I liked it so much (and saw that the RAM drought was going to last for at least a few years) so I bought a second one to use as a general server for myself and my family.
If expectations were static you’d be fine with qwen 2.5…but nobody sane says that today.
It’s a moving target thus we need open weights to move
DeepSeek and GLM are useful, though. Even the flash variants.
Side-by-side Qwen 3.8 27B is ~6x as fast as DS4 and GLM 5.3 and faster than that if you get the speculative engine set up properly. That means you can iterate very rapidly on the same problem and for many problems this is the way to solve them. DS4 and GLM 5.3 shine when Qwen would take too many turns to solve something, they simply have more world knowledge and seem to home in on possible solutions faster in #tg but slower in wall clock time. If a particular problem is within striking range of all three then Qwen will usually be the fastest solver by far.
It also depends on how good your prompt is, whether you are setting up points for guidance or whether you expect everything to be solved in a single shot.
Wow, that sounds like a lot! Do you use/mean the regular models or flash variants here?
5257408 DeepSeek-V4-Flash-0731-UD-Q8_K_XL-00001-of-00005.gguf
49215492960 DeepSeek-V4-Flash-0731-UD-Q8_K_XL-00002-of-00005.gguf
49700372160 DeepSeek-V4-Flash-0731-UD-Q8_K_XL-00003-of-00005.gguf
49466495968 DeepSeek-V4-Flash-0731-UD-Q8_K_XL-00004-of-00005.gguf
13481997024 DeepSeek-V4-Flash-0731-UD-Q8_K_XL-00005-of-00005.gguf
49842093280 GLM-5.3-Flash-Q3_XL-3.86bpw-00001-of-00004.gguf
49644428576 GLM-5.3-Flash-Q3_XL-3.86bpw-00002-of-00004.gguf
49653319264 GLM-5.3-Flash-Q3_XL-3.86bpw-00003-of-00004.gguf
5694702080 GLM-5.3-Flash-Q3_XL-3.86bpw-00004-of-00004.gguf
20437336576 qwen3_8_27b.ninfer
Qwen benefits in two ways: all of the VRAM is on a single GPU so there is no communications other than generated tokens and new activations, that gives pretty good utilization for the card, the second is of course simply the fact that it is a much smaller model so there is less to compute. The 4090 is still power limited so it could do more but it's loud (and hot) enough as it is.
More details here:
https://jacquesmattheij.com/local-inference-with-large-langu...
https://forum.effectivealtruism.org/posts/53Gc35vDLK2u5nBxP/...
Which is literally the coolest thing to come out of this...
Did they get a letter from Nintendo's legal team or something?
Banning them would just invite competition which is why I think HF is holding out as long as they legally can.
Example: https://huggingface.co/audnai/penclaw-GLM-5.3-abliterated-fo...
Previously, I recall HuggingFace only took models down if someone complained / sent a DMCA or cease and desist.
Punish the consumer, ala the copyright war since the 90s
Unlikely given how trivial it is to DIY this (I think...haven't myself but from afar it seems within avg hn crowd reach)
Especially as people grow more dependent on agents doing all of their computer work.
To use an old analogy, it's not rocket science:
The cost was approximately $80 for generating synthetic data and $10 to create and integrate the LoRA.
This is the kind of bet where I would take the opposite position without hesitation.
The only thing I could see them removing would be models that are specifically trained to do bad things, like a model trained to produce illicit underage image content. And I would be totally understanding of and encouraging of that, even if it makes the free speech absolutists unhappy.
I think the uncensored and abliterated models are more hype than actual threat. The journalists and politicians are too busy going after the AI labs every time someone does something bad and the police can find ChatGPT logs before it. I doubt we’re going to find a situation where someone was running local models and also keeping logs of everything and also commits a crime and also the journalists find it and also they make a story that catches on and so on.
It’s such a niche thing it’s just not a priority. There are some open LLM people who are treating it like a crisis before it happens, but come on. It’s the internet. We can download gigabytes of pirated TV shows despite that industry and lawmakers working hard to stop it for decades. If open models got banned somehow, you’d be able to download them the same way.
... you do know you can download from huggingface and make your own copy, right? And even if you couldn't, you could run heretic (or whatever approach they used) yourself on the official image. It's all just a well-understood process. A decent abliteration run on DS 4.1 flash will probably cost you about $25k.
They're not small but they're hardly out of reach. I have backups on my home NAS.
> very few people have the legit on-prem hardware resources/budget to run something like an abliterated Q8 of deepseek v4.1 flash 1017
It's something like $300-400/day for the raw hardware on RunPod, a little more on AWS. Not for home users, sure, but for a business wanting to eg check its security against the best of what attackers might bring, that's nothing. And it will go down.
Not as bad as you might think. This costs about as much as a decent new car nowadays.
The abliterated 1017 release I've seen is crazy good.
I imagine the various GGUFs are still pretty impressive, but haven't tried them myself.
...which is still tooo much for most people to spend on "AI hardware"....
AFAIK the timeline was:
- AI was shit, models could fit in your GPU
- AI was shit, models could no longer fit in your GPU
- AI got good
Although the same harnessing should also improve Fable's performance. The important thing here is the cap for local performance looks like it could be quite high.
[0] https://store.steampowered.com/hwsurvey/Steam-Hardware-Softw... suggests 16GB VRAM, 32 GB system.
The Bonsai quantizations are not good for real work. I have tried them. They are as bad as you would expect from 2-bit quants. They may have done a good job quantizing to that limit, but it’s still bad.
Even the 4-bit Qwen3.8 27B quants are still iffy for coding work. It’s not until Q5 or Q6 that I find them useful, which is too bad because that cuts into available context on 32GB cards.
> Which in turn suggests that Fable 5 coding performance can be achieved locally with a bit of clever harnessing.
I experiment with a lot of local inference engines and I also use both Fable and Astra. I wish this was true, but it’s just not. I can generate tens of millions of Qwen3.8 tokens per day and run the hardware nonstop, but even brute forcing it like this just can’t accomplish the same thing that I can get Astra, Fable, or even Opus and Sol to get done in an hour or less most of the time. You just cannot make up for the lack of intelligence built into the model by brute force unless maybe your timeline extends to infinity.
The swarm strategy can’t overcome the dumbed down model problem. You just end up with your swarm of agents all going in the same dumbed down loops.
5GB of bonsai weights end up being similar to 5GB of QAT weights even when one is derived from a 27B and the other from an 8B
Happy hallowen Tax Ramltman and Amowhateverwayyournameiswritten...
Actually we tried Cerebras. It caps output at 32K tokens so the 128K reasoning (the author budget) didn't fit. Cerebras speed is real though
Kind of an extension of the effect where you talk to a model, it is super confident (but wrong), you correct it not directly but perhaps with a question and asking it to clarify. This then in turn leads to a better approach overall.
I've looked and there are tons of sites that aggregate models vs. benchmarks but I'm not sure how much of that is trust worthy.
I’m mainly trying to replicate Gemini flash and muse agent for my family of 4 without breaking the bank (already had a 5080 for gaming and the other for image gen).
It is unclear if this memdoor works for agentic tasks, e.g.: “check daily if dune 3 tickets are for sale and if they are buy tickets for 4 where all 4 seats are next to each other in the same row and as center as possible.”
There is a huge cliff once you go past 128gb. Basically you need either Rtx 6000 pros (20k+) or a cluster of dgx sparks (15k+)