[rahimnathwani]

For anyone else trying to run this on a Mac with 32GB unified RAM, this is what worked for me:

First, make sure enough memory is allocated to the gpu:

  sudo sysctl -w iogpu.wired_limit_mb=24000

Then run llama.cpp but reduce RAM needs by limiting the context window and turning off vision support. (And turn off reasoning for now as it's not needed for simple queries.)

  llama-server \
    -hf unsloth/Qwen3.5-35B-A3B-GGUF:UD-Q4_K_XL \
    --jinja \
    --no-mmproj \
    --no-warmup \
    -np 1 \
    -c 8192 \
    -b 512 \
    --chat-template-kwargs '{"enable_thinking": false}'

You can also enable/disable thinking on a per-request basis:

  curl 'http://localhost:8080/v1/chat/completions' \
  --data-raw '{"messages":[{"role":"user","content":"hello"}],"stream":false,"return_progress":false,"reasoning_format":"auto","temperature":0.8,"max_tokens":-1,"dynatemp_range":0,"dynatemp_exponent":1,"top_k":40,"top_p":0.95,"min_p":0.05,"xtc_probability":0,"xtc_threshold":0.1,"typ_p":1,"repeat_last_n":64,"repeat_penalty":1,"presence_penalty":0,"frequency_penalty":0,"dry_multiplier":0,"dry_base":1.75,"dry_allowed_length":2,"dry_penalty_last_n":-1,"samplers":["penalties","dry","top_n_sigma","top_k","typ_p","top_p","min_p","xtc","temperature"],"chat_template_kwargs": { "enable_thinking": true }}'|jq .

If anyone has any better suggestions, please comment :)

[suprjami]

Shouldn't you be using MLX because it's optimised for Apple Silicon?

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)

[rahimnathwani]

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...