Qwen-3.6-35B-A3B was our "gateway drug" into switching our organisation to agent/harness-first coding.
Particularly, I had one team member who was extremely sceptical of AIs/LLMs/harnesses and refused to use them. One day he said "Well, I have an RTX 5090 doing nothing... should I try to get something up on it?" and a few minutes later he had 3.6-35B loaded up, running OpenCode.
It continues to be a workhorse to this day, running on both my local Mac for various types of jobs, an AMD R9700 at the office, and said teammember still uses it on his 5090, although in practical terms we do a lot more with DS-V4-Flash-0731 these days.
I’ve run 3.6-27B and 3.6-35B on 32GB locally for a lot of bulk non-code tasks. Let it run overnight and wake up to millions of output tokens worth of results without data having left my house, all for the price of electricity.
I haven’t found it very useful for code. It can do some code, but I’ve tried a dozen different quants and context lengths and the output is always bad enough that it has to be discarded for anything other than really easy tasks. It has been useful for exploring codebases for search and summary, though.
DS Flash is where local models begin to feel useful for coding, but the quants we run locally are sharply reduced in intelligence from the benchmarks for the full models.
For applications where data cannot leave the local network it’s good to have them. For actual coding work I can’t actually justify the power of electricity and cooling, let alone the expensive hardware, compared to hosted APIs.
But I admit I do enjoy playing with them anyway. I think it’s one of those hobbies where it’s most fun if you never do the math on how much you’re paying for the privilege. If someone has a requirement that data stay local then it’s different, of course.
> I’ve run 3.6-27B and 3.6-35B on 32GB locally for a lot of bulk non-code tasks.
Do you mind sharing your use cases?
Not OP, but I use it for a ton of smaller things. I have it hooked into Hermes and have been using it to help bulk rename my media folders so they all follow a common format, add titles that sort of thing which wouldn't be easy to 'script'. Another thing I use it for is comparing data sets, looking at my exported Spotify artists and compare to what I have locally, and letting me know where there are missing artists, or albums, and recommendations based on similar artists that I may not have locally.
Sure a lot of this could be done without AI, but it's certainly quicker and easier, and since my AI box is on solar, it's just the power of the sun to keep it going.
Not OP, but driving knowledge bases is the poster child use case for me https://github.com/aka-rider/llm-wiki
I started with Karpathy's LLM wiki, and did everything he said not to do - downgraded the model to mere tool usage and summarization, and it works great.
I am a data hoarder, and finally I can just dump all the content I remotely like, and get something interesting to browse for the price of electricity.
Agentic long-running tasks, as others have mentioned:
- Groom and triage tickets for agentic SWE workflows
- bug hunt — the probability of Qwen fixing a complex bug is 50/50 but often it is capable of identifying the root cause or at least laying the ground work for a more capable model to pick it up.
Love the workflow. Is there an existing way to do this inside Obsidian -- where I have all my knowledge?
SmartConnections but heads up, Connections Pro asks $300/year, or, more for the plugin than for an LLM subscription, more than Microsoft Office for that matter.
https://smartconnections.app
Keep in mind Obsidian is open standard JS plugins... You know what can write open standard JS plugins?
Classic which comes first, LLM or the plugin, though. :-)
Not OP, but I use it for personal tasks that are just not worth the claude tokens -- rooting through historical medical records to unify prescription history, super-OCR'ing thousands of PDF pages (i.e. beyond PDF dumping -- vision means it can look at tables, understand tricky things like a continuation of a block quote or aside on the next page, etc.), and when power is cheap I'll just let it noodle on little projects on my data. During my agent's "free time" I give it with a tremendously open ended prompt last week, it did a linguistic analysis of how my texting changes in the lead up to, initiation, maintenance, and ending of romantic relationships.
What kind of hardware do you run that massive beast on (DS-V4-Flash-0731)?
That is exactly what got me past just enough of my cynicism to get started. I am still cynical but now I have meaningful knowledge.
> Qwen-3.6-35B-A3B
The A3B models are super fast but I found the A3B Q4 model ran in circles a lot and ended up taking longer to complete tasks that 27B Q6 because it kept having to redo/rethink/fix something.
I was writing extensive prompts to rein it in and it would still ignore basic directives like "never force push on the repo, ask me instead". I ended up switching back to 27B after about a week of frustration and lost productivity.
We’re using 6bit quants since we have 32GB cards.
Gemma QAT is an honourable mention.
What!? You are skeptical of AI but will go through the manual process of hosting a model that’s less than frontier intelligence (talking about Qwen 3.6)? Anti-AI folks are always odd to me
A local model needs 0 investment and 0 commitment, takes literal minutes to get started (especially if you have someone who is into that stuff showing you the ropes) and if you end up disliking the experience of using AI you can just `rm -fr` it and forget the whole thing existed.
Needs 0 investment and 0 committment?
