u/No_Oil_6152

I want to use Qwen 27B for C# enterprise solution-level analysis, refactoring, and generating code.

However, even when using higher quants (Q6+), Qwen has entered an infinite loop, or just been too slow to be useful.

I have been advised (by Claude Sonnet 5) that my looping issue with Qwen 3.6 27B Q6 quant may not have been down to the smaller quant but rather my LM studio settings.

Anyone here use Qwen 3.6 27B with LM Studio? What are your settings and best quants to get successful long running coding tasks?

Are you getting decent results with unsloth Q4_K_XL? I am considering it. I use Q8 atm but have been told by some on this subreddit that a smaller quant can work.

If you use llama.cpp, that's fine, I'd like to see the llama.cpp parameters that give you best results. I'm not averse to using it.

Also, if anyone is running a Linux VM with llama.cpp on Windows, can you tell me your setup and its token output compared to windows?

Thanks.

I'm running the unsloth qwen3.6 27b Q8_0 quant and I find it keeps halting when I ask it to do a code review of a single project in a large c# solution.

Strangely, A35B could handle it - I want 27B to do the review as I expect it has more "knowledge".

I find the Q6 quant to not be good enough, it kept looping, so moved to Q8, but this isn't much better.

I have a Ryzen 9900x, R9700 pro 32GB VRAM, 64GB DDR6000.

Any ideas on how to get it running better in LM Studio? I felt the context was a decent compromise but could drop it to 65536 maybe.

I fear I may need more VRAM.

https://preview.redd.it/7vwvyfly18bh1.png?width=278&format=png&auto=webp&s=76411cdd9fbdcba3a776ab2044f54527ca9981c6

I have a Lancool 216 with a Corsair RM850e 850w PSU.

Just above the PSU, I have two 120MM SL-INF fans which are blowing air UP towards the GPU, which is a R9700 AI Pro. It gets hot when running LLMs.

The GPU's fan is facing down, towards the two fans - so basically the SL-INF and GPU are blowing air at each other (or are they?)

I'm concerned that instead of cooling my GPU down, I may be blowing hot air from the PSU up into the GPU and heating it.

I was wondering - is this a common issue, and what can I do to fix it?

Would fitting an exhaust fan over the PCI slot brackets help? Or a PCI exhaust fan like this: https://www.amazon.co.uk/Cooling-System-Expansion-Exhaust-Computer/dp/B0GS1QBRZP

I have:

1x 140mm SL-INF fan behind CPU fan, as exhaust.

2x 140mm SL-INF fans at top of case as exhaust.

2x 120mm SL-INF fans just above PSU, at bottom edge of motherboard, blowing up to my Soundblaster Audigy FX 2 sound card (small form) and GPU

2x Standard (160mm?) Lancool fans at front of case as intake.

Thanks in advance.

I recently bought a Sapphire R9700 AI Pro for both gaming and working with LLMs on Windows 11.

During gaming its pretty quiet but when using LLMs its quite noisy.

I've undervolted the card with AMD Adrenalin (by -50mv) but wondering if its safe to do more.

What are the average undervolts I can do with this card, and is there anything else I can do on Windows to reduce noise? I have the fancontrol app so could use that to reduce noise I guess, but is that wise if the card is being stressed for AI? I haven't changed "Total Board Power" - is that an option?

I have plenty air cooling in my lancool 216 case with 2x SL-INF blowing directly into the GPU (maybe its blowing warm air from the PSU!) and can't physically add more fans.

A few days ago I asked what graphics card to buy to run a coding LLM.

After much deliberation I decided to buy a Sapphire R9700 AI Pro (£1300 from Scan)

First thing I did was set up LM Studio and install Qwen 3.6 27B 4 bit. I'm running Windows 11 and I need to stay Windows 11 because I develop with Visual Studio 2026 (although I use VS Code from time to time)

Pleased to say, with LM Studio and Vulkan, all the layers managed to fit inside the 32GB of VRAM. In a new chat I was getting 700 t/s. (Edit: I'm talking bollocks, picked up the wrong stat.)

From VS Code it was much slower - Token wise I was getting 20 t/s. Not great, I expected a little bit more but I suspect its loading a LOT of context from my solution. 100% GPU usage in task manager (which is what I like to see)

Conversely, I could not get llama.cpp (Vulkan build, no turboquant fork this time) to host all the layers, BUT I am wondering if my Ryzen 9900x's onboard AMD GPU (which does support Vulkan according to llama.cpp on llama-server startup) was being used instead.

When I asked Qwen 3.6 for advice it kept advising me to use the CPU! Not happening.

I'll need to disable that GPU in the BIOS I guess, to rule it out.

I was wondering - for Qwen 3.6 27B 4bit with 32GB VRAM and 64GB RAM, has anyone got it much faster with a decent context (say, 131072+) ? Because of Visual Studio's needs, I cannot move to Linux and RocM (although maybe using WSL is possible now I come to think of it?)

I have an Nvidia 4070 GTX 12GB at the moment and 64GB of DDR5 6000 RAM.

