           arc arc/e
qx85     0.456,0.519,0.622,0.704,0.392,0.774,0.680
qx64-hi  0.445,0.513,0.622,0.704,0.384,0.786,0.702
mxfp4    0.455,0.510,...

Quant    Perplexity     Speed(t/s)  Memory
qx85     3.762 ± 0.024  448         98.86 GB
qx64-hi  3.778 ± 0.024  435         98.28 GB
mxfp4    3.909 ± 0.025  535         71.94 GB

The qx85 uses 5 bit data stores and 8 bit attention paths, embeddings, and head.

It was shaped to fit a 128GB Mac available RAM with a decent sized context.

The qx formula for the Qwen3.5 is still being refined, the model will be updated in-place if a more stable layer combination is found.

-G

This model Qwen3.5-122B-A10B-Text-qx85-mlx was converted to MLX format from Qwen/Qwen3.5-122B-A10B using mlx-lm version 0.30.8.

Use with mlx

pip install mlx-lm

python

from mlx_lm import load, generate

model, tokenizer = load("Qwen3.5-122B-A10B-Text-qx85-mlx")

prompt = "hello"

if tokenizer.chat_template is not None:
    messages = [{"role": "user", "content": prompt}]
    prompt = tokenizer.apply_chat_template(
        messages, add_generation_prompt=True, return_dict=False,
    )

response = generate(model, tokenizer, prompt=prompt, verbose=True)

nightmedia/Qwen3.5-122B-A10B-Text-qx85-mlx

Qwen3.5-122B-A10B-Text-qx85-mlx

Use with mlx

nightmedia/
Qwen3.5-122B-A10B-Text-qx85-mlx