- You at least need a capable machine, so that's not 0 monetary investment. - You need to spend at least an hour decicding between ollama, llamacp, mlx, etc. - You need to find the correct quantized version of the model that works for you based on the architecture. - You need to figure out the correct context window size to get reasonable performance. - You need to setup a harness that works against your model - You might need to setup additional websearch tools, image tools, etc since harnesses like pi don't come with the model. Ofc you can't use codex and claude code, because those aren't opensource and you are anti-AI.
Or, you could sign up for Opencode for $10 and just be productive.
I'm particularly calling out the hypocrisy of the original comment. Being Anti-AI, and then spending hours on setting up a less than frontier AI model.
> Or, you could sign up for Opencode for $10 and just be productive.
You forgot the step before where you spend months waiting for security to vet it, legal to sign off and procurement to approve it.
Or you could use hardware your team has lying around. Everyone isn’t working on cloud-hosted CRUD APIs.
You might be anti-ai in the sense you aren’t comfortable with all your data being shipped back and forth to a third party.
Im anti AI in the sense of VC backed global warming far right accelerationists.
Local AI is almost perfect. But its like all democracy: its history is marred with lots of crap.
All of your objections have already been addressed by the previous comments.
The original comment states that the person in question already had a suitable graphics card to hand, so it did not require a monetary investment.
GP clearly states that "someone who is into that that stuff" was guiding the process, so it did not require a significant time investment.
> I'm particularly calling out the hypocrisy of the original comment. Being Anti-AI, and then spending hours on setting up a less than frontier AI model.
I see no hypocrisy in the original comment.
You've also assumed the skeptic in question doubts the capabilities of AI. That may be the case (like you, I have no idea), but they may also have privacy concerns, in which case a local model is the appropriate choice.
There are plenty of reasons to be skeptical of AI.
install LM Studio, download the automatically selected quant based on your hardware, start a conversation with the automatic context size. 10 minutes at best and zero effort
Maybe not '0 investment and 0 commitment', but incredibly little depending on what you have laying around. It takes less than 5 minutes to download say LM Studio and an Open Model and as long as you have the hardware to support it, you start moving along. If you are on AMD in some ways it's even 'easier', you can download Lemonade and it will tell you exactly what will fit and best options based on what you are trying to do.
For me at least the local AI stuff, powered with solar has been pretty great. Would that scale to a large business? Goodness no, but for my tinkering and learning, it works great.
> You at least need a capable machine, so that's not 0 monetary investment
It is 0 monetary investment if I already have said machine lying around doing nothing.
Which is exactly the story OP talked about.
But most people don't have an RTX 5090 lying around, so the story doesn't apply to them, right?
You don't need a 5090 to run local AI. A whole lot of people out there are doing it with Macs. Unified ram is the biggest thing.
Correct. If the premise doesn't hold, it's ex falso quodlibet for anyone.
Back in “the day” nerds just bought the hardware to fuck with. Some of us still do. Claiming that compute is the barrier to entry just means you’re not a nerd. That’s ok.
I'm running Qwen 27B no problem with an AMD 9070XT + 24gb DDR5 ram. Does basic web search for me (tool call with tavily, costs nothing I get 1000 searches a month) and is great for creative writing (primarily breaking writer's block). Until the recent surge in ram costs, that wouldn't be hard to do. I built the computer for ~$1600 a year ago.
I am getting ~13-15 tps with my 9070XT for the 27B (~35tps for the 35B-A3B), but I think for me the main bottleneck is the 64gb of DDR4 3600 memory. What kinda speeds are you getting with what speed of DDR5?
> You need to setup a harness that works against your model - You might need to setup additional websearch tools, image tools, etc since harnesses like pi don't come with the model
Pi has a nice guide on it (https://pi.dev/docs/latest/llama-cpp) and it is really not that hard.
How is that hypocrisy? Self hosting is somehow anti AI? Its not anti AI. Its literally using AI!
…and honestly, at a higher technical level than slapping your wallet against a token provider and running prompts in a hosted sandbox you can't even see the prompts in.
Look man I am incredibly skeptical of how LLM’s have been rolled out and all the promises people make (it’s so much snake oil and pipedreams), but I also found it very trivial to hop on LM studio and start tinkering with models. If you’ve already got a decent midtier computer on hand, which I imagine a lot of us already do, then it’s really not hard to get started and get immediate results.
Local models on regular hardware aren't really capable of anything. Whatever you're testing is nowhere near a measly $20/mo subscription, so it's of limited use.
i really like the idea of running local models but i'm always in the position of wanting the best model(s) available and i don't have any severe privacy concerns. as such i have yet to justify ever using local models.
This is the diametric opposite of the rent-vs-buy scenario that this entails.
Local: You need to invest $thousands into GPU and/or very-high-end CPU+Memory hardware.
Vendor: You can use any existing device, even a phone or tablet. A very low-end laptop is fine.
