As the post says, LM Studio has an MLX backend which makes it easy to use.
If you still want to stick with llama-server and GGUF, look at llama-swap which allows you to run one frontend which provides a list of models and dynamically starts a llama-server process with the right model:
I didn't know about llama-swap until yesterday. Apparently you can set it up such that it gives different 'model' choices which are the same model with different parameters. So, e.g. you can have 'thinking high', 'thinking medium' and 'no reasoning' versions of the same model, but only one copy of the model weights would be loaded into llama server's RAM.
Regarding mlx, I haven't tried it with this model. Does it work with unsloth dynamic quantization? I looked at mlx-community and found this one, but I'm not sure how it was quantized. The weights are about the same size as unsloth's 4-bit XL model: https://huggingface.co/mlx-community/Qwen3.5-35B-A3B-4bit/tr...
iiuc MLX quants are not GGUFs for llama.cpp. They are a different file format which you use with the MLX inference server. LM Studio abstracts all that away so you can just pick an MLX quant and it does all the hard work for you. I don't have a Mac so I have not looked into this in detail.
Many user benchmarks report up to 30% better memory usage and up to 50% higher token generation speed:
https://reddit.com/r/LocalLLaMA/comments/1fz6z79/lm_studio_s...
As the post says, LM Studio has an MLX backend which makes it easy to use.
If you still want to stick with llama-server and GGUF, look at llama-swap which allows you to run one frontend which provides a list of models and dynamically starts a llama-server process with the right model:
https://github.com/mostlygeek/llama-swap
(actually you could run any OpenAI-compatible server process with llama-swap)