What are the hardware requirements for GLM 4.6 Mlx 8bit Gs32?

GLM 4.6 Mlx 8bit Gs32 requires approximately 198.6 GB of VRAM and supports a context window of 198K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed GLM 4.6 Mlx 8bit Gs32 and how large is it?

GLM 4.6 Mlx 8bit Gs32 is developed by mlx-community, a model with 352.8b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

Where can I download or evaluate GLM 4.6 Mlx 8bit Gs32?

GLM 4.6 Mlx 8bit Gs32 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

GLM 4.6 Mlx 8bit Gs32 by mlx-community — VRAM 198.6GB, 198K context

Name: GLM 4.6 Mlx 8bit Gs32
Author: mlx-community

GLM 4.6 Mlx 8bit Gs32 is an open-source language model by mlx-community. Features: 352.8b LLM, VRAM: 198.6GB, Context: 198K, License: mit, Quantized, LLM Explorer Score: 0.2.

8-bit 8bit Base model:quantized:zai-org/g... Base model:zai-org/glm-4.6 Conversational En Glm4 moe Mlx Quantized Region:us Safetensors Sharded Tensorflow Zh

Model Card on HF 🤗: https://huggingface.co/mlx-community/GLM-4.6-mlx-8bit-gs32

GLM 4.6 Mlx 8bit Gs32 Parameters and Internals

LLM Name	GLM 4.6 Mlx 8bit Gs32
Repository 🤗	https://huggingface.co/mlx-community/GLM-4.6-mlx-8bit-gs32
Base Model(s)	zai-org/GLM-4.6 zai-org/GLM-4.6
Model Size	352.8b
Required VRAM	198.6 GB
Updated	2026-08-08
Maintainer	mlx-community
Model Type	glm4_moe
Model Files	5.0 GB: 1-of-90 4.4 GB: 2-of-90 4.4 GB: 3-of-90 4.4 GB: 4-of-90 4.4 GB: 5-of-90 4.4 GB: 6-of-90 4.4 GB: 7-of-90 4.4 GB: 8-of-90 4.4 GB: 9-of-90 4.4 GB: 10-of-90 4.4 GB: 11-of-90 4.4 GB: 12-of-90 4.4 GB: 13-of-90 4.4 GB: 14-of-90 4.4 GB: 15-of-90 4.4 GB: 16-of-90 4.4 GB: 17-of-90 4.4 GB: 18-of-90 4.4 GB: 19-of-90 4.4 GB: 20-of-90 4.4 GB: 21-of-90 4.4 GB: 22-of-90 4.4 GB: 23-of-90 4.4 GB: 24-of-90 4.4 GB: 25-of-90 4.4 GB: 26-of-90 4.4 GB: 27-of-90 4.4 GB: 28-of-90 4.4 GB: 29-of-90 4.4 GB: 30-of-90 4.4 GB: 31-of-90 4.4 GB: 32-of-90 4.4 GB: 33-of-90 4.4 GB: 34-of-90 4.4 GB: 35-of-90 4.4 GB: 36-of-90 4.4 GB: 37-of-90 4.4 GB: 38-of-90 4.4 GB: 39-of-90 4.4 GB: 40-of-90 4.4 GB: 41-of-90 4.4 GB: 42-of-90 4.4 GB: 43-of-90 4.4 GB: 44-of-90 4.4 GB: 45-of-90
Supported Languages	en zh
Quantization Type	8bit
Model Architecture	Glm4MoeForCausalLM
License	mit
Context Length	202752
Model Max Length	202752
Transformers Version	4.54.0
Vocabulary Size	151552
Torch Data Type	bfloat16

Rank the GLM 4.6 Mlx 8bit Gs32 capabilities

Have you tried this model? Rate its performance — this feedback helps the ML community find the right model for their needs.

Instruction Following and Task Automation
Factuality and Completeness of Knowledge
Censorship and Alignment
Data Analysis and Insight Generation
Text Generation
Text Summarization and Feature Extraction
Code Generation
Multi-Language Support and Translation

Best Alternatives to GLM 4.6 Mlx 8bit Gs32

Best Alternatives	Context / RAM	Downloads	Likes
GLM 4.7 6bit	198K / 239.2 GB	2060	1
GLM 4.7 4bit	198K / 197.4 GB	2822	5
GLM 4.7 8bit Gs32	198K / 198.6 GB	1937	4
GLM 4.6 4bit	198K / 197.4 GB	891	15
GLM 4.7 8bit	198K / 189.5 GB	144	3
GLM 4.6 8bit	198K / 189.5 GB	22	2
GLM 4.6 5bit	198K / 228.6 GB	125	3
GLM 4.5 4bit	128K / 197.4 GB	226	16
GLM 4.7 Heretic	198K / 355.2 GB	461	1
GLM 4.7 Heretic	198K / 224.9 GB	63	7

Note: green Score (e.g. "73.2") means that the model is better than mlx-community/GLM-4.6-mlx-8bit-gs32.