What are the hardware requirements for GLM 5.3 Flash Abliterated MLX 4bit?

GLM 5.3 Flash Abliterated MLX 4bit requires approximately 185.6 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed GLM 5.3 Flash Abliterated MLX 4bit and how large is it?

GLM 5.3 Flash Abliterated MLX 4bit is developed by vaultai, a model with 321.3b 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 5.3 Flash Abliterated MLX 4bit?

GLM 5.3 Flash Abliterated MLX 4bit is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

GLM 5.3 Flash Abliterated MLX 4bit by vaultai — VRAM 185.6GB

Name: GLM 5.3 Flash Abliterated MLX 4bit
Author: vaultai

GLM 5.3 Flash Abliterated MLX 4bit is an open-source language model by vaultai. Features: 321.3b LLM, VRAM: 185.6GB, License: mit, Quantized, LLM Explorer Score: 0.3.

4-bit 4bit Abliterated Apple-silicon Base model:blackfrost-ai/glm-5... Base model:quantized:blackfros... Conversational Derisked Dflash2 Glm-5.3-flash Glm5 next Image-text-to-text Mlx Mtp Omlx Quantized Region:us Safetensors Sharded Speculative-decoding Tensorflow Uncensored

Model Card on HF 🤗: https://huggingface.co/vaultai/GLM-5.3-Flash-Abliterated-MLX-4bit

GLM 5.3 Flash Abliterated MLX 4bit Parameters and Internals

LLM Name	GLM 5.3 Flash Abliterated MLX 4bit
Repository 🤗	https://huggingface.co/vaultai/GLM-5.3-Flash-Abliterated-MLX-4bit
Base Model(s)	zai-org/GLM-5.3-Flash Blackfrost-Research/GLM-5.3-Flash-DERISKED-BF16 zai-org/GLM-5.3-Flash Blackfrost-Research/GLM-5.3-Flash-DERISKED-BF16
Model Size	321.3b
Required VRAM	185.6 GB
Updated	2026-10-14
Maintainer	vaultai
Model Type	glm5_next
Model Files	5.0 GB: 1-of-35 5.6 GB: 2-of-35 5.5 GB: 3-of-35 5.4 GB: 4-of-35 5.4 GB: 5-of-35 5.5 GB: 6-of-35 5.4 GB: 7-of-35 5.4 GB: 8-of-35 5.5 GB: 9-of-35 5.4 GB: 10-of-35 5.4 GB: 11-of-35 5.5 GB: 12-of-35 5.4 GB: 13-of-35 5.4 GB: 14-of-35 5.5 GB: 15-of-35 5.4 GB: 16-of-35 5.4 GB: 17-of-35 5.5 GB: 18-of-35 5.4 GB: 19-of-35 5.4 GB: 20-of-35 5.5 GB: 21-of-35 5.4 GB: 22-of-35 5.4 GB: 23-of-35 5.5 GB: 24-of-35 5.4 GB: 25-of-35 5.4 GB: 26-of-35 5.5 GB: 27-of-35 5.4 GB: 28-of-35 5.4 GB: 29-of-35 5.5 GB: 30-of-35 5.4 GB: 31-of-35 5.4 GB: 32-of-35 5.5 GB: 33-of-35 5.4 GB: 34-of-35 1.1 GB: 35-of-35
Quantization Type	4bit
Model Architecture	Glm5NextForConditionalGeneration
License	mit
Model Max Length	1048576
Transformers Version	5.16.0
Tokenizer Class	TokenizersBackend
Padding Token	<\|endoftext\|>

Rank the GLM 5.3 Flash Abliterated MLX 4bit 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 5.3 Flash Abliterated MLX 4bit

Best Alternatives	Context / RAM	Downloads	Likes
RED SNOW 5.3 FLASH MLX 4bit	0K / 186 GB	69	3
...5.3 Flash Abliterated MLX 4bit	0K / 185.6 GB	911	2
GLM 5.3 Flash	0K / 237.5 GB	34	1289
GLM 5.3 Flash AWQ W4A16	0K / 176 GB	4247	9
GLM 5.3 Flash JANG MTP	0K / 102.8 GB	277	1
GLM 5.3 Flash BF16	0K / 237.1 GB	24	38
Apex Flash 1 Abliterated	0K / 460.9 GB	539	40
Apex Flash 1	0K / 460.9 GB	367	37
RED SNOW 5.3 FLASH BF16	0K / 215.5 GB	557	10
GLM 5.3 Flash FP8	0K / 237.5 GB	5048	22

Note: green Score (e.g. "73.2") means that the model is better than vaultai/GLM-5.3-Flash-Abliterated-MLX-4bit.