GLM-5 is a 744B parameter (40B active) Mixture-of-Experts model developed by Z.ai, targeting complex systems engineering and long-horizon agentic tasks. It uses Multi-Head Latent Attention (MLA) with 47 transformer layers, 64 routed experts (4 active per token), and 1 shared expert.

Quantization

This model uses a mixed-precision quantization.

Component	Mode	Bits	Group Size
Expert weights (switch_mlp)	MXFP4	4	32
Attention, embeddings, shared expert, dense MLP, lm_head	Affine	8	64

Use with mlx-lm

pip install mlx-lm

python

from mlx_lm import load, generate

model, tokenizer = load("mlx-community/GLM-5-MXFP4-Q8")

prompt = "hello"

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

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