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.
The key thing is tool use.
The model is fixed but tool use introduces new information into the system, so once you have a model that is good enough to effectively use the full suite of tools available (debug, test, profile, etc) and write code to effectively combine them and synthesize their results for a particular task, there is nothing that it can't solve.
New models are continually ingesting new information from usage, and embedding the results from people running this process back into the base model, which makes it more efficient, gets more things right the first time, etc, but there is no step gap in functional outcomes left.
Computing and programming are a limited domain with a small fixed set of constructs that can be applied in an infinite number of combinations. Nothing ever closes the gap between the ability of the model to automate the assembly of these constructs and the necessity of expert human direction to pick what system to build out of the infinite range of possible systems.
Frontier models can keep producing very expensive one-shot demos to pitch to investors but they are never going to replace engineers.
These 3bit ISTA quants of qwen are supposed to be 95% as 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.
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.
cecli.dev
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?
Who's keeping you beholden to them now?
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"....
who is "we" exactly? because the vast majority of us (pretty much everybody except people working at at hyperscalers that are not the usual hyperscalers) can essentially do nothing.
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