Just wanted to leave a note for folks who might not have the memory to run a big 32gb model - I just found out there are some pruned models that have really good performance and If I had a smaller machine I might try this pruned unsloth Q4 quant of GLM 4.7 flash that sits at 14gb: https://huggingface.co/unsloth/GLM-4.7-Flash-REAP-23B-A3B-GG...
I usually use LM Studio for this type of thing but unsloth has their own studio type app that might be even better suited for these quants.
I used GLM 4.7 flash as my main model for months and it was an incredibly tenacious model and very very fast - I think on restricted hardware, this could be a great choice.
There isn’t any benefit to running a windows machine.
.\llama-server.exe -m ..\Qwen3.6-35B-A3B-UD-Q4_K_M.gguf -ngl 999 --n-cpu-moe 41 -c 262144 --port 8081 --flash-attn on --cache-type-k turbo4 --cache-type-v turbo3 --no-mmap --mlock --host 0.0.0.0 -t 8 -tb 8 -np 1
Haven't had much time to test it other than asking a few questions & changing some HTML in cline so it might be thick as a brick for all I know, but still worth trying
It's like we're mostly treading mud at this point. New editions are released, a version number increases, but I have to wonder if all steps are forward or they're more just tuned differently with similar actual perf per dollar as when this year began.
Most in fact seem to be happening to me with small models. Like your Qwen. Or Gemma 4 31B which is kinda magic especially when considering multilingual abilities. So yes, in that sense I can see "development" probably as we refine data sets and training methods but I see it less on the big hulking beasts with daily limits (unless you turn it up to 11 like Fable).
Edit: As I posted this, I saw a "before and after" comparison for Fable and the reintroduced version is seeing a catastrophic drop in BridgeBench performance as they're still mucking with the model. Go figure... https://x.com/Hesamation/status/2072692225100612032
I wrote up how I run local LLMs, with numbers and a focus on running Qwen 3.6 and Gemma 4. I prefer Gemma 4 31b, even though the general consensus is that Qwen 3.6 is better for code, and it is better on most coding focused benchmarks...it doesn't seem to be for my use cases, Gemma feels smarter. And, with QAT, you get more smarts in less memory, so it's fast and runs on more hardware.
https://swelljoe.com/post/how-i-run-local-llms/
Currently, the sweet spot for self-hosted models is either Qwen 3.6 or Gemma 4, and those top out at 31B (Gemma) and 35B (for Qwen, but you want the dense Qwen 3.6 27B if you can run it as reasonable speed...the dense models are much smarter), so for now, a system with 64GB or 128GB is going to be running the same models. Going to a bigger model doesn't get you better performance because there aren't any better models that are a little bigger. I wish there was a ~70B or even ~120B MoE in the Qwen 3.6 or Gemma 4 families, as I've got a Strix Halo running a model that leaves a lot of memory on the table (and it's not very fast, to boot...an MoE would be faster, and hopefully smarter if it's a much bigger model, like double or triple sized).
In short, right now, 64GB is all you need for the best models you can self-host on anything short of five-figure machines, but, I wouldn't buy any hardware right now, if you can wait a while. Tokens from DeepSeek are so cheap, you can wait out the memory shortage and get access to models you could never host locally. And, OpenRouter always has free models in preview or just because that you can use lightly, as they're rate-limited (but your self-hosted models are going to be rate-limited, too, because a Mac Mini can't run models very fast). Google AI Studio has the Gemma 4 models for free too, also rate/usage limited.
Hmmm
Then I assumed for cost and battery/heat reasons that a Mini would be better than a laptop.
27B dense can fit on a consumer graphics card. Even without getting into various "intrusive" ways to shrink the size of a model (e.g. REAP), something like a NVFP4 quant of Qwen3.6 27b
https://huggingface.co/nvidia/Qwen3.6-27B-NVFP4
should fit within ~22GB of VRAM. So easily on a 5090. It would also fit on a 3090/4090, but iirc they don't have NVFP4 natively, so you would want a different quant for them.
you can see /r/LocalLLama for some discussions. See this (random) post about Qwen3.6-27B on a 3090 at ~100 tok/s
https://www.reddit.com/r/LocalLLaMA/comments/1ujo46r/qwen_36...
Note that it is possible you could still do this stuff with a mac, as there are ways of hooking up a eGPU to macs and using it for inference. My understanding is they're all fairly hacky though, so it would likely be preferrable to just get a 3090 (or a non-nvidia option, e.g. an AMD r9700 pro has ~32GB of VRAM for much cheaper than a 5090.
https://www.reddit.com/r/LocalLLaMA/comments/1u50hnm/qwen_27...
that seems considerably slower though (~30 tok/s). I don't know if that's an outlier/misconfigured setup or what. In general there will be much better resources for local setups using 3090s, as they're quite popular. Note that 3090s (but not 4090s nor 5090s) have NVLink, so you can network the cards fairly effectively. For this reason 2x 3090 setups are fairly popular as well. I've heard that club 3090 makes that relatively straightforward
https://github.com/noonghunna/club-3090
but don't have experience myself.
You really don’t need them. After a certain point, bigger models give diminishing returns. If you can get 80% of the productivity gain with a free local model, use the local model. It will still be way faster than doing everything by hand, but you also don’t have to pay for tokens to a cloud provider and the tools won’t be ripped away from you on a whim.
This is the new attitude enlightened people should adopt. Reject the arms race.
There are guardrails you can and must add to protect your team if you take the vibe approach: a good type system, a good database with clearly written business model and a good data model to drive your business. Make it loud and clear when something breaks with your tooling.
But... I'd definitely not vibe everything after a certain point. Reading and fixing code is also a lot of fun.
