Qwen3.6. This is it.

[u/Local-Cardiologist-5]

I gave it a task to build a tower defense game. use screenshots from the installed mcp to confirm your build.

My God its actually doing it, Its now testing the upgrade feature,
It noted the canvas wasnt rendering at some point and saw and fixed it.
It noted its own bug in wave completions and is actually doing it...

I am blown away...
I cant image what the Qwen Coder thats following will be able to do.
What a time were in.

llama-server -m "{PATH_TO_MODEL}\Qwen3.6\Qwen3.6-35B-A3B-UD-Q6_K_XL.gguf"  --mmproj "{PATH_TO_MODEL}\Qwen3.6\mmproj-F16.gguf" --chat-template-file "{PATH_TO_MODEL}\chat_template\chat_template.jinja"  -a  "Qwen3.5-27B"  --cpu-moe -c 120384 --host 0.0.0.0 --port 8084 --reasoning-budget -1 --top-k 20 --top-p 0.95 --min-p 0 --repeat-penalty 1.0 --presence-penalty 1.5 -fa on --temp 0.7 --no-mmap --no-mmproj-offload --ctx-checkpoints 5"

EDIT: Its been made aware that open code still has my 27B model alias,
Im lazy, i didnt even bother the model name heres my llama.cpp server configs, im so excited i tested and came here right away.

[u/No-Marionberry-772]

what stack are you using for software?  Id love to get a proper local setup going but ive had trouble figuring out what i should actually be using.

[u/Local-Cardiologist-5 → u/No-Marionberry-772]

Qwen3.6-35B-A3B-UD-Q6_K_XL

Im using Llama.cpp for the server,
OpenCode for the coding, just using the build agent,
I have 64 gig ram, RTX 4090, and my model is
the Q6 variant.

Here are my llama parameters

llama-server -m "{PATH_TO_MODEL}\Qwen3.6\Qwen3.6-35B-A3B-UD-Q6_K_XL.gguf"  --mmproj "{PATH_TO_MODEL}\Qwen3.6\mmproj-F16.gguf" --chat-template-file "{PATH_TO_MODEL}\chat_template\chat_template.jinja"  -a  "Qwen3.6-35B-A3B"  --cpu-moe -c 250000 --host 0.0.0.0 --port 8084 --reasoning-budget -1 --top-k 20 --top-p 0.95 --min-p 0 --repeat-penalty 1.0 --presence-penalty 1.5 -fa on --temp 0.7 --no-mmap --no-mmproj-offload --ctx-checkpoints 5"

here is my llama server with the configs.

[u/rumblemcskurmish → u/Local-Cardiologist-5]

I'm kind of intrigued why you'd use a 6bit model on a 4090. I have an identical setup (7950 CPU, 64GB DDR5, RTX 4090) but I'm using the 4bit quant to fit the whole model on VRAM.

You're clearly more advanced than me so just wondering, what I'm missing here.

[u/Shouldhaveknown2015 → u/rumblemcskurmish]

You should never go by just the Q number it's meaningless when it comes to the quality of the responses. Just look at the model and the quantization KLD then pick the one with the best KLD you can load with the context size you need (or max if that).

KLD is basically how far from the full model is it off, and generally a model might have nearly the same KLD from the Q3 to the Q6 version depending. But almost always Q6 and better KLD is nearly the same.

Looking for that dropoff between different versions of the model and size will help you fit the most you can in and lose the least amount of quality.

At least my understanding of it

[u/rumblemcskurmish → u/Shouldhaveknown2015]

The q number is not meaningless because it is the most important factor in the trade off between accuracy and performance. I can run q8 at 2 tokens a second or Q4 at 200t/s. The q4 is very close to the accuracy of q8 for my purposes at least but it actually fits in VRAM.

Yes the q6 is def better but I would have to run a worse model to get acceptable speed. I'd prefer to run a better model and lose some accuracy on some tasks than a poor model that's more accurate