What are the hardware requirements for GLM 5.3 MXFP4?

GLM 5.3 MXFP4 requires approximately 132.2 GB of VRAM and supports a context window of 1024K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed GLM 5.3 MXFP4 and how large is it?

GLM 5.3 MXFP4 is developed by RedHatAI, a model with 753.3b 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 MXFP4?

GLM 5.3 MXFP4 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

GLM 5.3 MXFP4 by RedHatAI — VRAM 132.2GB, 1024K context

Name: GLM 5.3 MXFP4
Author: RedHatAI

GLM 5.3 MXFP4 is an open-source language model by RedHatAI. Features: 753.3b LLM, VRAM: 132.2GB, Context: 1024K, MoE.

8-bit Base model:quantized:zai-org/g... Base model:zai-org/glm-5.3 Compressed-tensors Conversational En Endpoints compatible Fp4 Glm moe dsa Llm-compressor Moe Mxfp4 Region:us Safetensors Sharded Tensorflow Vllm Zh

Model Card on HF 🤗: https://huggingface.co/RedHatAI/GLM-5.3-MXFP4

GLM 5.3 MXFP4 Parameters and Internals

LLM Name	GLM 5.3 MXFP4
Repository 🤗	https://huggingface.co/RedHatAI/GLM-5.3-MXFP4
Base Model(s)	zai-org/GLM-5.3 zai-org/GLM-5.3
Model Size	753.3b
Required VRAM	132.2 GB
Updated	2026-09-13
Maintainer	RedHatAI
Model Type	glm_moe_dsa
Model Files	4.6 GB: 1-of-141 2.9 GB: 2-of-141 2.9 GB: 3-of-141 2.9 GB: 4-of-141 2.9 GB: 5-of-141 2.9 GB: 6-of-141 2.9 GB: 7-of-141 2.9 GB: 8-of-141 2.9 GB: 9-of-141 2.9 GB: 10-of-141 2.9 GB: 11-of-141 2.9 GB: 12-of-141 2.9 GB: 13-of-141 2.9 GB: 14-of-141 2.9 GB: 15-of-141 2.9 GB: 16-of-141 2.9 GB: 17-of-141 2.9 GB: 18-of-141 2.9 GB: 19-of-141 2.9 GB: 20-of-141 2.9 GB: 21-of-141 2.9 GB: 22-of-141 2.9 GB: 23-of-141 2.9 GB: 24-of-141 2.9 GB: 25-of-141 2.9 GB: 26-of-141 2.9 GB: 27-of-141 2.9 GB: 28-of-141 2.9 GB: 29-of-141 2.9 GB: 30-of-141 2.9 GB: 31-of-141 2.9 GB: 32-of-141 2.9 GB: 33-of-141 2.9 GB: 34-of-141 2.9 GB: 35-of-141 2.9 GB: 36-of-141 2.9 GB: 37-of-141 2.9 GB: 38-of-141 2.9 GB: 39-of-141 2.9 GB: 40-of-141 2.9 GB: 41-of-141 2.9 GB: 42-of-141 2.9 GB: 43-of-141 2.9 GB: 44-of-141 2.9 GB: 45-of-141
Supported Languages	en zh
Model Architecture	GlmMoeDsaForCausalLM
Context Length	1048576
Model Max Length	1048576
Transformers Version	5.15.0
Vocabulary Size	154880

Rank the GLM 5.3 MXFP4 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 MXFP4

Best Alternatives	Context / RAM	Downloads	Likes
GLM 5.3	1024K / 232.2 GB	8804	1255
GLM 5.2	1024K / 232 GB	2391730	4875
GLM 5.3 CYBERSECURITY FP8	1024K / 112.6 GB	227	29
GLM 5.3 BF16	1024K / 237.4 GB	482	24
GLM 5.3	1024K / 237.4 GB	379	6
GLM 5.3	1024K / 232.2 GB	200	1
GLM 5.3	1024K / 232.2 GB	81	1
GLM 5.3 W4A16	1024K / 73.9 GB	268	1
GLM 5.2	1024K / 237.4 GB	1671	26
GLM 5.2 MXFP4xMXFP8	1024K / 68.1 GB	296	2

Note: green Score (e.g. "73.2") means that the model is better than RedHatAI/GLM-5.3-MXFP4.