I have to advocate for the vibe-coded mess-colony.
There are applications where it either works or it doesn't, and it's simultaneously obvious whether it does. Think stock price prediction software. I've killed time in the evenings verbally chatting with agents about that specifically, and what emerged worked! It didn't work well, but it clearly outperformed randomness, and I was able to verify that myself easily.
I didn't look at a line of code, but I had an absolute blast.
Are you familiar with the concept of a Markov Chain? (If not, it's a simple tool that technically works better than randomly guessing for predicting stock movement.) I designed a very intense neural network meta-architecture, applied it, and the results were the same as if I'd used a basic Bayes model or Markov Chain. Which is a little humorous; I very much used a bulldozer to sweep the garage.
I used close minus open to determine up vs. down movment. Can't remember the lookback, but was predicting the immediate next day. Over the entire US market, a basic Markov-based model can predict the next day 52.5% of the time or something like that. (Given 1000+ stocks, you guess which direction all will go, 52.5% will be correct guesses.)
For what it's worth, I don't really know the details of the statistical tools. I do have a good grasp of train/test/validate sets, so I know what my results meant.
Except you kinda do. Try getting a job today without mentioning Claude experience. In another year it'll probably be something else. Saying you like to use Copilot today makes one seem elderly.
Not saying you need frontier models on a technical basis, but for career PR you probably do.
I tried out a few models and ended up going with either Qwen3-Coder-Next (no think, just do) and Qwen3.6-35B (thinking, w/llamacpp token budget). Created a customized prompt that works fairly well to around ~60k tokens and then is a toss up on whether it's poisoned itself or I've directly steered it into the wrong. When it's clear that's happened, if it's important to continue, ask it to write a doc then start fresh.
I don't kno whow any one cold have witnessed the last 2 decades of American VC funded tech startups and tell themselves, "you know, this will be a reliable technolgy with no hidden problems".
Even a sober technical evaluation is just two steps:
1. You're proposing to build a app on a non-deterministic model.
2. That model is hosted behind a non-deterministic system (model alignment, model guardrails, system context subterfuge, cost/token pricing)
---
So you want to build your app and you think you're going to kep up with both #1 and #2?
LLMs are, as far as the nastiness of the Real World goes, really fucking benign. Future models outperform past models, both in open weight land and at the big frontier labs. Performance per $ only ever goes up. That's just nice.
Except the Enterprise, and a lot of what people want compute for, is built on deterministic systems or processes. I'm not saying the non-deterministic nature of LLMs isn't useful. However I've worked with a lot of organizations on SOAR projects, for example. When you can weave the deterministic and non-deterministic together you get a relatively efficient system. A workflow that will stay on the rails and will come to a conclusion as expected. And the "as expected" part is critical in these types of systems. The reality of, using SOAR as an example, is also that most enterprise would be much better served by fast SLMs. Parse an email and validate if it's SPAM / Phishing or read a chunk of firewall logs and look for outliers / indications for escalation - those things can get messy in a deterministic system because of potentially unstructured data.
I don't believe it's either / or. And I believe that LLMs just aren't efficient, fast or reliable in the sense that deterministic are. It seems, at least to me, a better together story.
LLMs are what made me start considering this. Imagine a company using an LLM that was fully deterministic. All RNG was either removed or seeded in such a way that the same input (so many the seed counts as part of the input) gave the exact same output. Fully deterministic.
But such an LLM, with a slight drift in input, could still produce very different outputs. This isn't being non-deterministic, but more than the change in outputs does not naturally follow from the input. I'm thinking like how 2 double pendulums can (but not always do) greatly diverge given a very small change in their input.
So in light of that I've begun to call this new property non-chaotic. So Enterprise depends on non-chaotic systems, which are a subset of deterministic systems, and then wrangling the chaotic elements they cannot remove as much as possible.
The follow question I now have is if all LLMs are inherently chaotic, or if it is possible to have a non-chaotic LLM.
#2 is not fine; that non-determinism you do not control, have no insight into, etc.
I'm saying sure, give me #1 if it means I can build a harness around it and smooth over the edges. But I'm not taking #1 and #2. There's zero reasonable way to manae two non-deterministic systems.
So piracy on an by piracy trained ai model..
Alibaba didn't steal Opus weights, they used opus output to train their model.
If this is piracy, then so is reverse engineering efforts powering a bunch of Linux drivers.
Also, yeah, they already stole their copyrighted works, so a thief from a thief is still...theives?
If you have ethical concerns, model distillation feels like an arbitrary line to draw. Why is the first type of piracy ok, the second not? You should restrict yourself to ethical open source models. Which is btw where I genuinely hope the future of local models is going to lie. Open weights is not enough, we need fully open source models to be sustainable. Even for simple things like updating the knowledge cutoff. How we are going to distribute the training effort will be an interesting problem where I don't see an obvious solution yet. Maybe the blockchain/federated learning people can suggest something. Or university consortia, or some public sector solutions. Or something really boring - I for one would absolutely be willing to pay for DRM-free weights of an open source model (even if I could pirate them for free).
Btw, ethically sourced, open source LLMs exist! Check out eg Olmo by Allen AI: https://allenai.org/olmo
Not gonna lie, if you're coming from ChatGPT/Claude Code, you'll mostly be adding back features you've taken for granted, or solving problems you wouldn't have had. But sometimes you do get some extra utility, like uncensored models, which have become my go-to. Not because I'm doing anything saucy, but I hated how I'd become trained to pre-emptivly self-censor my prompts. The guardrails in open weights models are no less strong than in proprietary ones, subjectively even a bit stronger in Qwen. But luckily there's an entire sub-discipline of model ablation. Another advantage would be better control over image generation (although I can't attest to that, yet).