> takes literal minutes to get started
Local: Typical scenario is hours just to download the software, the model weights, and then faffing around with CUDA and matching your GPU drivers.
Vendor: Free-tier available instantly on a web URL. Even local agents have free tiers from multiple vendors. Install is a single command and/or download and "next,next,next,finish" wizard that takes ~1 minute.
> you can just `rm -fr` it and forget the whole thing existed.
I'm still cleaning up multi-GB model weights floating around in hidden subdirectories under my user profile from months ago when I was experimenting with local models!
Meanwhile I simply... stopped using Gemini. That was the entire process: I no longer actively use it. They stopped billing me for my token usage, because it is now zero. That's... it.
You have it totally backwards.
> I'm still cleaning up multi-GB model weights floating around in hidden subdirectories under my user profile from months ago when I was experimenting with local models!
Are you trying to say that local models are hard to use because... you're having issues handling files properly? I am not sure I get the argument.
I get the rest of the comment: local models require an investment upfront, and it is less convenient. It doesn't say that it is not cheaper, though.
> you're having issues handling files properly?
I guess they were using ollama, which does not tell you where it puts the models it downloads.
Filelight / ncdu are my friends for finding random 30GB directories containing cached models.
Personally, I prefer QDirStat. I just tried to use FileLight to compare, but the package seems to be broken on Lubuntu.
You two have just listed several tools needs to clean up after another tool: not a strong argument for “simpler”.
It took me about three hours total to set up a local model. I already have a GPU and I have fiber for the download. llama.cpp is not difficult to compile and has many backends. It can run parts of the model on different backends, like in the common case that the GPU doesn't have enough VRAM for everything. There are many step-by-step guides available.
Takes even less depending on your system. LM Studio or Lemonade and you are set up in minutes and now they can even tell you what models will fit with the memory you have.
And it would be in seconds if models weren’t that large and slow-ish to download! LM studio is such a noob friendly experience, pretty neat first experience!
Three hours is a lot longer than one minute.
> "I'm still cleaning up multi-GB model weights floating around in hidden subdirectories under my user profile from months ago when I was experimenting with local models!"
I used to deal with these kinds of frustrations too.
Huh what? Qwen3.5-35B-A3B runs just fine with maximum context, on an RTX SUPER 12 GB, with offloading of some expert layers to DDR4-3200.
Same story on an RTX 4060 Ti 16 GB. MTP is a serious boost to tg.
Downloading the model is a simple hf command that HuggingFace's web UI even gives you.
llama.cpp is trivial to use, and so is llama-swap, if you want to use other models too.
If you don't know what arguments to run it with, you download ggrun and use that.
Local LLMs are incredibly capable and don't need expensive hardware. A $500 GPU will do. Or even cheaper.
This is all trivial.
Full model or a 4-bit quant? I have a 5090 and I'm not sure whether I should use a quant that fits within the VRAM or a much bigger version where I'd have to offload a lot to 64GB RAM and a beefy CPU (but still a CPU)
I personally run the Q6 quant on my RX 9070 XT (16GB VRAM). On r/LocalLlama there was a post recently as well, which talked about the degradation of different quants (for the 27B version)[0]
[0]: https://www.reddit.com/r/LocalLLaMA/comments/1vef79c/quantiz...
RTX Super 12GB costs $700. An openrouter account costs nothing.
A lot of people already have 12GB+ GPUs lying around for playing games, doing video editing, etc. I would not get a GPU or mac just to run LLMs personally, but if one wants to get such a device for other tasks too, it may make sense to eg choose a slightly higher (v)RAM variant if they want to run some bigger models. Then what you pay for the local llms is just the difference.
> Local: Typical scenario is hours just to download the software, the model weights, and then faffing around with CUDA and matching your GPU drivers.
Download LM studio, search models, click download, wait minutes, prompt and have fun
“If you have the prerequisite hardware, then… know which model you want out of thousands of a variants… and your drivers are up to date, then it is fast!”
This largely describes me. I'm skeptical of AI in that it's capabilities, while very impressive, are vastly oversold and overblown. Being skeptical of AI is not being "Anti-AI". That's largely the AI data centers are using up all the water and electricity types.
Maybe you're anti-AI because you're really anti-outsourcing your thinking to some remote corporation you don't control?
That's one of my main issues with AI anyways, the thought of having all my data go through some sketchy foreign (to me) entity with questionable motives and under a questionable regime.
Local AI solves for all of those.
I'm not against AI. I'm calling out the hypocrisy in the comment. I'm anti-AI, but will spend hours trying to setup a local model, instead of just getting access to frontier intelligence in 15 mins, and actually getting useful work done.
If you're learning about model inference, then it's a different and you are definitely not anti-AI in that case.
I think learning how to set up a local AI now requires less time than learning about potential pitfalls of token-plans and processing payments in corporate environments.
It's not that billing is complicated, but learning to set up a local AI is a lot more useful and more rewarding.