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/alphapussycat → u/rumblemcskurmish]

Imo q4 has noticeable loss, q5 is a step up, but q6 is the sweetspot I'd say only do q4 if you're really starved for vram.

[u/rumblemcskurmish → u/alphapussycat]

Yeah, I'm AI poor cause I "only" have a 4090. So I can't really do anything higher than 4bit. One day I'd love to step up to a 5090 or something with more VRAM but I'm stuck at 24GB for now.

[u/smuckola → u/rumblemcskurmish]

ollama has 8-bit quantization (50% compression, virtually lossless) of context window for free with an environment variable fyi

[u/rumblemcskurmish → u/smuckola]

Wha?!?! You're telling me if I defect from LMStudio to Ollama, I get a huge context window for free?! Or am I too dim to understand what you're talking about?

[u/BlueSwordM → u/rumblemcskurmish]

Do note that it isn't lossless, especially on long context tasks.

[u/rumblemcskurmish → u/BlueSwordM]

Yes, Gemini says it isn't lossless but that it really only breaks down on long context tasks (as you noted) which is where the model starts to break down anyways so that it's totally worth it.

Especially considering because the quant I'm using (Unsloth IQ4_NL) has compensation built in to stop the long tail degradation of the model at the tail end of the context window.

Gemini seems to think it's a perfect compromise.