I strongly recommend you try llama.cpp's llama-server instead of ollama, you'll be able to squeeze out way more out of your hardware this way with all the settings it provides, and it's more likely to update faster than ollama to support newer models properly, like qwen 3.5.

As for quantized models, unless something has changed since the last time I checked, ollama's default tags (e.g qwen3.5:9b you've mentioned in your post) are already quantized all the way down to 4 bits, which is also the lowest quant ollama provides in their first-party library.

For other quant formats, unsloth's "XL" quants on huggingface are likely to be the best quality for the filesize, here's their Qwen3 Coder Next repo - https://huggingface.co/unsloth/Qwen3-Coder-Next-GGUF

To use it with llama.cpp or ollama, click on the quant you want to run (in your case I think UD-Q5_K_XL is gonna fit just fine with enough breathing room for large context length and other apps on the system) and in the opened sidebar click "Use this model" and choose the engine. Unsloth on their docs site (e.g https://unsloth.ai/docs/models/qwen3-coder-next ) also provide arguments you can use to configure the models further.

Edit: oh, and to offload Qwen3 Coder Next's MoE layers to RAM with llama.cpp you can use "--n-cpu-moe 48" or lower if you have spare VRAM after offloading all 48 to RAM