I don't think 12GB VRAM is going to cut it for what I want to do with LLMs, which is (eventually) develop production grade software, refactor solution wide, solution wide code review.

I don't need looping agentic behaviour like adversarial code review. I'm a pro software dev so I will be in the loop reviewing the code it generates.

So, I was wondering, what affordable choices do I have which will run a production grade LLM and game as well as, or better than the 4070 I have?

• A 7900XTX with 24GB of RAM is obviously better gaming wise BUT I am advised (by AI) it would be worse than the 4070 for LLMs because ROCm is less mature than CUDA.
• A r9700 32GB is apparently worse for gaming so I'm not considering it.
• I cannot - or rather will not - pay £3000 for a 5090 32GB. That's a ridiculous amount of money for what you get, Huang should be ashamed of himself.
• A 3090 would be a backwards step - no ray tracing - and it looks like prices on EBay UK are going up.

So what options do I realistically have, apart from "invest the money into a cloud LLM" - something I'm already doing with Deepseek R4 Pro.

I have two windows 11 instances hosted on separate m2 ssds:

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* One for playing games

* One for development

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When I select the development instance from the list of available OS's my machine ALWAYS has to restart first.

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Why is this?

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Is there any way to remove/mitigate the need for this restart?

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AI advises me I need to disable Fast Startup for both instances but it doesn't seem to work.

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What else can I try?

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Rig: msi tomahawk x870 wifi, 64GB DDR5, Ryzen 9900x, 2x Gen5 m2 ssd, USB hub for keyboard + mouse.

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Thanks in advance.

I found a post that said you could access Deepseek via Ollama Proxy, but unfortunately Ollama Proxy seems to have been taken off Github.

Could someone tell me if its possible to use Deepseek API via VS 2026 (not code, I already have that working) and if so, how to set it up step by step?

Hi, I wonder if you could recommend some LLMs or tweaks to me.

I am a local LLM newbie and I've got llama.cpp running, but the models are not fast enough to be useable.

I have the following setup:

• Windows 11
• AMD 9900X
• 64GB DDR5 6000 RAM
• NVIDIA 4070 12gb
• Gen5 M2 SSDs at 14900 IOPs
• llama.cpp (CUDA) installed

I am a C# developer interested in writing games with DirectX or OpenGL for a hobby. I also like to develop with Blazor, Entity Framework, Azure.

I would like to jettison Github Copilot Pro because I expect after June 1st I won't be able to afford it. I thought maybe local LLMs could assist me with my hobbyist coding work - maybe even scaffold apps for me which I can then edit to fix code issues etc.

Basically an LLM excellent at coding is what I'm after.

I ran LLMfit and installed these two "good" coding LLMS for llama.cpp (CUDA version):

• Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled.IQ4_XS.gguf
• qwen3-coder-30b-a3b-instruct-q4_k_m.gguf

Both of these run pretty slowly, certainly not usable at the moment for anything except single file editing and even then, context appears to be limited to 65536 (anything larger = sloth response)

I'm thinking of "upgrading" to a 24GB 7900XTX so there's more VRAM for LLMs, but I read ROC is much slower and less mature than CUDA so less tokens/s. I really don't want to spend a grand on a 5080 either, that's probably going to cost more than the Github Pro sub I have.

Here are my command lines for both LLMs:

Qwen3 Claude Opus Reasoning Distilled (one I was tinkering with for a few minutes, probably wildly wrong command line arguments)

llama-server -m C:\llms\Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled.IQ4_XS.gguf --alias qwen36claude47 --host 127.0.0.1 --port 8080 -c 65536 --parallel 1 -fa on --cache-type-k q8_0 --cache-type-v q8_0 --cache-ram 2048 --ctx-checkpoints 8

Qwen3 coder 30B (one I spent hours on yesterday trying to get it responsive)

llama-server -m C:\llms\qwen3-coder-30b-a3b-instruct-q4_k_m.gguf --alias qwen3coder30ba3binstruct --host 127.0.0.1 --port 8080 -c 65536 --parallel 1 -fa on --cache-type-k q4_0 --cache-type-v q4_0 --no-mmap --cache-ram 512 --ctx-checkpoints 2

What would you recommend I do? I really want an excellent local coding LLM with Claude Sonnet 4.6 capabilities (a man can dream can't he?).

Hi,

My favourite horror movies are Poltergeist, Salems Lot (1979), Return of the living dead, Dawn Of The Dead (1979) and The Sixth Sense.

I'm looking for serious, intense adult-oriented books with similar plots and supernatural elements.

Ideally the books would also contain graphic depictions of violent death and/or gore (just like the movies). Exhumations/rising from the grave, with graphic detail, are a BIG bonus 😂 - they always unnerve me.

I am not looking for psychological horror where there's nothing supernatural. I want to be scared by the dead.

Would you have anything to recommend?

NB: I already have the novelisations of the first four movies listed, and the following books containing their themes : Later (King), They Thirst, Hell House, The Strain, I am legend, The Rising, Suffer The Children, Handling the Undead. Am aware of Let The Right One in, not keen on it.

Thanks in advance.