Comment by trollbridge
17 hours ago
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.
automatic coding is a flow thing with 3.6, but I agree it isnt fully there, it does need specific per function instructions to maintain coherence, with a few exceptions Im unsure hold in general which I got via an experimental harness.
> 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.
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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.
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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.
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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.
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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.
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